Dijk, Pim van — Physiology, Psychoacoustics and Cognition in Normal and Impaired Hearing

Yield

1762 of 6073 paragraphs kept (29.0%). Target for mining: 30%–50% — ⚠️ BELOW target.

Pre-Assessment

Pim van Dijk is a professor at the University of Groningen’s Department of Otorhinolaryngology and Head & Neck Surgery, with a long research career in cochlear mechanics, otoacoustic emissions, and psychoacoustics of normal and impaired hearing. This is an edited scholarly volume (published by Springer, in the Advances in Experimental Medicine and Biology series) that collects peer-reviewed papers from a hearing research conference — so it is authoritative and current for the field, though it is a multi-author proceedings-style collection rather than a single-author textbook. Given your stated “mining” stance and goal of deeply understanding normal unimpaired hearing, the yield is predominantly facts and experimental data (physiological measurements, psychoacoustic findings) with some conceptual frameworks woven through the chapters; expect dense, technical content rather than inspiration or broad synthesis. It will be uneven in coverage precisely because it’s a conference proceedings — but for targeted deep-diving into specific topics (cochlear mechanics, efferent pathways, temporal coding, cognition-hearing interaction in normal listeners) it should be highly substantive.

Reading Goal

Prioritizing: Causal Relationships (Understanding mechanisms linking physiology to perception is central), Factual Claims (Core need for accurate empirical findings on hearing physiology), Definitional Content (Technical terms (psychoacoustic, physiological) need clear definitions), Connection Points (Links across physiology, psychoacoustics, and cognition are useful) De-prioritizing: Craft and Method (Methodology secondary to understanding the phenomena themselves), Inspiration and Speculation (Focus is factual understanding, not creative extrapolation)

Thesis

Binaural loudness may exhibit constancy across conditions · ch. 0

Argument

Annotation Roundup

Question

Binaural TFS sensitivity declines with age even when audiograms are normal — What structural/anatomical change underlies the age-related TFS decline if not outer-hair-cell threshold loss? Worth chasing.
Cochlear hearing loss spares amplitude modulation detection but distorts perceived fluctuation depth — Why would loudness recruitment make a fixed modulation depth fluctuate MORE rather than simply sound louder overall? Mechanism worth chasing.
Cochlear hearing loss independently degrades binaural processing of temporal fine structure — How does TFS1 testing distinguish TFS from envelope cues operationally? Would be worth clarifying for the reader’s understanding of the method.
Reported BMLD reductions tied to hearing loss may actually stem from aging — A clean experimental design separating age from hearing loss on BMLDs remains an open problem worth following in later literature.
TFS1 thresholds correlate with age independent of audiometric hearing loss — The correlation with normal-range absolute thresholds confounds age and subclinical hearing loss — worth chasing how studies disentangle the two.
Frequency discrimination worsens with age even when audiometric thresholds stay normal — If processing efficiency explains the DLF deficit, what specific mechanism — attention, neural synchrony, cognitive slowing — drives it, and does it generalize across all auditory tasks?
Aging reduces envelope processing efficiency but not temporal resolution. — The Schoof-and-Rosen null result with noise-band carriers suggests the carrier type itself masks age effects — worth comparing sinusoidal vs. noise carriers in future work.
Aging reduces envelope processing efficiency but not temporal resolution. — Why would increasing age reduce efficiency rather than temporal resolution for ENV, when the opposite effect pattern (AGE) appears for TFS? Worth probing the underlying neural mechanism distinctions.
Monaural temporal fine structure processing declines with age even without hearing loss. — What neural mechanism mediates this age-related TFS decline if not cochlear threshold loss? Retrocochlear timing jitter vs. synaptic loss?
BMLD as a threshold difference naturally cancels age-related processing efficiency losses. — Worth checking later whether this cancellation assumption has caveats — differential aging of the two measures being subtracted is a possible confound.
Cochlear hearing loss raises interaural time discrimination thresholds independent of age — A rare study age-matched (25 vs 27) to isolate hearing loss from age for binaural TFS — why are so few others designed this way?
Older adults lose measurable suppression entirely when maskers are long — Why would masker duration interact so sharply with age — is the loss of suppression with long maskers best explained by cochlear compression loss or central adaptation?
Higher auditory thresholds predict smaller low-side suppression only at high signal levels — The low-side suppression correlates with threshold at 2 kHz but high-side suppression does not — why the asymmetry between low-side and high-side in threshold dependence?
Cochlear gain declines with age before measurable threshold elevation appears — If gain and suppression decline before threshold elevation, what behavioral probe could detect this early cochlear aging in a clinical setting?
Long combined maskers trigger MOCR gain reduction that underestimates true suppression — The MOCR-suppression interaction mechanism posited from animal data deserves closer reading of Winslow & Sachs and Kawase et al.
Aging significantly reduces cochlear gain estimates at multiple signal levels — Whether decreased endocochlear potential’s differential apical-basal effects could serve as a biomarker for presbycusis before threshold shifts appear is worth chasing further.
Aging impairs speech-in-noise ability beyond what hearing thresholds predict — Is decreased endocochlear potential now the leading accepted mechanism for age-related cochlear nonlinearity loss, or still one of several competing explanations?
Older adults show significantly lower cochlear gain estimates than younger adults — The absence of a correlation between 2 kHz threshold and gain is striking — worth chasing whether age-related gain reduction reflects outer hair cell dysfunction not yet visible in audiograms.
Cochlear suppression declines with age in both high-side and low-side components — Whether the total absence of suppression in older adults reflects true cochlear loss or measurement floor effects of the forward-masking paradigm is worth investigating.
Peripheral coding efficiency drives lateralization except for low-frequency ITD — Why would low-frequency ITD lateralization escape the influence of peripheral temporal coding efficiency when high-frequency ITD shows it clearly? Worth chasing the mechanistic explanation in the body of the paper.
ITD sensitivity is not explained by basic temporal-structure processing efficiency. — What other factors then determine ITD sensitivity? The paper suggests something beyond monaural temporal coding.
Resolved spectral components, not just phase-locking, may drive temporal fine structure tasks. — This is a contested point in the literature — whether temporal fine structure tasks are actually ‘pure’ or contaminated by spectral cues.
Age-related sensory decline offers a natural experiment for correlational analysis. — The methodological trick of widening inter-subject variance with age is worth remembering as an experimental strategy.
Temporal-structure and interaural-timing sensitivities rely on partially separate mechanisms — If ITD and temporal-structure processing share mechanisms, why don’t they correlate? What factor dominates ITD individual differences?
Low-frequency pitch judgments reflect temporal fine structure via neural phase locking — Does an unchanged spectral envelope with shifted components actually isolate temporal fine structure from envelope cues, and how valid is that assumption?
High-frequency ITD sensitivity is studied via envelope cues using transposed stimuli — Why is envelope-based phase locking at high carrier frequencies relevant to hearing-impaired encoding of ITDs?
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — Why does the WMC-SiN correlation vanish for listeners under 40? Could auditory processing capacity, not WM, be the binding constraint in young adults?
Working memory capacity explains under two percent of speech-in-noise variance in young listeners. — This is a strikingly weak effect for a widely-studied relationship — worth chasing why so many speech-in-noise and working memory studies exist if the composite effect is this small.
Most working memory and speech-in-noise studies are underpowered to detect real effects. — A caution worth remembering when reading positive claims in cognitive hearing science — sample sizes are systematically too small.
Reading-Span may be the wrong cognitive measure for predicting speech-in-noise skill — Which WM sub-process — inhibition, updating, shifting — actually predicts unambiguous young-adult SiN? Unanswered in the literature.
Young listeners’ working memory stays irrelevant to speech-in-noise even at very difficult signal levels — Under what acoustic distortion level does WM load actually emerge for young NH listeners? The ceiling on bottom-up processing may need to be much higher than typical SiN tasks impose.
Complex conversational understanding engages working memory where isolated word recognition does not — The task-dependence of WMC involvement — dynamical conversation vs. isolated SiN — suggests a gradient of cognitive engagement worth mapping against background noise conditions.
Working-memory capacity predicts speech understanding only in linguistically complex listening tasks — Which working-memory sub-processes actually predict speech-in-noise performance — inhibition, shifting, or updating? The passage flags this as open.
Higher working-memory capacity predicts better speech-in-noise identification in hearing-impaired listeners — This null result complicates the Ease of Language Understanding model — under what conditions does working memory actually engage? Worth chasing the boundary conditions.
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — Is the null correlation in young listeners because their bottom-up processing is sufficient and never taxes working memory, or because the task lacks difficulty at their performance level?
Age-related temporal and level sensitivity decline precedes measurable hearing loss — If PTA declines with age even among audiometrically normal hearers, at what point does subclinical sensitivity loss cross from confounding variable to the actual driver of speech-in-noise difficulty?
Young listeners’ working memory stays irrelevant to speech-in-noise even at very difficult signal levels — This carefully controls for the task-difficulty confound and still finds no WMC-SIN link in the young — an important and somewhat surprising result worth verifying against other cohorts and testing paradigms.
Age differences in working memory’s relevance may stem from differing performance levels — This is offered as a possible explanation but then partially addressed by the lowest-SNR analysis showing no young correlation even at matched difficulty — the tension between the two results deserves resolution.
The working memory link to speech-in-noise emerges only past age forty. — Why does the WMC-SiN correlation jump specifically after age 40? Is it declining perceptual fidelity or something else that recruits working memory?
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — Worth chasing: does the WMC-SiN correlation depend on task difficulty/ceiling effects at high SNRs, or is it a genuine shift in processing strategy with age?
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — Why would WMC matter for older but not younger normal-hearing listeners? Is this an audibility artifact, a processing-speed decline, or evidence that young listeners compensate via other mechanisms?
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — If neither difficulty nor age-per-se explains the emergence of the WMC-SIN link in middle age, what intervening variable does - processing speed, listening effort, neural degradation thresholds?
Working memory only matters when the listening task is acoustically hard — This offers a mechanistic explanation for literature inconsistencies — that easy speech tasks simply don’t load working memory. Could be confirmed or refuted by meta-analysis.
Pure-tone thresholds capture hearing aspects that speech reception thresholds miss — What aspect of hearing beyond the speech reception threshold does the pure-tone average still capture after sensitivity adjustment? Worth chasing the mechanism literature.
Working-memory and temporal-processing contributions to intelligibility depend on listening conditions. — Why would temporal processing help only at high SNR where the stimulus is clearly audible and no semantic context exists? Worth chasing the mechanism.
Processing-heavy working memory tasks correlate more strongly with noisy speech perception — Worth chasing: why do some storage-heavy WM tasks still correlate with SiN perception, contradicting the general trend?
Sensitivity to interaural correlation change estimates loss of neural synchrony — How directly does interaural decorrelation sensitivity map onto actual neural dyssynchrony? Worth checking the Wang et al. model.
Sensitivity to interaural correlation change estimates loss of neural synchrony — Worth chasing: how exactly does interaural correlation change detection map onto neural synchrony loss in the auditory pathway? Wang et al. 2011 is a lead.
Cognitive contributions to speech perception vary by listening situation — Which specific listening conditions activate which cognitive abilities remains unstated here; worth chasing the detail.
Pure-tone average hearing sensitivity still predicts intelligibility even at equalized levels — What suprathreshold aspect of hearing does PTA capture that SRT-adjusted presentation fails to equate? Worth chasing in follow-up literature.
Working-memory storage aids speech intelligibility while vocab and reading do not. — Puzzling and worth chasing later: why would the processing-heavy reading span task (RST) only trend toward helping at low SNR while pure storage (LNS) was robustly beneficial across all situations — the intuitive direction would be reversed if central processing compensates for degraded input.
Temporal processing aids intelligibility only for clear isolated single words — Counterintuitive result — why would temporal cues matter most at high SNR when they are typically thought relevant for degraded signals? Worth chasing the follow-up work.
Acted speech masks true effects of hearing loss on affect perception — Worth chasing: what experimental designs would avoid the acted-speech confound? Naturalistic/ecological prosody paradigms.
Hearing aid use restores arousal perception but shifts cue weighting — Why would hearing-aid users become more sensitive to intensity differences than normal-hearing listeners? Is this a compensation/attentional effect or an artifact of hearing-aid compression curves?
Acted speech masks true effects of hearing loss on affect perception — Worth chasing: how much do acted-speech studies across emotion science systematically overestimate how well perceivers parse natural affect?
Emotional prosody cues are shared across closely related languages — Is prosodic affect encoding universal enough to survive translation into a distant language family?
Hearing loss can impair affect perception in ways hearing aids may not fully restore — Does hearing aid amplification actually restore affective prosody perception, or does signal processing trade it away?
Valence perception is hearing-loss independent while arousal perception depends on amplification. — What neural or perceptual mechanism makes valence robust to hearing loss while arousal is degraded? Dissociation between the two affect dimensions is a strong finding worth chasing.
Pitch-based electrode matching in cochlear implants is confounded by acclimatization and brain plasticity — How exactly does acclimatization skew pitch matching while leaving IPTD and BIC measures unaffected? Mechanisms unstated here.
BIC amplitude does not correlate with interaural pitch comparison outcomes. — If physiological BIC and perceptual pitch don’t correlate, which measure actually reflects binaural fusion quality? Worth chasing the follow-up.
BIC-based electrode pairing is validated against psychoacoustic pitch and timing measures — Why do pitch-based pairing and brainstem BIC pairing diverge - what does pitch plasticity imply about the two measures indexing different neural stages?
Acclimatization to implant frequency maps can inflate pitch-matching accuracy — Worth chasing: if acclimatization corrupts pitch matching but not BIC/IPD measures, pitch matching may be overtrusted clinically.
Binaural brainstem responses are disrupted by large electrode place mismatches — Worth chasing: why does BIC fail to correlate with pitch comparison yet track electrode offset in some paradigms? What does that say about what BIC actually measures?
Best-matched interaural electrode pairs yield maximal BIC amplitude — Does the BIC amplitude maximum hold in humans despite the null correlation result that follows, or is this cat-only?
Bone-conducted sound substantially contributes to hearing with an occluded ear. — How large is the bone-conduction contribution to occluded-ear loudness compared to residual air-conducted sound? Worth chasing a follow-up measurement study.
Bone-conducted sound substantially contributes to hearing with an occluded ear. — How large is the bone-conduction contribution to the occluded ear relative to air-conduction leakage around the earplug? Worth chasing in the physiology literature.
Visual cues are required for loudness constancy to appear — Does the visual need reflect cognitive binding of auditory source identity rather than any acoustical property? Would a static face suffice or must it be speaking?
External real-world sounds show loudness constancy across one and two ears — Why would headphone presentation reproduce summation while free-field listening suppresses it? The mechanism behind this modality difference is worth chasing.
Occluding one ear in a sound field dramatically reduces binaural loudness summation — The cognitive-constancy hypothesis — that merely knowing one ear is occluded preserves binaural loudness — is a striking claim about top-down processing worth chasing in the broader literature.
Occlusion-based monaural hearing dramatically reduces binaural loudness summation. — Is the cognitive element real, or is it confounded by residual low-frequency energy leaking past the occluded ear? The authors themselves flag both as explanations.
Loudness constancy occurs regardless of whether sound is speech or noise — Reverberation cuing an external source may be the key manipulation, not ecological validity of the stimulus itself.
Bone conduction likely contributes substantially to occluded-ear loudness — The interplay of air conduction attenuation with bone conduction in occlusion under-determines these conclusions.
Loudness constancy may require ecologically valid sound sources — Worth chasing whether the effect requires speech specifically or externalisation generally.
Binaural loudness constancy operates independently of stimulus type — The claim that awareness of ear occlusion may mediate loudness summation suggests a possible top-down cognitive confirmation-bias mechanism worth comparing with how occlusion is usually acoustically modeled.
Headphone-deactivated monaural presentation collapses the externalised sound image — Whether the collapse of externalisation, rather than true monaural listening, enhanced the measured summation effect is unresolved and could confound ecological-validity conclusions.
Simulated finger occlusion reduces but does not remove binaural loudness summation. — Why does real finger occlusion (which leaves low-freq bone conduction intact) shrink the summation effect less than a full silencing? Worth chasing the mechanism.
Spatial release from masking stays constant across all SNR criteria tested — This stable SRM despite deletion of low-SNR components is the study’s central surprising finding — worth holding as a key datapoint on spatial hearing mechanisms.
Spatial release from masking persists even when target speech components are selectively discarded — How does spatial release from masking arise if not through binaural unmasking of sub-threshold components?
Spatial release from masking stays constant across all SNR criteria tested — Why does spatial release of masking not diminish even at very low SNR criteria? This seems significant and unexplained here.
Binaural unmasking cannot explain spatial release from masking in multispeaker situations — Could the result depend on the restricted vocabulary task and learned words, masking an effect that would appear with open-set materials?
BMLD detection advantages may operate at spectro-temporal unit level, not just per frequency band. — Worth checking whether later hearing models (e.g. EC/Glimm models) have validated per-time-slice BMLD assumptions against speech interferers rather than stationary noise.
Spatialized audio stimuli must preserve separate spectra per channel to estimate binaural local SNR — Worth understanding whether the squared-mean-of-channels approach to collapsing stereo spectra is the standard choice, or whether more sophisticated binaural SNR metrics exist.
Spatial release from masking stays constant across all SNR criteria tested — Why would the spatial benefit persist even where the discarded low-SNR components carry little monaural information — does spatial release depend on a different neural pathway or on glimpsing integration?
Hearing-impaired listeners’ poor spatial hearing may not reflect actual spatial-processing deficits. — If audibility confounds spatial-release-from-masking measures, how many published HI spatial deficits are actually audibility artifacts?
Quiet-only performance measures miss masking effects that reduce speech redundancy. — Worth chasing: do glimpses present in only one ear vs both ears confer any advantage, and how do hearing-loss thresholds interact with the binaural glimpsing count?
Spatial release from masking deficits often reduce to an audibility problem — Worth chasing: why would reduced SRM coexist with spared binaural discrimination and localization? The chapter’s core puzzle.
Mono mixed spatial-task evidence contradicts the assumption that hearing loss disrupts spatial processing globally — What exactly defines the ‘spatial processing’ claim being challenged, and which binaural sensitivity measures (e.g. interaural level vs time difference thresholds) show HI listeners matching NH?
Audibility equalization reduces but cannot eliminate hearing-loss effects on speech-in-speech — Which specific non-audibility factors — cochlear nonlinearity, efferent function, binaural processing, cognitive capacity — drive the residual hearing-loss effect after equalization? This demands follow-up.
Reduced target audibility, not spatial processing loss, explains much of the hearing-loss speech deficit — What exactly are the non-audibility factors — is auditory filter width or cognitive aging the dominant residual driver when audibility is equated?
Simulated hearing loss isolates audibility from other hearing-loss effects — Loudness discomfort caps hearing-aid gain below what would restore normal audibility - does this itself explain residual SRM deficits in aided HI listeners?
Energetic masking of targets may not fully explain hearing-impaired spatial speech performance — The gap between glimpsed-stimulus and natural-binaural performance would reveal what else beyond audibility explains spatial unmasking.
Reduced audibility, not spatial processing loss, may explain apparent spatial deficits in HI listeners — The chapter’s hypothesis: symmetric-masker SRM deficits may trace to audibility rather than binaural/spatial coding loss.
Spatial release from masking deficits often reduce to an audibility problem — What mechanism explains the remaining SRM deficit in HI listeners unaccounted for by audibility — degraded binaural cues, reduced temporal fine structure coding, or cognitive load?
Most of the SRM deficit in hearing-impaired listeners stems from reduced audibility — Why did NAL-RP fall short at 55 dB SPL masker — is the insufficiency mainly HF gain limits or loudness discomfort capping real-world gain? Worth chasing the loudness cap interaction.
Increasing spectral contrast enhancement worsens vowel identification in CI users — Counter to the hypothesis that stronger spectral enhancement would help, it hurt. Why — over-emphasis distorting formant structure, or limited electrode resolution?
Formant extraction from cochlear implant electrodograms stays robust across users at low noise — What exactly is ‘jnd noise’ in the virtual CI model, and why does it only explain differences at higher noise factors?
Spectral valley over-attenuation may explain why contrast enhancement harms CI users — Two competing explanations are offered (valley over-attenuation vs. too-high SCE factors) but left unresolved; a natural question to chase.
Over-attenuating spectral valleys may remove speech information CI users need — Worth asking: does excessive valley attenuation hurt specifically because formant trough locations carry speech cues, or via a more general loudness distortion? The mechanism proposed is left unverified.
Formant extraction from cochlear implant electrodograms stays robust across users at low noise — The claim that jnd noise alone explains virtual-user differences, at zero internal noise, is striking — would want to see how the MPI model generates jnd noise and whether physiological noise is really user-independent in real CI listeners.
Increasing spectral contrast enhancement worsens vowel identification in CI users — A head-scratcher worth chasing: why would a vowel-identification model fail so completely in the opposite direction in real CI users? The gap between model and implant suggests the model omits something fundamental about valley information.
Vowel identification models inject noise scaled to each user’s spectral modulation threshold — How well does per-listener jnd-scaled noise in an MPI model actually predict real CI vowel-confusion patterns?—worth chasing the validation method later in the book.
Missing efferent benefits may explain cochlear-implant users’ speech-in-noise deficits — Whether replicating the MOCR computationally genuinely restores cocktail-party abilities in real CI users remains unconfirmed behaviorally—worth chasing the follow-up trials.
The MOCR’s unmasking role remains unproven in direct human experiments — A key methodological insight: why has no one been able to selectively deactivate MOCR efferents in humans while preserving afferent nerve function? This is the central experimental gap in the field.
The olivocochlear reflex may unmask speech from noise via dynamic-range restoration — The unmasking role remains only indirectly evidenced — worth tracking whether later work confirms it directly.
MOC reflex’s speech-in-noise unmasking role remains unproven in humans — What would a direct human test of MOCR speech unmasking look like, and what confounds limit current indirect evidence?
Simulation parameters were chosen for visibility of effects, not physiological fidelity. — If these parameters deliberately do not match natural MOCR characteristics, how much does this simulation actually reveal about the real contralateral reflex?
The real MOCR is stronger apically and inhibits a half-octave below the elicitor — The half-octave downward frequency shift between inhibited region and elicitor is a precise, counterintuitive feature of the natural MOCR worth confirming and understanding.
Medial olivocochlear reflex inhibition varies by cochlear location — Why is MOCR inhibition stronger apically and half-octave shifted? Mechanics worth chasing for psychoacoustic understanding.
MOCR speech-in-noise unmasking remains unproven because selective deactivation is impossible in humans — The claim that cochlear implants enable an approximate selective-deactivation experiment is worth pursuing — how does a CI bypass or preserve the efferent pathway?
The contralateral efferent reflex inhibits cochlear compression at matched frequencies — Does the natural MOCR truly respond to cochlear output energy rather than acoustic input level? The model assumption rests on plausibility, not direct evidence — worth chasing the underlying physiology.
The medial-olivocochlear reflex activates and deactivates with exponential time courses. — The two time constants (4 and 29 ms here) are cited as inspired by natural MOCR activation and deactivation — worth verifying those natural values against the original Backus and Guinan study.
The contralateral reflex enhancement evolves dynamically over tens of milliseconds after onset. — How does this ~onset-to-plateau build-up time relate to the actual temporal dynamics of the human MOC reflex measured physiologically?
Deactivating indiscriminable implant electrodes fails to improve and even worsens perception — Why would removing low-information electrodes degrade spectro-temporal ripple perception rather than merely not help? Mechanism worth chasing — possibly the n-of-m strategy competes for remaining channels.
Pitch-ranking-based electrode deactivation provides no measurable benefit for ACE implant users. — Would larger electrode deactivation counts, or selection based on other criteria, show benefits that this underpowered study missed?
Only half of cochlear implant users benefit from electrode deactivation, and many worsen. — Worth chasing: the device difference (AB/MEDEL at 62% vs Cochlear at 44%) and the n-of-m explanation deserves verification.
Deactivating electrodes improves consonant but worsens vowel perception in ACE users — Why would deactivation benefit consonants while degrading vowels? The vowel-degradation mechanism tied to filtering adjustments and acclimatization warrants chasing.
Deactivating non-distinct cochlear implant electrodes fails to improve speech perception — Why would spectro-temporal ripple perception deteriorate specifically? The mechanism of harm while speech stays flat is not explained here and worth chasing.
Deactivating pitch-indiscriminable cochlear implant electrodes yields no speech benefit — Why did deactivation degrade spectral resolution rather than improve it? The default filter reallocation mechanism deserves closer scrutiny.
Only half of cochlear implant users benefit from electrode deactivation, and many worsen. — Learning effects unaccounted for means gains could partly be adaptation, not the remapping itself.
Spectral ripple tasks can confound loudness and edge-frequency cues — What are edge-frequency cues and how do they contaminate spectral resolution measures? Worth pursuing a definition.
Cochlear electrode pitch can be probed by acoustic tones or direct biphasic stimulation. — What is the STAR system, and why might acoustic and direct-stimulation pitch measures diverge?
Broader cochlear filtering reduces low-frequency noise fluctuations, explaining lost masking release — Worth chasing: does broader filtering physically smooth low-frequency envelope fluctuations, and at what channel widths does the effect kick in?
Hearing-impaired listeners benefit less from masker fluctuation when spectral resolution is degraded — Does this fluctuation benefit deficit stem from reduced frequency selectivity, audibility, or impaired temporal processing in HI listeners?
Polarity effect magnitude is similar across species even though its direction is reversed. — Opposite sign yet matching magnitude of polarity effect across species — does equal magnitude hint at a shared underlying mechanism differently gated?
Neural degeneration does not drive the anodic sensitivity seen in humans — Falsifies the degeneration hypothesis the study set out to test — what else differs between guinea pig and human cochlea besides degeneration state?
Neural degeneration alone does not explain polarity sensitivity differences in cochlear implants — Why does the polarity effect actually decrease slightly right after deafening? The mechanism behind this transient change is unexplained and worth investigating.
Cathodic pulses are more efficient in animals but anodic pulses in humans — Why would cathodic stimulation beat anodic in animal cochleas but lose to it in human cochleas at suprathreshold levels? Worth chasing the mechanism.
Anodic current outperforms cathodic current in human cochlear implant users at suprathreshold levels — Why would polarity effects differ between threshold and suprathreshold levels? Worth chasing the psychophysical evidence behind this asymmetry.
Pulse shapes vary phase duration while holding total delivered charge balanced — Worth verifying the charge-balance rationale — why total charge must be balanced and what happens if it isn’t (tissue damage? electrode corrosion?).
Cochlear implant input-output functions can be non-monotonic at high levels — Why would increasing stimulation current reduce response amplitude? This suggests a mechanism worth investigating—perhaps differential fiber recruitment ordering or refractoriness.
Anodic stimulation is more effective in human implant users but less effective in animals — The mechanism behind the human-animal polarity discrepancy is unresolved; periprocess degeneration was proposed as an explanation but this study’s chronic design failed to confirm it.
High-level cathodic stimulation can block action potential propagation centrally on auditory nerve fibers — Whether this conduction block was actually observed in the guinea pig IC data is a question the paper tests with non-monotonic growth functions.
Cochlear implant growth functions can behave non-monotonically near the top of the dynamic range — What neural mechanism makes a cathodic pulse more effective at low levels but less effective at high levels? Could involve two different fiber populations (central vs peripheral processes) with different thresholds.
Neural degeneration shifts eCAP firing-component scaling and latency in measurable ways. — The inferential step assuming the cochlear base degenerates more than the apex in humans is worth scrutinizing — how strong is the evidence for that assumption?
A single unit response profile cannot represent a degenerating nerve population — Worth chasing: what exactly are the UR differences across cochlear regions in humans, and how much of CDLD variation do they explain?
Electrically evoked auditory nerve responses fit Gaussian rather than gamma latency distributions — What produces a Gaussian (rather than skewed gamma) jitter distribution across the entire fibre population? Convolution with between-fibre latency scatter is offered, but the mechanism deserves chasing.
Deconvolution can recover firing-discharge timing from macro compound responses — Conviction question: at what signal-to-noise floor does deconvolution of a 1.7 ms eCAP window stop being geometrically recoverable?
Residual hearing does not predict electrically evoked compound action potential shape — If the presumed neural-health proxy (residual hearing) shows no group effect at p = 0.054, borderline — is the assumption itself weak, or is the measure insensitive? Worth chasing the actual clinical implication.
Human cochlear-implant eCAP studies use loudness-based limits unlike animal models — What is the ‘loudest acceptable presentation level’ criterion operationally in clinical software, and how does it vary across subjects?
Hearing loss can compress loudness range while keeping sharp categories — What physiological mechanism allows category boundaries to stay sharp despite the compressed range? Suggests peripheral compression need not degrade categorical resolution.
Mid-range loudness categories show the most listener uncertainty — Why do mid-range categories (5-25 CU) specifically drive the most uncertainty? Might connect to compression of the perceptual growth function or to how listeners anchor category labels.
Maximum-likelihood catalog fitting yields accurate loudness estimates from few observations — How reliable is ML estimation from the catalog when a listener’s true MCPF deviates from all catalog patterns? No validation metric is given here.
Maximum-likelihood estimation can predict categorical loudness functions from only 15-30 trials — How well do ML estimates from catalogs perform against direct measurement at various observation counts? The claim that it works with ‘relatively low’ observations invites a validity check.
Adaptive stimulus selection can maximize information gain per loudness trial — Worth pursuing: what adaptive information-maximizing stimulus selection schemes exist for loudness and are they clinically practical?
Loudness judgments at a fixed level vary probabilistically across categories — Worth chasing: what physiological variability — neural firing stochasticity, adaptation, criterion shifts — actually generates the response distributions the MCPF models?
Short clinical test lengths amplify response variability into inaccurate loudness curves. — Chain worth tracing: how does trial count quantitatively scale the CLS error, and where is the accuracy floor under fixed clinical time budgets?
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Worth chasing: does the loudness transform (compressive cochlear I/O function) explain the quadratic-vs-linear distinction, and at what stage does it arise?
Testing hearing-impaired listeners could reveal how loudness distortion maps to brain activity — Would recruitment patients show the quadratic-vs-linear dissociation collapsing, pinpointing where the transform happens?
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — This is the chapter’s central open question — whether cortical BOLD reflects the percept or the stimulus. Worth tracking how the study answers it.
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Worth chasing: the quadratic vs linear distinction — what mechanism explains quadratic growth in cortical activation for SPL but linear for loudness?
Subcortical auditory structures encode sound intensity linearly while cortex compresses it. — Is the subcortical-vs-cortical encoding difference real, or an fMRI SNR artifact? The cardiac gating fix is worth researching further.
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Worth chasing: the subcortical result is close to non-significant (only slightly louder-related) and noisy — how robust is it really?
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Whether cortical BOLD tracks perceived loudness over physical dB is unresolved — this is the central open question driving the chapter.
The neural site where intensity becomes loudness remains unknown. — The two open questions — where intensity-to-loudness conversion happens and how AC subregions differ — are precisely what this study aims to answer.
Cortical BOLD responses grow nonlinearly with sound intensity, steepening at high levels — The dispute between quadratic, linear, and saturating intensity functions across studies is flagged by the authors as open — worth chasing what stimulus/dynamic-range factors explain it.
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Why would cortex code loudness linearly while sensory inputs grow quadratically with pressure? What transformation happens between periphery and cortex?
The default mode network may mediate perception of internally generated sound. — Worth chasing: is DMN alteration a shared mechanism across phantom-sound conditions, or just correlational noise given variable schizophrenia findings?
Tinnitus reduces default mode network activity by blocking mind-wandering — The causal direction (source vs consequence) remains untested — worth flagging as unresolved.
Whether tinnitus causes or results from network changes is unknown — A genuine open direction worth chasing: causal direction between tinnitus percept and network restructuring.
Scanner noise does not mask chronic tinnitus perception during imaging — Worth chasing: does ambient scanner noise actually not mask tinnitus, or is this idiosyncratic to this cohort? This bears on whether tinnitus is modulated by masking sound at all.
Scanner noise does not mask chronic tinnitus perception during imaging — Why does ambient MRI noise fail to mask tinnitus in patients, and why does stress/fatigue increase tinnitus loudness — worth chasing the underlying mechanism.
Tinnitus patients show reduced default mode network connectivity and a blunted task effect. — Could reduced DMN connectivity in tinnitus relate to the attentional capture effects of phantom sound? Worth tracing this to the physiology-versus-perception debate in the book.
Tinnitus patients show reduced default mode network connectivity and a blunted task effect. — The finding that DMN fails to strengthen from fixed to resting state only in patients — attributed to the ‘unremitting engaging effect’ of the tinnitus percept — is a causal mechanism claim that deserves chasing down into the full paper for confirmation and its theoretical framing.
Resting-state fMRI reveals connectivity differences in numerous brain regions in tinnitus. — Whether tinnitus-induced connectivity changes are causal and in which direction remains unresolved.
Tinnitus alters memory, attention, and emotion networks, with unknown causality — The causal direction question — does tinnitus reshape these networks, or do pre-existing connectivity patterns predispose to tinnitus — is open and worth following.
Tinnitus patients show reduced default mode network connectivity and a blunted task effect. — Blunted DMN task-modulation in tinnitus suggests the phantom sound may chronically engage the DMN even during active tasks, collapsing the normal rest-task distinction — worth investigating as a mechanistic hypothesis.
Single pooled ICA of all subjects avoids cross-group decomposition bias — Worth chasing: does pooled single-ICA analysis meaningfully outperform separate per-group ICA in detecting true resting-state group differences?
Binaural coincidence precision emerges through Hebbian-tuned coarse then fine delay matching — What exactly are the ‘unknown activity dependent processes’ that refine delays to ±20 μs after coarse developmental matching?
Microsecond ITD precision arises from phase-delay matching, not absolute latency — This result seems counterintuitive: how do coincidence detectors distinguish phase-equivalent latencies (differing by full cycles) when a given frequency produces identical ongoing phase? Worth chasing the computational logic.
Neural delay matching proceeds by coarse developmental matching followed by precise activity-dependent refinement — The ‘as yet unknown activity dependent processes’ controlling the ±20 μs refinement remain unspecified - worth chasing.
The periodic ringing of cochlear inputs sustains phase-based ITD coding for nonperiodic sounds — How does phase-only matching produce microsecond accuracy when the neural elements conveying the signal are noisy and slow? The question is resolved later in the chapter.
Neural ITD maps require precise phase delays, not stable absolute latency — How does the nervous system achieve microsecond-scale phase precision given noisy individual spikes? Mechanism not fully specified here.
Steady-state EEG responses can distinguish which brainstem binaural pathway is active — Could a similar pathway-dissociating EEG measure help differentially diagnose brainstem vs. cortical binaural deficits clinically?
A single EEG response can reveal which binaural pathway cochlear implant users rely on — Worth following: does objective device fitting via IPM-FR outperform current behavioral CI fitting methods, and does pathway identification predict real-world benefit?
Listeners can discriminate interaural time differences of just tens of microseconds below 1.3 kHz. — The 1.3 kHz upper limit and the ‘few tens of microseconds’ threshold — what mechanistic reason explains the 1.3 kHz cutoff specifically? Worth chasing the physiological basis.
Optimal interaural phase difference for cortical following responses depends on the IPM rate — A rate-dependent tuning pattern (peaked at mid-IPD for slow, monotonic for fast) suggests two different neural mechanisms — worth chasing how this maps onto known binaural coincidence model behavior.
Even enlarged pinna cues leave hearing-impaired localization far below normal — Why do enlarged-pinna cue transposition cues give so little benefit to hearing-impaired listeners, when the rationale for them seems strong?
Oversized pinna cues significantly improved localization accuracy only at wide reference angles. — Worth chasing: does enhanced outer-ear detail help only where front-back or large-angle ambiguity dominates, suggesting the cues matter for resolving spatial ambiguity rather than improving acuity broadly?
Enhanced pinna cues only partially close the spatial-resolution gap in hearing-impaired listeners — Why did W-cues help HI listeners but not NH listeners? The NH group discriminated equally well across all cue sets, suggesting ceiling effect or a different localization strategy.
Hearing-impaired listeners cannot perceive fine high-frequency pinna spectral details — The proposed solution—transposing pinna cues to 2–6 kHz—depends on whether reduced frequency selectivity truly makes these fine details imperceptible, which the study presumably tests.
Hearing-impaired listeners cannot perceive fine high-frequency pinna spectral details — What exactly is the threshold of frequency selectivity reduction in hearing-impaired listeners, and at what point do fine spectral cues become unusable?
Compensating hearing-impaired listeners relies on incomplete audiogram data at extreme frequencies — Does this 9 kHz cutoff for gain compensation materially affect the pinna cues under study, since pinna cues extend well into the ultrasonic range in some subjects?
One-up-two-down adaptive staircases can report lower thresholds than other estimation methods — Why does the one-up-two-down approach bias thresholds downward? Worth checking the psychometric/estimator mechanism.
Enlarging the pinna improves azimuth localization only for hearing-impaired listeners — Why did enlarged pinna cues not affect normal-hearing listeners at all, while clearly helping the hearing-impaired? The ceiling-effect interpretation is plausible but deserves scrutiny.
Enhanced pinna cues only partially close the spatial-resolution gap in hearing-impaired listeners — The 30-to-20 degree improvement being questioned as possibly imperceptible in real life is worth chasing — what is the perceptual salience threshold for angular localization benefit?
Exaggerated outer-ear cues improve sound localization only in hearing-impaired listeners. — What mechanism leaves HI listeners’ minimum audible angles (around 20-30 degrees even with best cues) far above NH levels (about 10 degrees)? Is the bottleneck spectral resolution rather than cue strength?
Auditory motion direction detection requires much larger angular sweeps than static sound localization does. — Worth chasing: does this static-vs-motion circuit dissociation generalize to humans, where ‘binaural sluggishness’ (slow time constant for ITD changes) is a known phenomenon?
Auditory motion tracking requires short time constants, but measured values vary by method — The dependence of binaural time constants on measuring procedures hints at a deeper methodological puzzle worth chasing: which measure reflects real tracking capacity?
Angular range, not velocity, dominates barn owl auditory motion discrimination sensitivity — Is the 30ms degradation evidence of the inhibitory circuit’s developmental window, or of a different temporal-integration mechanism entirely?
Owls require 24° of motion but only 3° for static localization — Eightfold gap between static localization acuity (3°) and required motion range (24°) invites further mechanistic inquiry.
Angular range, not velocity, dominates barn owl auditory motion discrimination sensitivity — Counterintuitive that duration and velocity matter so little versus spatial extent — worth understanding the mechanism.
Auditory motion processing relies on directionally tuned midbrain neurons — Are these motion-sensitive midbrain neurons genuinely specialized circuits, or do static localizing circuits suffice with temporal integration? Worth chasing.
Stimulus duration and angular velocity are interdependent in auditory motion — For a given angular range, duration and velocity are inversely coupled — how do they parse each parameter’s independent contribution when the design cannot fully decouple them?
Larger change sizes and longer sampling improve texture change detection — What specific accumulation-to-bound model does this evoke, and does reaction time data let one fit a Wiener process?
Standard d-prime must be modified for time-variable change detection tasks — Worth chasing how a trial-outcome labeling convention biases early-time d’ estimates in time-varying tasks.
Incidental listening may improve statistical change detection over attention — The subjective report that ignoring the stimulus improves detection is intriguing and worth chasing with a dual-task design.
Incidental attention may improve detection of statistical change in sound textures — Does inattentional processing genuinely outperform focused attention for statistical-change detection? A dual-task paradigm could answer this.
Some listeners detect texture changes more readily when not actively attending — Self-reported better change detection with inattention is intriguing — could align with pre-attentive statistical tracking or automatic deviance detection (MMN-like) that focused attention disrupts. Worth chasing literature on attention effects on texture/timbre change detection.
Listeners detect statistical changes in ongoing sound based on time available for evidence integration — Presents findings as summary (‘main predictors appear to be the time available for integration’) without detailed numbers; worth chasing the full study for effect sizes and time constants of statistical integration.
Change-detection hit rates rise along an invariant cumulative Erlang-shaped curve. — The Erlang shape parameter k held constant across subjects and change sizes is an empirical assumption worth verifying — what theoretical mechanism (accumulator model, integration window) would predict it?
Exponential change-time distributions require logarithmically expanding bins for equal trial counts. — Why use an exponential distribution of change times rather than uniform? Presumably to weight early changes more heavily, but the rationale is implied not stated.
Auditory change detection integrates statistical evidence accumulated over time — Reaction time is a proxy for when the decision system reaches internal certainty — how early decision onsets map onto accumulation thresholds in drift-diffusion terms is worth chasing.
Statistically driven spectral analysis requires dynamically presented stochastic tone streams — Is there converging evidence that reaction times (not just accuracy) index how the auditory system accumulates spectral statistics?
Statistical change detection requires predicting stimulus statistics rather than sequence events — Worth chasing: how does the brain carry ‘marginal probability’ estimates over continuous listening, and what neural time-constant governs the integration window they report?
Impaired temporal resolution degrades fine-structure pitch processing more than reduced frequency selectivity does — This is a surprising causal claim — that temporal-resolution deficits hurt fine-structure pitch processing more than frequency-resolution deficits. Worth chasing the mechanism: why would timing deficits degrade FS extraction more than spectral smearing?
Impaired temporal resolution degrades fine-structure pitch processing more than reduced frequency selectivity does — The reverse-causal direction here (that temporal resolution drives FS use?) is counterintuitive given FS is spectral; worth reading the underlying studies to see how temporal and spectral processing interact in pitch.
Increasing sensorineural hearing loss shifts tone perception from fine structure to envelope cues — Worth chasing: is the mechanism truly reduced frequency selectivity degrading FS processing, and does that causal chain replicate in ANSD listeners whose frequency selectivity is normal but temporal synchrony is impaired?
Fine-structure cues carry Mandarin tone perception, not envelope cues — What neural mechanism lets envelope coding survive when fine-structure processing fails in ANSD — neural desynchrony at the auditory nerve?
Increasing sensorineural hearing loss shifts tone perception from fine structure to envelope cues — Why would pure-tone pitch rely on fine structure while speech perception works on envelope alone — what determines a cue’s weight in a given task?
Modulation Detection Interference is frequency-selective in its effectiveness — What model did the authors arrive at to capture the frequency selectivity of MDI interference? The chapter presumably reports this below.
Interferers partially rather than fully suppress the modulation cues used for detection — The gradually-weighted modulation filterbank idea is underspecified here — worth chasing how weights would be derived and whether they predict MDI data.
The SII model’s assumed SNR range fails for sharply filtered speech signals — The asymmetry between target-filtering and noise-filtering slopes is an intriguing mechanistic puzzle worth exploring further.
Two-dimensional SNR-importance functions are needed to model filtered-source intelligibility — Worth chasing: what exactly is Studebaker and Sherbecoe’s two-dimensional importance function, and could it be adapted for narrower bands?
Effective SNR range for speech extends far beyond SII standard bounds. — SII standard is central to many speech models; worth checking why negative vs positive SNR contribution differs so asymmetrically.
The 1400 Hz cut-off equalizes spectral contribution based on the SII importance function — Why is 1400 Hz chosen as the SII-balanced boundary, and how does this map onto real frequency-band importance for speech intelligibility?
The cochlea may regulate gain slowly rather than compress sound instantly — Whether the cochlea processes intensity changes as an AGC with delay, or instantaneously, is the open question this whole chapter answers.
Cochlear compression delay implies automatic gain control circuitry — Does the cochlea genuinely implement an AGC-like mechanism, and what psychophysical phenomena does this delay explain?
Cochlear gain control tracks intensity changes only up to about 1 kHz — What is the precise upper frequency for cochlear gain control in humans vs gerbils, and what are the psychophysical correlates of this ~1kHz limit?
The cochlea’s compressive nonlinearity may act as a time-averaging gain control, not instantaneously. — Whether cochlear compression is an AGC rather than an instantaneous nonlinearity is an open question worth tracking — could reshape how dynamic stimuli are modeled.
Cochlear compression delay implies automatic gain control circuitry — This frames the chapter’s central unresolved question: is the cochlea an instantaneous nonlinearity or a slower AGC system? Worth chasing how the field has since answered it.
Cochlear compression delay implies automatic gain control circuitry — Worth chasing: does the AGC-style delay idea predict specific psychophysical effects that distinguish it from level-dependent group-delay models in hearing-impaired listeners?
Intense low-frequency sound alters inner ear function even though we barely hear it — Why does perceptual insensitivity mask physiological impact — what is the adaptive or mechanical reason LF sound reaches the inner ear despite being inaudible?
Low-frequency exposure temporarily activates new otoacoustic emissions that were previously absent in most ears. — What mechanism explains the oscillatory increase-then-decrease of quadratic distortion products after low-frequency offset? Is it a slow outer hair cell state change?
The SOAE Bounce arises from homeostatic correction of low-frequency-induced calcium influx — If Ca2+ flux is the mechanism, what dose-response and temperature-dependence does this predict for the Bounce’s time constants?
The Bounce is caused by OHC calcium homeostasis disruption, not stria damage — Worth verifying whether the Bounce phenomenon’s Ca2+ hypothesis is settled or still debated in later literature.
The olivo-cochlear efferent system accelerates recovery from calcium-induced sensitivity changes. — The mechanism linking constant Ca2+ release to accelerated reuptake is hypothesized, not demonstrated — worth checking for subsequent confirmation.
Even 90 seconds of low-frequency exposure measurably alters inner ear active mechanisms — Longer or repeated LF exposure effects remain an open experimental question worth chasing.
Loud low-frequency tones cause a characteristic two-phase threshold change called the bounce — What mechanism — the same OHC/IHC machinery the next paragraph describes — produces the biphasic bounce? Worth tracing.
Low-frequency sound exposure triggers long-lasting damped oscillations in spontaneous otoacoustic emissions. — What mechanism explains de novo SOAE generation in ~71% of subjects? Is it a change in the cochlear amplifier’s operating point, or a transient gain shift at specific cochlear sites?
Low-frequency stimulation can reveal latent otoacoustic emissions invisible before exposure — What biophysical state does an SOAE occupy when it is unmeasurable pre-exposure yet emerges under LF stimulation? Does this imply SOAEs are always present but vary in the strength of their coupling to the ear canal?
Low-frequency sound exposure triggers long-lasting damped oscillations in spontaneous otoacoustic emissions. — Why would roughly 70% of SOAEs shift up initially while ~12% show the inverted pattern — what distinguishes the two populations mechanistically?
Low-frequency exposure temporarily activates new otoacoustic emissions that were previously absent in most ears. — How does LF exposure cause emissions to appear at new cochlear locations that were silent before? This suggests LF stimulation can transiently boost the local amplifier gain at specific sites — worth understanding the mechanism.
Low-frequency exposure measurably alters cochlear amplifier gain via outer hair cell activity — Worth checking what the long-term health implications of anthropogenic LF sound exposure turn out to be, per the authors’ own open research call.
SOAE bouncing occurs in an open meatus, not only in a sealed ear canal — Rules out middle-ear impedance change as the mechanism, leaving cochlear homeostasis as the explanation — but was there a self-calibration concern about probe placement affecting measured levels?
Low-frequency after-effects on inner-ear gain are calcium-mediated and olivocochlear-modulable — Why did CAS suppress the level-reduction (unadapted) phase but leave the level-increase phase unchanged, and can this asymmetry identify two distinct underlying mechanisms rather than one homeostatic loop?
Low-side suppression operates at very low sound levels in ANFs — Why are low-side suppression thresholds so low compared to excitatory thresholds? What mechanism makes low-side suppression detectable at 0-20 dB SPL?
Noise-induced hearing loss eliminates high-side suppression in some broadly tuned fibers — Does the loss of high-side suppression in HI fibers explain perceptual phenomena like loudness recruitment or poor frequency selectivity in hearing-impaired listeners?
Broadened frequency tuning correlates with loss of suppression in auditory nerve fibers — Is loss of suppression a cause or a consequence of broadened tuning after noise damage, or do both stem from a shared mechanism?
High- and low-side suppression rest on different cochlear mechanisms — What structural mechanism could keep low-side suppression intact while high-side suppression is lost under the same 60% outer-hair-cell loss?
Peripheral hearing damage may trigger compensatory excitation-inhibition shifts in brainstem — Worth chasing: the phenomenon of ‘central gain’ in audiology — that the brain up-regulates gain after peripheral loss — and whether it explains why similar cochlear damage yields very different perceptual outcomes across individuals.
High-side two-tone suppression is lost while low-side suppression survives outer-hair-cell damage. — What could explain the selective vulnerability of high-side suppression? The authors explicitly flag potentially different low- vs. high-side mechanisms.
Two-tone suppression reflects saturation of outer-hair-cell receptor currents. — Worth chasing: why do current hearing aids fail in speech-in-noise despite amplification, and could suppression-based models fix this?
Suppression measured via Wiener kernels bundles together three distinct neural mechanisms — Given that suppression conflates three mechanisms, how does noise-induced hearing loss preferentially affect one over the others?
Higher auditory thresholds predict smaller low-side suppression only at high signal levels — Why does low-side suppression stay sensitive in damaged cochleae while high-side suppression rises exactly with thresholds? Asymmetric vulnerability of the two suppression mechanisms is worth chasing in the parent physiology literature.
Noise-induced hearing loss elevates high-side suppression thresholds within single auditory nerve fibers. — What distinct mechanism could underlie high-side versus low-side suppression that they degrade so differently under hair cell loss?
Second-order Wiener kernels reveal cochlea suppression overlapping excitation in time and frequency — What are the hypothesized timing signatures distinguishing peripheral suppression loss from central gain compensation?
Noise-induced hearing loss eliminates high-side suppression in some broadly tuned fibers — Why would low-side suppression survive noise-induced hearing loss while high-side suppression disappears in some fibers? This asymmetry hints at distinct nonlinear mechanisms worth chasing.
Degraded speech delays semantic integration and blocks contextual relief of effort — Worth chasing: does this delayed-integration effect for degraded speech interact with hearing-aid signal processing that itself introduces degradation?
Signal degradation delays semantic integration and raises listening effort — Worth chasing later: what exact time course (in ms) separates natural from degraded speech in gaze data, and does pupillometry correlate with neural measures of listening effort?
Delayed disambiguation under degradation accumulates working-memory load across running speech. — The claim that per-word processing delays ‘accumulate’ across connected speech is presented as extrapolation, not directly measured here — an empirical gap worth probing in real-world CI listening.
Baseline-normalized pupillometry underestimates effort from continuous degradation. — Raises a methodological worry: if chronically degraded speech raises baseline pupil tone, relative-to-baseline measures may hide the true cumulative effort. Worth chasing in CI listening studies.
Speech-level dips and impairment type independently affect syllable recognition with no interaction — The non-significant interaction is striking: dip/constant and hearing-impairment effects are additive. Worth chasing whether this holds across other simulated impairments.
Compression’s masking-growth effect extends from pure tones to broadband speech — Worth chasing: how far does the compression-masking-growth generalization extend, and does it hold for other complex stimuli like music?
Model-based hearing-impairment simulation isolates the compression component of auditory processing — This model-based simulation approach (the hearing impairment simulator) could be worth understanding in full — how is compression cancelled independently of other peripheral nonlinearities?
Loss of cochlear compression most harms speech recognition of intensity-attenuated syllables — What is the cochlear compression mechanism being cancelled here — outer hair cell function producing the compressive nonlinearity? Worth chasing the underlying physiology.
A hearing impairment simulator can isolate cochlear compression loss in normal-hearing listeners — Does a ‘100% compression’ simulation of an average 80 year old represent genuine presbycusis, and how valid is the 0% compression condition as a pure loss-of-compression model?
A hearing impairment simulator can isolate cochlear compression loss in normal-hearing listeners — Worth confirming how the dcGC-FB simulator models inner-hair-cell compression loss separately from pure attenuation, since that’s the core manipulation here.
A hearing impairment simulator can isolate cochlear compression loss in normal-hearing listeners — The 0% condition removes compression entirely then matches loudness — does removing basal compression change the shape of excitation patterns in ways that ‘loudness matching’ cannot equalize? This seems like a critical methodological asymmetry worth chasing.
Loss of cochlear compression most harms speech recognition of intensity-attenuated syllables — Why does complete compression loss attenuate the quieter syllable’s internal level disproportionately? The mechanism linking outer hair cell function to this syllabic masking deserves follow-up.
Speech can be resynthesized to selectively remove cochlear compression in hearing listeners — Worth chasing: does removing compression via dcGC-FB resynthesis preserve the temporal-fine-structure cues that real inner-hair-cell loss would also degrade? The simulator isolates compression but real presbycusis never does.
A hearing impairment simulator can isolate cochlear compression loss in normal-hearing listeners — Simulating presbycusis separately from compression loss raises the question whether even normal aging involves compression degradation separable from audiometric loss — worth chasing how the dcGC-FB model separates the two.
Age-related hearing decline begins even with clinically normal pure-tone audiograms — The last sentence is odd - good TFS sensitivity associating with less faithful brainstem encoding seems counterintuitive unless TFS processing happens at a different level than brainstem encoding fidelity. Worth checking the original study.
Brainstem encoding accuracy declines with age only for modulated tones — Why do the two stimulus types dissociate so cleanly — age affects modulated-tone encoding while TFS sensitivity affects complex-harmonic encoding — and yet neither correlates with speech in noise?
Better speech-in-noise ability correlated with worse brainstem encoding — This counterintuitive direction — better speech understanding paired with worse stimulus encoding accuracy in EEG — is worth probing: is it an artifact of stimulus choice, a ceiling effect, or a genuine dissociation between cortical encoding fidelity and behavioral performance?
Better speech-in-noise ability correlated with worse brainstem encoding — A counterintuitive reversal: why would superior listeners encode the stimulus envelope less faithfully? Possibly efficient coding, top-down suppression, or a nonlinearity in the SNR metric itself — worth investigating.
Brainstem encoding accuracy declines with age only for modulated tones — Why would age affect envelope-following responses to a modulated carrier but not encoding of a complex harmonic tone? The stimulus-dependent selectivity is unexplained and worth chasing.
Temporal fine-structure sensitivity correlates with brainstem encoding and speech-in-noise ability — The premise that functional-hearing decline can be detected before permanent threshold shift implies a staged cascade — when in noise exposure does TFS encoding degrade relative to audiometric thresholds?
Brainstem and steady-state responses measure audibility but not hearing ability. — What objective measures could bridge the gap between audibility thresholds and actual speech-in-noise ability?
Electrophysiological stimulus encoding declines with age only for modulated stimuli, not harmonic tones — Why would encoding accuracy for the complex harmonic tone not decline with age, and why does better TFS sensitivity paradoxically correlate with reduced encoding for that tone? The dissociation invites mechanistic interpretation.
Age-related hearing decline begins even with clinically normal pure-tone audiograms — This implies hidden hearing loss measured by TFS and speech-in-noise degrades with age before audiometric thresholds shift — worth chasing the larger clinical literature.
Age impairs speech understanding, TFS sensitivity, and encoding even in normal-hearing adults — Why does better TFS sensitivity correlate with less faithful brainstem encoding of a complex tone? This seems counterintuitive and worth chasing.
Better speech-in-noise ability correlated with worse brainstem encoding — The reversed direction of the encoding-accuracy/speech relationship is counterintuitive and unexplained here; could signal a confound or an efficiency tradeoff worth chasing.
Patient history predicts only 10 percent of cochlear implant speech outcomes — What accounts for the other 90% of variation in post-implant speech understanding? That unexplained variance is precisely where brain-imaging prognostics would fit.
The mature brain rewires in response to sensory deprivation — Does glucose metabolism rising with deafness duration index compensatory plasticity or purely metabolic down-regulation reversal? Worth chasing the underlying PET literature.
Good CI speech understanding tracks left-hemisphere language network activation while poor understanding does not — Small sample (single CI user per outcome); worth tracking whether group data confirm the left-hemisphere contrast.
Resting-state connectivity reflects neural plasticity from deafness and reorganisation — Worth chasing: what exactly does default-mode activity’s predictive function entail at the neurophysiological level for hearing-impaired listeners?
Cochlear implant users show weaker inter-hemispheric resting connectivity than normal-hearing listeners — Is reduced inter-hemispheric connectivity a cause or consequence of deafness, and does implantation normalize it over time?
Cross-modal brain plasticity from deafness variably helps or harms cochlear implant outcomes — Which specific communication strategies (lip-reading vs sign vs speech) drive beneficial versus detrimental plasticity is left vague — a precise testable hypothesis worth chasing.
Auditory cortex in deaf brains gets recruited by visual stimuli, harming later implant success. — Does resting-state auditory area hyperactivity in pre-implant deaf children correlate with implant outcomes in a way that could guide early intervention?
fNIRS only images near-surface cortex because depth is spatially limited — Worth chasing: can laser-based pulsed fNIRS actually yield useful 3-D brain images beyond infants, and how shallow is ‘near surface’ in practice?
Patient history predicts only 10 percent of cochlear implant speech outcomes — What accounts for the remaining 90% of variation in implant outcomes — neural plasticity measures like fNIRS are the proposed answer worth chasing.
Good CI speech understanding tracks left-hemisphere language network activation while poor understanding does not — Does frontal recruitment predict good performance, or is it a compensation marker that would predict poor long-term outcomes?
Playing degraded speech after its clear version primes listeners toward intelligibility — Does prior clear presentation genuinely cause greater neural or perceptual recovery of degraded speech, or is the effect purely acoustic?
Phonetic models predict EEG better, but only when speech is intelligible. — Which phonetic representations (Ph vs Fea) drive the improvement, and does this reflect top-down phoneme decoding rather than low-level feature extraction?
The auditory system responds differently to continuous speech than to discrete sounds — This challenges the validity of entire ERP experimental paradigm for understanding speech — worth chasing how exactly discrete-sound responses differ from continuous-speech ones.
Human auditory processing is tuned for continuous speech, not isolated discrete sounds — In what specific ways do neural responses to continuous speech differ from ERP responses to discrete tones? Worth chasing details on the evolutionary/functional tuning argument.
Prior exposure to original speech improves comprehension of vocoded versions — Why does the prime boost confidence only for signal-congruent (‘standard’) trials and not for the physically altered ‘deviant’ stream? The deviant contained a ~5s inconsistent portion — does the prime help at all on the intact stretches?
The auditory system responds differently to continuous speech than to discrete sounds — Worth chasing: what specific evidence (Bonte et al. 2006) shows auditory mechanisms respond differently to continuous vs discrete sounds, and what physiological substrate that difference reflects.
Entracking neurons respond to amplitude modulation with rate scaling linearly with modulation frequency — The neuron’s best response at CF tail rather than at CF is puzzling — worth pursuing what tuning-curve-tail drive implies about how pitch information is routed.
Pitch is coded by firing rate constancy, not spike phase — Whether pitch works via scalar firing rate rather than a tuned filter bank is the open empirical question driving this hypothesis.
Delay-based vector codes for pitch lack plausible brainstem mechanisms — Worth chasing whether the range problem (tens to tenths of ms) has since been solved by identified delay mechanisms in the brainstem.
Uniform cross-neuron firing rate distinguishes true entracking from mere low rate — The claimed distinguishing criterion — low cross-neuron firing-rate variability during entracking — is presented as a hypothesis (‘would again be’); worth chasing whether this has been empirically tested.
Dichotic harmonic presentation boosts resolvability beyond what temporal interaction models predict — The Oxenham dichotic experiment is a genuine challenge to pure temporal models of pitch — warrants deeper reading to understand what mechanism explains it.
Shifted complex tone pitch depends on envelope-fine-structure interaction, not fine structure alone. — Worth chasing how the TE × TSF multiplication interaction term is formally defined — the notation is introduced without derivation.
Dichotic split-harmonic presentation enables pitch via resolvability that temporal models cannot capture. — Worth understanding how splitting harmonics across ears increases resolvability — mechanism is not intuitive and would deepen the pitch-model picture.
Pitch-change sequence processing drives larger N1m and P2m responses than constant notes — Worth chasing: can the pitch-processing N1m/P2m enhancement be dissociated from simple note ‘novelty’ or expectation effects in future designs?
Melodic notes trigger earlier and larger auditory cortex responses than tonic notes — Why would a melodic (presumably more complex) note produce an earlier peak than a simple tonic? The authors flag this as counterintuitive—worth understanding the mechanism.
MEG source models distinguish energy-onset responses from between-note transition responses — The attention diversion to a silent movie is a notable design choice — how does diverted attention shape the melodic processing signal being measured?
Electrical brain stimulation may enhance sounds temporally coherent with entrained oscillations — Worth chasing whether phase-locked enhancement reflects genuine cortical gain or an artifact of attention/arousal entrainment.
Perceptual gains from tACS entrainment are small and often statistically weak — Whether the modest effect sizes reflect genuine limits of tACS or suboptimal parameter selection is unresolved.
Transcranial alternating current phase modulates auditory click detection at the stimulation frequency — Does tACS non-invasively entrain auditory processing in a phase-dependent way that might generalize to other sensory or cognitive tasks?
Phase-coherent brain stimulation may boost perceived loudness of matched click trains — Whether tACS enhances loudness of temporally coherent versus anti-coherent click trains is offered as an interpretation, not settled — worth tracking the experimental confirmation.
tACS phase benefits to hearing are real but statistically modest — The modest, non-significant effect sizes (7% click improvement, 0.3 dB tone) raise the question of whether tACS benefits are practically usable at all.
Transcranial stimulation may someday bypass impaired auditory pathways by injecting envelope information — Could individualized EEG-matched tACS serve as a therapeutic adjunct for hearing-impaired listeners whose peripheral pathway is damaged?
Scalp current can entrain sound processing while bypassing the peripheral ear — An open research program: whether tACS entrainment generalizes beyond alpha range and into complex listening scenes.
tACS phase effects generalize from the alpha to the delta and theta oscillation ranges — Worth tracking whether the ongoing informational-masking study shows tACS effects on scene analysis — a key open question about real-world relevance.
Perceptual segregation of target sounds may arise from temporal coherence with cortical oscillations — Worth chasing: how exactly cortical temporal coherence with a target enables segregation from spectrally overlapping distractors.
tACS can entrain cortical oscillations while bypassing the peripheral auditory system — If tACS bypasses the periphery, how exactly does current cross intervening tissue and reach auditory cortex reliably? Worth chasing for mechanism clarity.
Phase timing between acoustic and electric stimulation was the core experimental variable — What physiological mechanism makes the relative phase between a 4-Hz current and a 4-Hz click train matter? TACS phase effects likely ride on neural oscillation entrainment but the causal chain is unclear.
Key open questions concern feedback contributions and dorsal-ventral pathway integration — Feedforward vs feedback separation is the fundamental open methodological problem in choice-activity research.
Coherent auditory perception requires both dorsal spatial and ventral identity processing — How exactly do spatial cues from the dorsal pathway interact with non-spatial ventral processing to form a unified percept during a task?
How feedforward and feedback signals shape auditory perception remains unknown — The cited open questions — attention interacting with hierarchy, perceptual grouping with decision-making, dorsal-ventral integration — are research directions worth tracking for the reader’s understanding of hearing physiology.
Apparent decision-related cortical activity can conflate stimulus coding with choice coding — The confound between stimulus-related and choice-related activity frames the central methodological danger in reading perceptual decisions from cortex — worth chasing into Nienborg’s choice analyses.
Listeners can extract either speaker identity or linguistic content from the same speech sound — The speaker-identity versus content dissociation connects to classic ‘who said what’ streams-of-processing debates worth chasing later.
Choice-related neural signals are easily confounded with stimulus-driven activity in analysis — What trial-selection method cleanly dissociates choice modulation from stimulus correlation - ideal-observer or jackknife analyses worth investigating.
Reverberation and natural pitch modulation combine to impair vowel segregation more than either alone — Why does F0 modulation interact with reverberation but not with noise? The dissociation suggests different degradation pathways worth investigating.
Inferior colliculus may compensate for reverberation via inter-aural correlation. — A proposed mechanism for reverberation compensation downstream of the cochlear nucleus worth chasing in the Slama and Delgutte 2015 literature.
Auditory cortex achieves noise-and-reverberation-invariance that brainstem processing lacks. — The inferior colliculus inter-aural correlation compensation hypothesis is flagged as tentative (“perhaps one clue”) — worth chasing how that mechanism works.
Periodicity differences segregate concurrent sounds only under idealized quiet conditions — Worth later chasing: to what degree does the VCN’s periodicity segregation survive the degradation that defeats it at the behavioral level?
Voluntary attention modulates how audiovisual contexts affect subsequent fusion — Does the coherence index operate via comodulation in time as a low-level cue, or through higher-order semantic matching? Worth exploring what ‘audiovisual comodulations’ are measured as.
Brief incoherent audiovisual contexts reduce the McGurk effect by decreasing visual weight. — The unbinding mechanism — a few seconds of incoherent content durably reduces visual weight in fusion — begs the question of what temporal window and coherence threshold drive rebinding.
McGurk responses are processed 70-80 ms slower than matching audiovisual targets — Why would an illusory McGurk percept be processed slower? Likely tied to the extra neural processing needed to resolve conflicting audio-visual cues, worth chasing in later chapters.
Syllable speech binds audio and video more strongly than sentences do — Is the coherence gradient simply temporal synchrony, or does phonological structure play a role?
Two-stage scene analysis extends from auditory to audiovisual speech scenes. — Worth chasing: how exactly does the binding stage ‘control’ the decision output — a weighting shift or a hard gate?
A coherence index computed before fusion decides whether audiovisual inputs bind — What exactly constitutes the coherence index physiologically — is it a real neural correlate or a modeling construct? Worth chasing in the references.
Brief prior exposure to mismatched audio-visual speech reduces later visual weight in fusion. — What is the time course and persistence of this unbinding effect beyond a few seconds of context?
Syllable stimuli show far higher audiovisual correlation than sentences do — Why do audiovisual correlations differ so dramatically between syllables (0.59) and sentences (0.10) — is this a general acoustic-visual property or specific to this stimulus set?
Brief incoherent audiovisual contexts reduce the McGurk effect by decreasing visual weight. — The ‘unbinding’ interpretation is interesting — does the context effect decay over time, and over how long? Suggests a temporal-window question.
Stronger temporal audiovisual coherence increases visual weight in binding — Is the premise that syllables show stronger audiovisual modulation over time than sentences actually established in the literature cited, or assumed?
Large melodic intervals and multiple transpositions each independently impair deviant detection. — Does the insensitivity to Western-important intervals (perfect fifth/fourth) hold in impaired hearing populations, or does training interact differently?
Even trained musicians have great difficulty detecting deviant melodic patterns under transposition — Why does interval recognition training fail to transfer to this deviant-detection task? What is the mismatch between the trained skill and the experimental demand?
Neural detection of transposed melodies coexists with surprisingly poor explicit interval recognition — Interesting tension: infants show neural discrimination of transposed patterns yet adult explicit interval detection is poor — worth chasing the developmental or task-level explanation.
Relative pitch perception is more limited than commonly assumed — What exactly limits relative pitch processing — memory, representation coherence, or comparison demands? Worth chasing the McIntosh/Barascud thread.
Randomly transposing a standard phrase makes oddball melodic phrases nearly undetectable even for musicians — That musicians perform near chance on transposed oddball intervals undermines the robustness assumption — worth chasing the mechanism behind this surprising failure.
Pattern interval size, transposition, repetition, and variance all shape pitch pattern judgment accuracy — These six hypotheses are testable predictions — worth chasing the experimental outcomes to see which survived.
Perceptual discrimination precision falls as stimulus variance rises — How strictly does the inverse precision-variance relation hold empirically for pitch discriminations vs. other modalities?
Participants scoring under 30 percent were dropped for misunderstanding — Why does a score below 30 percent on a two-alternative task suggest the task was misunderstood rather than a genuine perceptual deficit? Chance is 50 percent, so sub-30 suggests systematic responding against the target, perhaps a reversed judgment criterion.
Pitch perception differs across listeners despite normal hearing thresholds — Where do individual differences in pitch audibility come from, and why do they survive ‘normal hearing’ screening? Worth chasing into cochlear dead-region literature.
Familiar consonant final intervals hinder deviant-tone detection relative to small ones — Counterintuitive — musical training and consonance expectations usually help, so why did perfect fourths/fifths impair detection compared to a 1-semitone final interval? Worth chasing the mechanism.
Zwicker tones produce pitch without peripheral phase locking. — The claim that ZTs lack phase locking is asserted as inference from mechanical-noncochelear origin — worth chasing the actual evidence for this.
Zwicker tones have no cochlear mechanical or otoacoustic correlate. — If ZTs have no peripheral phase locking yet produce pitch, this is direct evidence pitch can be central rather than temporal — worth exploring what the central mechanism could be.
Complex high-frequency tones evoke pitch where pure tones fail above 6 kHz. — The gap between complex and pure tone pitch above 6 kHz suggests place-based (spectral) mechanisms may carry musical pitch — worth investigating how harmonic structure feeds place coding.
Listening to notched noise can unmask spontaneous otoacoustic emissions — How does notched noise exposure unmask spontaneous OAEs — does it alter the cochlear amplifier’s gain temporarily?
Zwicker tones support accurate musical interval matching like pure tones — This experimental design tests whether a phantom auditory sensation (Zwicker tone) can be pitched relative to real tones — worth chasing how the results came out, since it bears on whether illusory pitches follow the same perceptual rules as physical ones.
Subjects matched musical intervals within about 2 percent in both conditions — The consistent slight flatness (larger intervals) in both conditions is interesting — is this a perceptual constant or an artifact of equal-temperament expectation?
Weak musical pitch perception can persist without peripheral phase locking. — If pitch survives without phase locking, what peripheral code actually carries it — and how would this interact with models that insist on temporal cues?
Weak musical pitch perception can persist without peripheral phase locking. — If ZTs lack phase locking, what central mechanism encodes their pitch? Worth chasing the proposed alternative.
Musically trained listeners can judge pitch intervals even above the phase-locking limit — Whether musical training can override or compensate for the loss of phase-locking timing cues at high frequencies is worth chasing in further reading.
Musically trained listeners can judge pitch intervals even above the phase-locking limit — Does the role of musical experience in high-frequency pitch judgment hint at top-down/cortical mechanisms rather than peripheral phase locking?
Zwicker tones support accurate musical interval matching like pure tones — What auditory mechanism allows Zwicker tones (normally a faint ‘ringing’ percept) to carry enough pitch structure to support interval tuning matching ordinary tones?
Hearing loss degrades formant coding by weakening synchrony capture across auditory-nerve fibres — The mechanism moves coding of formants from fine-structure to F0-rate fluctuations in the healthy ear; worth verifying how this maps to modulation transfer functions measured behaviorally in impaired listeners.
Rate-place coding of formants saturates at high levels while intelligibility persists. — If rate profiles saturate but intelligibility survives, something other than rate-place coding must carry the speech — a nice hook into temporal coding theories.
Hearing aids should restore F0-synchrony profiles not stimulus spectra — The open experimental question — whether restoring peripheral F0-synchrony improves speech intelligibility — is worth tracking as later chapters or papers may answer it.
Hearing aids should restore F0-synchrony profiles not stimulus spectra — Whether F0-synchrony restoration in peripheral input can actually improve intelligibility remains an untested experimental question.
SNHL disrupts the fluctuation pattern that normally codes vowel formants. — What evidence directly ties the diminished F0-fluctuation profile from SNHL to the disproportionate difficulty in noisy environments noted in 5474?
Speech processing strategies should restore F0-synchrony profiles rather than AN spectra — Untested hypothesis about whether central plasticity after cochlear trauma would undermine restitution attempts.
Hearing loss degrades vowel coding primarily through F0-synchrony loss rather than mean firing rates — The proposed experiment — restoring F0-synchrony profiles and testing speech-in-noise — is a concrete open question worth tracking in the SNHL signal-processing literature.
Steady-state masking noise varies perceptually even with a 100-ms time shift. — Worth chasing: what acoustic property of a 100 ms time-shifted frozen noise changes consonant cue masking? Purely a fine-timescale envelope effect?
Slow envelope fluctuations up to 16 Hz predict consonant-in-noise perception. — What is special about the 16 Hz cutoff — does it map onto syllable rate, or onto some physiological filtering mechanism in the auditory system worth chasing?
Slow envelope fluctuations up to 16 Hz predict consonant-in-noise perception. — Why do envelope modulations up to 16 Hz carry consonant information better than pure energy? The modulation-rate bound suggests a specific temporal-processing mechanism worth investigating.
Internal listener noise rises steeply as signal-to-noise ratio worsens — Across-listener variability in consonant perception remains unexplained — sensitivity differences, cognition, or individual bias? Open research question worth chasing.
The causes of individual listener differences in consonant perception remain unresolved — The authors’ own open question on the mechanistic origin of across-listener variability — sensitivity vs cognition vs bias.
Talker identity causes more perceptual variation in consonant perception than noise waveform shifts — What does ‘steady-state’ masking noise being non-steady imply for all classic masking experiments that assume stationarity?
Internal decision noise rises as speech-to-noise ratio falls. — What physiological mechanism makes internal noise SNR-dependent - is it spontaneous-rate nerve firings, adaptation, or decision-stage uncertainty?
Internal decision noise rises as speech-to-noise ratio falls. — Whether normal-hearing listener variability stems from sensitivity differences, cognition, or individual bias remains an open modeling problem.
A minimum 6 dB SNR change is needed to motivate people to seek intervention — The gap between ~3 dB JND and ~6 dB JMD is striking — worth chasing what mechanisms explain why meaningful benefit needs double the just-noticeable change.
Meaningful speech-in-noise benefit requires double the detectable SNR change — That age and hearing loss did not correlate with JND or JMD is surprising — worth investigating whether this generalizes beyond this sample.
SNR discrimination worsens at favorable signal-to-noise ratios — Why would the JND grow (from ~2.9 to 3.7 dB) specifically at the +6 dB favorable reference SNR? Worth chasing whether ceiling effects, loudness cues, or reduced neural synchrony explain this asymmetry.
Listeners may judge SNR changes via perceived intelligibility rather than raw signal level — This ambiguity — listeners judging SNR discriminability may actually be responding to intelligibility change — is worth chasing into the hearing-perception literature.
A just-noticeable difference in signal-to-noise ratio does not guarantee subjective value — The claim that psychophysical JND does not map onto clinical meaningfulness is worth chasing — it separates detection thresholds from subjectively important benefit.
Meaningful hearing benefit emerges at 4 dB SNR regardless of presentation order — Why would individual JNDs fail to correlate with meaningfulness ratings if detectability is requisite? The dissociation deserves closer scrutiny.
Repeated sentence presentations inflate perceived benefit and deflate perceived deficit — The familiarity/intelligibility boost from sentence repetition is cited from 1956 — worth probing the mechanism and whether modern intelligibility research has refined this.
Hearing aid SNR improvements fall below the just-noticeable threshold for speech understanding — The chapter implies SNR gains below the JND/JMD could still matter via attention, cognitive ease, or psychosocial engagement — worth chasing what the measurable non-SNR benefits actually are.
Measured SNR JNDs may actually reflect intelligibility rather than SNR discrimination — An unresolved interpretive ambiguity in JND measurement that could be followed up in the methodology literature.
The just-noticeable difference for speech in noise was nearly unstudied until recently — This JND finding sits near the core of the reader’s psychoacoustics interest but the passage gives only initial framing; worth reconfirming later.
A hearing-device benefit must exceed roughly 6 dB SNR to prompt voluntary switching. — Why does the JMD scale with reference SNR so strongly — is this a perceptual compression effect or a loss-aversion/valuation artifact?
A psychophysical JND in signal-to-noise ratio does not guarantee subjectively meaningful benefit to patients — Only one prior small study has measured the JND for SNR in speech — why so under-studied given its centrality to hearing aid fitting?
Detectable changes in speech SNR correspond to roughly 26 percent changes in word intelligibility — Whoa — a 3 dB change maps to a 26% intelligibility change? That seems enormous, worth checking the slope of the psychometric function and whether this generalizes.
Detectable changes in speech SNR correspond to roughly 26 percent changes in word intelligibility — If JND domain is ambiguous, how can hearing-aid trials that quantify ‘meaningful benefit’ in percent-correct terms avoid circularity? Worth chasing.
A repeated sentence is more intelligible on second hearing — This order/learning effect on sentence intelligibility could interact with clinical speech testing. Worth chasing how large the second-presentation boost is.
Otoacoustic emission levels predict which tones listeners weight more heavily — Pilot data only — worth tracking whether the SOAE/SFOAE-to-decision-weight correlation replicates with larger samples.
Pure tone audiograms fail to predict speech-in-noise listening difficulties — What physiological differences beyond audiometric thresholds explain cocktail-party difficulty in nominally normal-hearing listeners?
Otoacoustic emission irregularities affect perceptual decisions without requiring SOAE detection — The claim that the effect is not mediated by conscious SOAE detection is interesting - what mechanism links fine-structure depth to weighting if not detectability? Worth chasing the physiology.
Pure tone audiograms fail to predict speech-in-noise listening difficulties — What physiological deficit explains normal audiograms but impaired cocktail-party hearing? This is a genuine open question worth chasing.
Discarding high-power segments suppresses subject-generated noise in otoacoustic recordings — Why 1-Hz resolution and 250 ms step — what artifact rejection rationale underlies halving segments by power?
Otoacoustic emission levels predict individual decision weights in level discrimination tasks — Would swept SFOAEs prove clinical-grade predictors of speech-in-noise performance beyond the lab finding described here?
Cochlear emission fine structure may predict individual listening performance — Is the proposed causal chain (SFOAE minima/maxima -> loudness -> decision weights -> speech-in-noise) actually supported, or is loudness a confound rather than a mediator?
Differential metrics can separate cochlear gain loss from temporal coding deficits in hearing loss — Mechanism unclear — how does reduced gain counteract envelope-coding degradation in AM thresholds? Worth chasing for the physiology links.
Narrowband noise masks AM detection most in listeners with the poorest cochlear gain measures. — The claim that the masker specifically targets a temporal coding deficit is a tentative interpretation; worth deeper interrogation of the mechanism.
Normal-threshold listeners can show temporal coding deficits localized to the auditory brainstem — Worth chasing: what are the clinical implications of a hearing deficit that normal audiograms miss entirely?
Narrowband noise degrades AM detection mainly in listeners with reduced temporal coding — The paragraph notes an alternate brainstem modulation-coding explanation for the NB results — worth chasing whether envelope fluctuation in the masker, not fiber count, drives the effect.
Cochlear gain loss may paradoxically enhance modulation sensitivity in broadband noise — The proposed mechanism — that compression loss enhances modulation sensitivity — is presented as a tentative hypothesis; worth chasing whether psychoacoustic models support this apparent compensation effect.
Cochlear neuropathy and gain loss may push AM detection in opposite directions — The open question this whole chapter is built around: how the two pathologies interact, and whether neuropathy matters as much as gain loss. Worth tracking through the results.
Narrowband noise disrupts amplitude modulation detection in cochlear neuropathy more than broadband noise does. — The interaction between reduced compression and neuropathy in AM detection is genuinely unresolved; worth chasing the study’s outcome and methods.
Cochlear gain loss can compensate for temporal coding deficits in AM detection — What is the mechanism by which cochlear gain loss compensates? Does the reduced gain flatten the effective dynamic range and thereby improve envelope coding relative to internal noise?
Narrowband noise degrades AM detection mainly in listeners with reduced temporal coding — Is the stronger narrowband effect in HI listeners due to loss of off-frequency listening or wider effective auditory filters?
Wider auditory filters and steeper cochlear compression reduce noise-induced hearing loss. — Why would wider filters confer noise robustness? Common assumption is narrow filters aid signal extraction — this seems to reverse that for noise degradation.
Narrowband noise masking disproportionately impairs hearing-impaired listeners’ amplitude modulation detection — The alternative brainstem modulation-coding explanation for the NB result is flagged but not resolved here — worth checking whether later EFR or central measures disambiguate it.
Inferred basilar-membrane gain in humans is about 41 dB at 4 kHz — A 41 dB inferred human gain at 4 kHz is worth checking against physiologically measured BM gain in other species and other human estimates. Xref
Cochlear hearing loss spares amplitude modulation detection but distorts perceived fluctuation depth — The loudness-recruitment mechanism that amplifies perceived modulation connects to the cochlear gain/compression physiology covered in earlier chapters.
Cochlear hearing loss spares the binaural processing of envelope cues — A clean dissociation parallel: ENV spared versus TFS degraded binaurally mirrors the monaural results for AM detection vs DLFs.
Monaural temporal fine structure processing declines with age even without hearing loss. — Connects to the reader’s broader interest in normal-hearing auditory physiology; also relates to other findings on age-related declines independent of audiometric loss.
Hearing loss primarily harms monaural TFS processing while age harms binaural TFS processing — The King et al. 46-subject age-and-loss matrix data resonates with the reader’s interest in parsing age from loss confounds.
Hearing loss primarily harms monaural TFS processing while age harms binaural TFS processing — Connects to the reader’s interest in how both cochlear damage and central aging independently degrade distinct auditory features.
Envelope and temporal fine structure exist at three distinct processing levels — Connects to the reader’s broader interest in where acoustics becomes neural coding — parallels pitch perception and sound segregation mechanisms.
Monaural temporal fine structure processing declines with age even without hearing loss. — The matched-audiogram design answer to the threshold-correlation confound is a strong methodological parallel to how the reader elsewhere separates peripheral from central processing changes.
Binaural temporal fine structure sensitivity declines by middle age independently of hearing thresholds — Directly connects to the reader’s stated focus on psychoacoustics of temporal fine structure processing; the age-decline-with-normal-audiogram dissociation is a recurring causal claim in this literature.
Frequency discrimination thresholds are 2-4 times worse in older adults with normal audiograms. — Connects to the general theme of ‘processing efficiency’ decline with age as distinct from sensory loss, which parallels the reader’s existing interest in how peripheral and central aging factors separate.
Aging reduces envelope processing efficiency but not temporal resolution. — Contrasts with the TFS aging result in location 801, showing aging affects two auditory cues differently.
Monaural temporal fine structure processing declines with age even without hearing loss. — Links to age-related central auditory processing decline literature, alongside earlier binaural TFS findings in this chapter.
Cochlear hearing loss independently degrades binaural processing of temporal fine structure — Connects to broader theme from Zero Decibels/One Square Inch that age and environment confound perceptual measurements.
Frequency discrimination deficits depend on temporal fine structure, not excitation patterns — Connects to the loudness-recruitment and cochlear filtering material from earlier chapters on psychoacoustic consequences of cochlear damage.
MOCR gain reduction interacts with suppression to reduce it — Key mechanism linking efferent feedback and nonlinear suppression; central to the reader’s psychoacoustic understanding.
Masker durations over 25 ms trigger efferent feedback that reduces measured suppression — Connects to the reader’s standing interest in how efferent/feedback mechanisms shape perceptual estimates — a measurement-artifact mechanism.
Age-related endocochlear loss hits basal thresholds harder than apical ones — Explains why aging produces high-frequency threshold loss first, tying physiology to the classic audiometric shape of presbycusis.
High-side suppression diminishes or disappears with age — Connects to the reader’s broader interest in how cochlear nonlinearity degrades with age, distinct from pure threshold elevation.
Cochlear gain declines with age before measurable threshold elevation appears — This tracks the familiar idea that cochlear damage (e.g. synaptopathy, endocochlear potential loss) can precede audiometric threshold shift — connects to hidden hearing loss literature.
Age-linked decline in cochlear function stems from falling endocochlear potential — The apex/base discrepancy in endocochlear-potential effects is a mechanism echoed across the presbycusis literature.
Long combined maskers trigger MOCR gain reduction that underestimates true suppression — Connects to the reader’s broader interest in medial olivocochlear reflex physiology and efferent control of the cochlea.
Older adults show reduced cochlear two-tone suppression — Connects to general reading on age-related hearing loss and MOCR mechanisms; ties cochlear gain loss to endocochlear potential decline.
Aging impairs speech-in-noise ability beyond what hearing thresholds predict — Endocochlear potential decline with age links to the cochlear amplifier / OHC gain mechanisms discussed elsewhere in hearing physiology.
Sound durations longer than 25 ms recruit efferent feedback that reduces measured suppression — The idea that a measurement paradigm itself alters the measured mechanism is a classic reflexivity problem paralleling hearing-aid and adaptation research.
Off-frequency masking responses remain linear despite MOCR activation — Connects to the broader cochlear mechanics literature on basilar membrane linearity versus cochlear amplifier compression, used here as the rationale for an off-frequency masker control.
Older adults show significantly lower cochlear gain estimates than younger adults — This dissociation between gain reduction and audiometric threshold connects to the broader psychophysical literature (e.g., cochlear amplifier and hidden hearing loss) showing that gain changes can occur without measurable threshold shift.
Basilar membrane suppression can be altered by the sluggish medial olivocochlear reflex — Causal mechanism of cochlear gain reduction via MOCR ties to efferent/descending auditory system literature.
Age-related speech-in-noise difficulty stems from declining endocochlear potential, not just threshold elevation — Connects to known phenomenon that audiometric thresholds underestimate real-world hearing decline in aging — ties to spatial hearing / speech-segregation research.
Cochlear suppression declines with age in both high-side and low-side components — Relates to the classic ‘loss of suppression’ concept in auditory aging — reduction of cochlear compressive nonlinearity, central topic in impaired-hearing research.
Higher auditory thresholds predict smaller low-side suppression only at high signal levels — Relates to the broader literature tying age-related hearing loss (presbycusis) to reduced cochlear non-linearities including suppression, paralleling otoacoustic-emission findings.
Suppressor duration windows distinguish MOCR-mediated gain from cochlear mechanics. — Relates to the reader’s central question of physiological mechanisms behind psychophysical suppression estimates.
A brief transient signal with no steady state isolates cochlear suppression effects. — The no-steady-state design matters for how masking and gain reduction play out across the physiology timeline.
Peripheral monaural coding efficiency partly predicts binaural lateralization ability — Connects to peripheral vs central auditory processing debates and the question of where inter-listener variability originates.
Age-related correlations in auditory tasks may reflect frequency selectivity, not temporal coding. — Echoes the general statistical lesson in the Milgrom/trust material about carefully decomposing correlated predictors.
Temporal-structure sensitivity declines with age even when audiograms remain normal. — Reinforces the known dissociation between audiometric thresholds and temporal processing that Hopkins and Moore established.
Apparent links between temporal tasks may be confounded by frequency selectivity — Connects to the reader’s broader theme of aging and hearing, and to the role of peripheral frequency selectivity (ERB) in temporal processing — resonates with the account of the elderly-ears audiometric story in other chapters.
Apparent temporal-coding correlations across tasks vanish once age effects are removed — A cautionary link example for my own writing on hearing: age is a confound that can manufacture false correlations between supposedly related measures.
Monaural encoding efficiency partly explains inter-listener differences in binaural sensitivity — Links to the reader’s broader interest in the physiological stages between cochlear transduction and binaural integration.
Mixing young and elderly listeners sharpens correlation analyses by widening threshold ranges. — The age-expansion strategy for boosting statistical power parallels the ‘add variance deliberately’ design logic familiar from other psychophysics and experimental-design work.
Transposed stimuli induce phase locking to the envelope at high carrier frequencies — Connects to Bernstein and Trahiotis 2002, foundational transposed-stimulus work in psychoacoustics.
Low-frequency pitch judgments reflect temporal fine structure via neural phase locking — Connects to the reader’s goal of understanding TFS coding—a direct behavioral probe of phase-locking precision, complementing measures of frequency selectivity in the FRES task.
Notched-noise masking quantifies frequency selectivity as equivalent rectangular bandwidth — The roex/notched-noise method and ERB connect to general psychoacoustics texts (e.g., Moore) that define auditory filter analysis.
An interaural phase transition in noise evokes a pitch sensation — Ties to the reader’s interest in psychoacoustics—the Huggins pitch demonstrates that binaural cues alone can generate a pitch percept, relevant to coding of temporal fine structure.
Age-related correlations in auditory tasks may reflect frequency selectivity, not temporal coding. — Cautionary example for interpreting correlation matrices in aging studies — mirror of confounds discussed throughout hearing research.
Inter-Listener variability in sound localization partly reflects peripheral coding efficiency that precedes binaural computation. — Connects to the physiology-cognition reading thread: peripheral monaural coding efficiency shaping higher binaural percepts.
Inter-listener ILD differences arise from pre-binaural level-coding efficiency — Relates to broader theme that individual sensory differences often localize to early encoding stages, echoing the coding-efficiency emphasis in the reader’s existing audiogram/lateralization knowledge.
Young listeners’ working memory stays irrelevant to speech-in-noise even at very difficult signal levels — This reverses the common Ease of Language Understanding (ELU) framework assumption that cognitive capacity moderates speech perception even in normal hearing — connects to Rönnberg’s model.
Young listeners’ working memory stays irrelevant to speech-in-noise even at very difficult signal levels — Connects to the broader evolving understanding of hearing as partly a cognitive rather than purely sensory act, linking to other resources on working memory and listening effort.
Higher working-memory capacity predicts better speech-in-noise identification in hearing-impaired listeners — Relevant to earlier-established limits of psychoacoustic and cognitive models of speech perception — connects to the broader literature on individual differences in hearing.
Higher working-memory capacity predicts better speech-in-noise identification in hearing-impaired listeners — Links to the Ease of Language Understanding model cited in the same chapter and to the reader’s interest in cognition in normal hearing as counterpoint.
Speech-in-noise working-memory studies are routinely underpowered with null-inclusive confidence intervals — Connects to replication-crisis literature on low-powered psychology studies and the p-hacking incentive structure that follows from them.
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — This dissociation echoes the broader finding throughout this book that cognitive compensation becomes engaged only as peripheral and perceptual processing become less reliable; connects to Chapters 1–2 themes on age-related cognitive involvement.
Age-related temporal and level sensitivity decline precedes measurable hearing loss — Connects to the broader theme of silently progressive sensory decline discussed in aging/cognition literature; also pairs with the later finding that older participants’ performance gaps are not explained by audibility alone.
Age-related hearing decline in noise begins while thresholds remain normal — Connects to the broader literature on hidden hearing loss and cochlear synaptopathy, where audiometrically normal ears mask age-related neural degeneration.
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — Relates to the aging and cognitive-compensation literature — older adults rely more on top-down cognitive resources (working memory) than younger adults, paralleling findings in cognitive aging and sensory-cognition coupling.
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — This age-modulation of the WMC-SiN correlation speaks directly to the reader’s physiological/cognitive interest: suggests that young listeners rely on bottom-up auditory processing, and cognitive compensation (working memory) only matters once peripheral processing degrades.
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — The age-group dissociation suggests WMC’s predictive role only emerges once perceptual resources decline — echoing the compensatory-coupling hypotheses and scaffolding theories of aging cognition.
Working memory capacity explains under two percent of speech-in-noise variance in young listeners. — Connects to the broader literature questioning working-memory as a major contributor to speech-in-noise; relevant to models of cognition-hearing interaction.
Most working memory and speech-in-noise studies are underpowered to detect real effects. — Part of the wider well-powered-samples critique in cognitive hearing science, echoing replication-crisis findings in psychology.
Clinically normal hearing still shows age-linked decline that affects speech perception — Connects to the reader’s interest in hidden age effects beneath clinically normal thresholds — relevant to any chapter on aging, hidden hearing loss, or audiological screening limits.
Higher working-memory capacity predicts better speech-in-noise identification in hearing-impaired listeners — Connects to the broader cognitive-hearing ‘ease of language understanding’ model literature this reader explores elsewhere.
The relevance of a cognitive ability depends on the listening situation — Resonates with the idea from earlier reading that cognitive-load tradeoffs are context-dependent, e.g. the work on signal vs. noise distinction in soundscapes.
Working memory storage helps intelligibility while inhibition and vocabulary do not — Connects to Ease of Language Understanding model and the general finding that cognitive factors matter more at low SNR — relevant to the reader’s interest in cognition of normal hearing.
Which cognitive ability predicts speech-in-noise intelligibility depends on the listening situation — Connects to the aging/noise frameworks from other chapters and to the broader literature on working-memory models of speech perception (e.g. Rönnberg’s Ease of Language Understanding).
Poorer hearing drives reliance on more prosodic cues for affect judgement — Relates to how sensory degradation forces compensatory strategies across sensory modalities — parallels findings in the soundscape/Deep Listening readings about attention shifting under duress.
Age-normal hearing can be less sensitive to intensity than aided impaired hearing — Reverses the naive assumption that any hearing aid user is worse off than an age-normal listener; echoes surprise findings elsewhere about aided users out-perceiving ‘normal’ controls on specific dimensions.
Hearing loss severity drives listeners toward alternative acoustic cues for arousal — Connects to the trade-off theme in the noise/silence books: when one perceptual channel degrades, listeners recruit alternative ones rather than simply losing information.
Acted speech masks true effects of hearing loss on affect perception — Echoes the general research-methods lesson that exaggerated/paradigmatic stimuli wash out real-world effect sizes — parallels design critique in behavioral studies.
Hearing aid users’ affect perception is tested aided and unaided against normal-hearing controls — Connects to the broader line of research on how degraded auditory input affects emotional prosody processing in older adults.
Hearing aids do not change affective valence perception but do alter arousal perception — Connects to the reader’s interest in how degraded auditory access (hearing loss) changes weighting of prosodic cues — links to general psychoacoustic work on cue weighting in speech perception.
Hearing aid use restores arousal perception but shifts cue weighting — Connects to the general theme that hearing loss is not merely loss of audibility but loss of prosodic/affective information — parallels how reduced sensory bandwidth changes affective decoding.
Hearing loss severity drives listeners toward alternative acoustic cues for arousal — Connects to psychoacoustic work on how sensory impairment triggers use of alternative acoustic cues — a form of neural/computational compensation analogous to other sensory-substitution findings.
Acted speech masks true effects of hearing loss on affect perception — Connects to the broader psychoacoustic theme that laboratory stimuli often diverge from ecological conditions, mirroring points in Hinton/Uchanski and other chapters about natural vs. acted speech.
Hearing aids improve intelligibility but may not restore emotion perception — Connects to the broader theme across the literature that intelligibility and emotion perception are dissociable in hearing loss — links to the reader’s chapter on hearing-aid physiology in this same volume.
Emotional prosody cues are shared across closely related languages — Connects to the reader’s existing work on soundscape and acoustic communication meaning.
Valence perception is hearing-loss independent while arousal perception depends on amplification. — Relates to psychoacoustic finding that F0 (pitch) carries valence while intensity carries arousal — classic emotional prosody substrate.
Hearing loss severity drives listeners toward alternative acoustic cues for arousal — Connects to the book’s theme of sensory compensation and cue-weighting strategies, and bears on how perception research must account for listener adaptation rather than treating impairment as simple signal loss.
Binaural interaction components show statistically significant electrode tuning in humans — Connects to objective methods for cochlear implant fitting/eABR; also to interaural pitch and timing psychoacoustic methods for electrode pairing.
Brain plasticity can make pitch-matching data misleadingly good in CI users — Connects to plasticity/adaptation themes in auditory perception and to the general distinction between subjective report and objective physiological measures.
Pitch-matched electrode pairs do not maximize binaural time-difference sensitivity — Connects to peripheral-place and tonotopic-mapping literature in cochlear implant psychophysics — pitch and timing are coded by overlapping but distinct mechanisms.
Pitch-matching and binaural-cue methods yield uncorrelated electrode pairings in implant patients — Counterintuitive place-vs-pitch dissociation resonates with the general principle that perceived pitch and spatial/implant stimulation site diverge, connecting to cochlear tonotopy literature.
Pitch matching can be misleadingly good due to acclimatization and brain plasticity — Relates to the reader’s interest in cognition — acclimatization as top-down plasticity reshaping perception. Crosses to perceptual-learning literature and Neuroplasticity accounts of sensory remapping.
BIC amplitude does not correlate with interaural pitch comparison outcomes. — Connects to the broader theme of physiological-vs-perceptual measures disagreement seen in the reader’s hearing science knowledge base.
Binaural brainstem neurons compare only interaurally place-matched inputs. — Connects to the reader’s interest in binaural physiology and how the brainstem performs interaural comparisons — a mechanism-level fact about normal hearing that explains why CI electrode mismatch degrades performance.
BIC-based electrode pairing is validated against psychoacoustic pitch and timing measures — Connects to the reader’s broader interest in how physiological measures (eABR) are cross-validated against psychoacoustic perception measures — a core interface between physiology and cognition of hearing.
Physiological and behavioral binaural measures agree with each other but not with pitch matching. — The dissociation between pitch-perception measures and binaural-physiology measures resonates with broader questions in auditory neuroscience about independent coding pathways.
BIC-based electrode pairing is validated against psychoacoustic pitch and timing measures — Relates to the reader’s knowledge of auditory evoked potential recording and binaural processing.
Interaural electrode mismatch degrades bilateral cochlear implant binaural function. — Connects to binaural hearing physiology and objective audiological fitting approaches.
Pitch-matching and binaural-cue methods yield uncorrelated electrode pairings in implant patients — Connects to the broader theme that pitch perception and binaural processing recruit different neural machinery — parallels how auditory nerve fibers carry place vs. timing cues in the physiology literature.
Binaural brainstem responses are disrupted by large electrode place mismatches — Fits the stated reading goal on binaural cue processing physiology in normal-hearing and implanted listeners.
Binaural brainstem neurons compare only interaurally place-matched inputs. — Connects to the reader’s interest in why bilateral implant users underperform NH listeners and to binaural cue physiology in the quiet-hearing literature.
Electrically evoked brainstem responses are larger and faster than acoustic ones — Grounds the physiology of electrical vs acoustic cochlear stimulation in a concrete latency differential — links to general neurophysiology of direct vs sensory nerve activation.
Binaural loudness summation is not a simple sum of the two ears. — Connects to Moore and Glasberg 1996a,b and 2007 loudness models, which also appear in one-eared hearing loss research.
Bone-conducted sound substantially contributes to hearing with an occluded ear. — Connects to the classic point that bone conduction lets us hear our own voice and heart; occlusion effect raises low-frequency bone-conduction output. Relevant to the reader’s physiology interest.
Binaural loudness may exhibit constancy across conditions — Bears on the general claim that binaural summation of loudness is partial rather than full, a theme in psychoacoustics that the reader would benefit from pinning down.
Bilateral hearing aids use reduced gain due to loudness summation — Directly connects to hearing-aid fitting protocols and the loudness models of Moore and Glasberg that form the theoretical backbone of modern fitting.
Binaural loudness summation levels depend strongly on how monaural presentation is achieved. — Relates to loudness summation literature and the physical vs. methodological basis of binaural loudness; connects to psychoacoustics texts on how earphone-coupling and occlusion alter measurements.
Occluding one ear in a sound field dramatically reduces binaural loudness summation — The speech-versus-noise difference implying ecological validity sits with the reader’s interest in how cognition shapes basic auditory perception.
Binaural loudness constancy depends on residual low-frequency cues and knowledge of ear occlusion — Connects sensory constancy phenomena (like visual size/color constancy) to auditory processing — the listener’s knowledge of occlusion overriding the physical summation signal.
A finger blocks ~30 dB but leaves low frequencies below 200 Hz unattenuated — Connects to occlusion attenuation data; a natural fact about how the ear plugs physically work.
Sounds presented to both ears are perceived louder than to one ear — Connects to the reader’s broader loudness-summation knowledge base; also relevant to hearing-aid fitting design tradeoffs.
Occluding one ear cuts predicted loudness there by 52 percent. — Connects to the reader’s broader concern with binaural loudness summation and Moore and Glasberg’s inhibitory loudness model vs. older summation models.
Directional source allocation, not binaural unmasking, explains spatial release — Ties SRM to informational masking / source confusion rather than energetic binaural unmasking — connects to the reader’s broader interest in how the auditory system allocates streams.
Binaural unmasking cannot explain spatial release from masking in multispeaker situations — Contrasts the classic BMLD (binaural masking level difference) literature with the modern cocktail-party spatial release picture; the mechanism invoked here is source segregation/allocation via directional cues, not binaural detection advantage.
Only about 20 percent of time-frequency units carry audible target speech — Connects to gated-noise and sparse-coding literatures showing the redundancy of natural signals, plus parallels with how the visual system reconstructs from sparse features.
Spatial separation of talkers reduces ambiguity in assigning speech components. — Connects to classic binaural unmasking literature and spatial hearing models of auditory scene analysis.
Spatially separated speech sources improve intelligibility through binaural release from masking. — Connects to the reader’s general knowledge of psychoacoustics and the binaural masking level difference literature.
Binaural unmasking depends on masker duration but not signal duration — Connects to psychoacoustic masking-level-difference literature (Hirsh 1948) and to spatial release from masking.
Binaural unmasking cannot explain spatial release from masking in multispeaker situations — Contradicts the classic Levitt-Rabiner (1967) prediction and most current binaural intelligibility models that assume within-band SNR improvement; this echoes prior reading about how binaural advantage models are built on tone-detection results.
Binaural unmasking cannot explain spatial release from masking in multispeaker situations — Sets up the spatial-release-from-masking mechanisms central to cocktail-party hearing research; ties to how the acoustic environment shapes binaural unmasking.
Spatial release from masking stays constant across all SNR criteria tested — Connects to binaural processing and spatial release from masking discussion elsewhere in the book and general psychoacoustics of hearing-in-noise.
Hearing-impaired speech deficits stem from using glimpsed information, not spatial processing — Connects to the broader glimpsing / informational-masking literature and the ideal binary mask technique.
Hearing aid gain is capped by loudness discomfort before full audibility restoration is achieved. — Connects to the loudness recruitment / dynamic range compression literature in hearing aid fitting — limited usable gain constrains audibility restoration.
Hearing-impaired speech deficits stem from using glimpsed information, not spatial processing — Connects to degraded auditory filtering and glimpsing literature; the claim that masking release failures in HI listeners reflect audibility rather than binaural processing parallels broader themes about bottom-up versus top-down hearing deficits.
Speech perception depends on brief time-frequency glimpses where target energy exceeds masker energy. — Connects to ‘glimpsing model’ of Cooke 2006 and ideal binary mask ideas from ideal time-frequency segregation methods.
Mono mixed spatial-task evidence contradicts the assumption that hearing loss disrupts spatial processing globally — Echoes the general pattern in this reading list where apparent deficits attributed to a mechanism are sometimes confounded by a more mundane cause (here audibility).
Reduced target audibility, not spatial processing loss, explains much of the hearing-loss speech deficit — Connects to the broader picture that cognitive factors (working memory, attention) influence speech-in-noise perception independent of audibility, matching reading interests in cognition alongside psychoacoustics.
Reduced target audibility, not spatial processing loss, explains much of the hearing-loss speech deficit — Connects to cocktail-party-effect and informational-vs-energetic masking literature in psychoacoustics.
Standard hearing-aid gain prescriptions may under-amplify high frequencies at low presentation levels. — Relevant to how measured ‘hearing-loss deficits’ may partly be amplification artifacts rather than true neural/spatial problems.
Hearing-impaired speech deficits stem from using glimpsed information, not spatial processing — Plausibly connects to literature on how hearing loss impairs informational masking and top-down restoration beyond simple audibility loss.
Hearing-impaired listeners’ poor spatial hearing may not reflect actual spatial-processing deficits. — The r=0.92 correlation between natural and glimpsed performance connects to broader models of speech-in-noise as limited by glimpsing/cue access rather than perceptual grouping.
Spatial release from masking deficits often reduce to an audibility problem — Directly relevant to the reader’s overall book purpose on normal hearing physiology; the residual ~2.5 dB SRM gap that audibility cannot explain points to suprathreshold/binaural processing deficits beyond audiogram shape.
Most of the SRM deficit in hearing-impaired listeners stems from reduced audibility — Connects to the running theme that audibility is only part of what drives spatial release from masking; overlaps with psychoacoustic work on binaural processing and energetic vs informational masking.
Spectral contrast degradation in CIs impairs formant detection for speech — Connects to the listener’s prior reads on soundscape and sonic physiology — spectral/ripple resolution as a quantifiable hearing dimension.
Spectral resolution closely predicts vowel and consonant recognition in CI users — This causal link between a physiological/psychophysical resolution measure and speech outcome is central to the reader’s interest in how hearing measures predict cognition.
MPI vowel-identification models add Gaussian jnd noise scaled from SMT thresholds — Internal/neural noise as the bridge from physiology to perception echoes the reader’s interest in hearing mechanisms.
Three-down-one-up adaptive tracking efficiently estimates perceptual thresholds — Relates to Levitt’s classic adaptive staircase method — a methodological core for psychoacoustic threshold estimation the reader studies.
Spectral contrast degradation in CIs impairs formant detection for speech — Connects to the reader’s noise/silence work on how degraded spectral information shapes perception in impaired vs normal hearing.
Computer models of vowel perception can predict cochlear implant user outcomes — Connects to the reader’s interest in psychoacoustic models of vowel perception; the Sagi/Svirsky model of vowel identification is the kind of computational surrogate the reader would want to understand.
Individual electrode-nerve interfaces cause the wide variability in CI outcomes — Connects to the physiological grounding of how electrode placement, nerve survival, and current spread differ across users — the individual physiological interface as root cause of outcome variability.
Over-attenuating spectral valleys may remove speech information CI users need — Connects to reader’s interest in how spectral cues (formant peaks AND valleys) encode speech information cognitively.
CIE electric-field spread flattens spectral contrast, obscuring formants needed for speech — Directly relevant to reader’s goal of understanding physiology-perception links: electrode spread reduces the neural representation of spectral peaks and valleys.
Increasing spectral contrast enhancement worsens vowel identification in CI users — Connects to the reader’s work on spectral envelope perception and the distinction between peaks and valleys in speech — likely echoes material in van Dijk chapters on spectral integration and modulation detection limits.
Cochlear implant spectral smearing flattens formant peaks needed for speech recognition — The degradation of peaks-through-broad-neural-activation mechanism parallels noise’s masking of signal peaks in the reader’s ear-science reading.
Spectral ripple test measures spectral modulation detection thresholds in cochlear implant users — Connects to the reader’s earlier interest in psychoacoustic test methods and signal-processing techniques for evaluating spectral resolution in hearing.
Stronger contralateral sound causes greater efferent inhibition and more linear cochlear response — Connects to models of cochlear compression and efferent feedback in normal hearing physiology.
The contralateral efferent reflex strengthens gradually over tens of milliseconds — The slow build-up and positive-feedback loop resembles the reader’s broader knowledge of efferent reflex time constants and suggests temporal dynamics matter for real-world sound processing.
The MOCR may act as a cocktail-party processor via head-shadow-based gain control — The ‘cocktail party processor’ hypothesis connects directly with the reader’s interest in speech-in-noise cognition.
Cochlear implant compression mimics cochlear mechanics and can test efferent function — CI back-end compressor as functional substitute for cochlear mechanical compression parallels how models stand in for unavailable physiological variables.
The contralateral efferent reflex inhibits cochlear compression at matched frequencies — Connects to the broader MOCR/efferent-reflex literature (Hood 1996, Backus & Guinan 2006) already established in earlier chapters of the efferent system.
Contralateral efferent inhibition is proportional to output energy, magnifying inter-aural differences. — This output-based feedback asymmetry relates to the positive-boost effect described in the soundscape literature where perceived differences get amplified through feedback loops.
Deactivating indiscriminable implant electrodes fails to improve and even worsens perception — Connects to my reading on silence and signal processing in The Soundscape — the idea that removing redundant channels degrades rather than cleans the percept, an analogy to over-filtering soundscapes.
Acoustic-tone and direct-pulse pitch ranking correlate strongly — Connects to the broader question of whether acoustic simulation of cochlear-implant processing faithfully predicts direct electrical stimulation outcomes — relevant to the psychoacoustics of place-pitch coding.
Deactivating cochlear implant electrodes based on pitch ranking worsens spectral resolution without improving speech. — Connects to the reader’s broader interest in cochlear implant fitting strategies and pitch perception physiology.
Only half of cochlear implant users benefit from electrode deactivation, and many worsen. — Concrete empirical data useful for the reader’s physiology/psychoacoustics background on implant signal processing.
Deactivating multiple electrodes can worsen rather than improve information precision. — Relates to the reader’s interest in spectral-to-place mapping distortions in hearing.
Cochlear implant re-mapping deactivates poorly placed electrodes to improve spectral resolution. — Connects to the reader’s broader hearing physiology and implant signal-processing interests.
Deactivating an electrode shifts frequency-to-electrode allocation and can negate expected benefits. — Connects to the question of why channel-deactivation benefit varies across devices and studies; echoes the 2057 finding that Cochlear users benefited least.
CT-guided CI electrode deactivation benefits only half of recipients — Relates to the reader’s knowledge of cochlear implant fitting and spectral resolution.
Deactivating cochlear implant electrodes harms spectral ripple resolution but spares speech perception — Connects to the general theme that speech understanding in implants survives degraded spectral detail while fine spectral/temporal tasks suffer — a contrast relevant to psychoacoustic reading on spectral resolution vs. speech intelligibility.
Pitch discrimination passes are set by binomial significance thresholds on a fixed trial count. — The 8-of-10 binomial criterion pattern echoes the classic ABX and forced-choice discrimination criteria found across psychoacoustic methods literature.
Broader cochlear filtering reduces low-frequency noise fluctuations, explaining lost masking release — Explains the loss of masking release — connects to the psychoacoustics of comodulation masking release in normal vs impaired cochleae.
The speech-masking potency of inherent noise fluctuations depends on listener frequency selectivity and level — Connects to the broader question of why cochlear implant and hearing-impaired users get less benefit from fluctuating maskers (informational masking release) — parallels the earlier chapter theme on temporal fine structure loss.
Inherent noise fluctuations do not universally limit speech perception across hearing conditions — Connects to the general theme that what degrades hearing in noise is temporal masking/interaction rather than raw energy — parallels work on simultaneous masking and modulation.
Broader cochlear filtering reduces low-frequency noise fluctuations, explaining lost masking release — Connects to the reader’s ongoing focus on masking release and amplitude-modulation listening benefits in impaired ears — central to psychoacoustics of hearing loss.
Raising speech to equal 85 dB SPL reduces normal listeners’ masking release benefit — Relevant to loudness recruitment literature — fixed high SPL presentation reduces the effective dynamic range and thus dip-listening opportunities in NH ears.
Both spectral sparsity and lack of fluctuations in a masker release speech from masking — Connects to the broader literature on release from masking via dip-listening and comodulation masking release in fluctuating noise.
Cathodic pulses produce larger cochlear implant responses than anodic pulses across pulse shapes — Connects to the reader’s interest in pulse-shape polarity effects in cochlear implant stimulation; also relevant to neural response polarity sensitivity literature in auditory physiology.
At suprathreshold levels louder electrical pulses can sometimes evoke smaller neural responses — Reverses the intuitive monotonic input-output assumption; relates to earlier reading on compression and nonlinear gain in the auditory system.
Cathodic pulses are more efficient in animals but anodic pulses in humans — Connects to the reader’s neuroscience-of-hearing corpus on electrode-neuron interaction and polarity mechanisms, and the general puzzle of species differences in neural stimulation.
Nerve degeneration or central blocking may explain anodic pulse dominance — Ties into the reader’s broader theme of how neural degradation alters stimulation responses, echoing deafness-and-neural-plasticity threads elsewhere in the library.
Charge-balanced asymmetric waveforms reduce the current needed to elicit hearing in cochlear implant users. — Links to the broader question of how stimulus waveform design interacts with physiological safety limits in human implantation.
Guinea pig cochlear implant sensitivity is measured via inferior colliculus evoked potentials — The use of inferior colliculus evoked potentials as a readout of cochlear implant activation connects to the broader auditory brainstem pathway literature in psychoacoustics.
Neural degeneration cannot explain why anodic stimulation suits humans but cathodic suits animals — Links to general cortical-adaptation-after-deafness literature and to cochlear-implant physiology in the reader’s hearing-science corpus.
Cathodic pulses evoke larger inferior colliculus responses than anodic pulses — Directly contradicts the human suprathreshold anodic-sensitivity literature reviewed in the introduction, a species contrast relevant to understanding cochlear physiology.
Neural degeneration alone does not explain polarity sensitivity differences in cochlear implants — Cochlear implant electrophysiology contrasts animal cathodic preference with human anodic preference — connects to broader question of species differences in neural excitation.
Anodic stimulation is more effective in human implant users but less effective in animals — Connects to the reader’s core interest in mechanisms of electrical vs acoustic hearing; a species-difference in polarity sensitivity that animal models fail to reproduce is methodologically striking.
Cathodic pulses produce larger cochlear implant responses than anodic pulses across pulse shapes — Connects to central-auditory/psychoacoustic understanding of electrode-nerve interface in the reader’s hearing library.
Neural degeneration shifts eCAP firing-component scaling and latency in measurable ways. — Connects to the broader cochlear-implant and auditory-nerve physiology work in this volume, and to the mechanism linking neural health to eCAP shape.
A compound action potential equals single-fibre waveform convolved with spike latency distribution — Convolution model generalizes beyond hearing — same framework underlies population spikes in other sensory systems and even neural population coding.
Auditory nerve degeneration shortens spike latency and enlarges the second discharge peak — Connects to cochlear-implant neural survival literature and to the wider theme of spiral ganglion degeneration in sensorineural hearing loss.
Deconvolving the eCAP reveals two separate auditory nerve firing components. — Connects to compound action potential biology and cochlear implant electrophysiology beyond van Dijk’s perceptual focus.
Human cochlear-implant eCAP studies use loudness-based limits unlike animal models — The contrast between animal and human stimulation protocols echoes a recurring theme of translating physiology across species used in hearing research chapters.
The nerve’s two latency components reflect neurons firing twice, not two fiber populations — Relates to the recovery/masker-probe response measures also discussed in the Frankfurt School materials on physiological measurement methods, though thematically to auditory physiology more broadly.
Hearing loss can compress loudness range while keeping sharp categories — Shows that reduced dynamic range and loss of perceptual resolution (sharpness) are separable dimensions — relevant to physiological accounts of loudness recruitment in cochlear damage.
Two principal components capture most loudness-category variability — PCA dimensionality reduction of perceptual parameters mirrors similar eigen-decompositions across the hearing/psychoacoustics literature; connects to discussion of individual variability and its low-dimensional structure.
Loudness judgments at a fixed level vary probabilistically across categories — Connects directly to this book’s core theme of individual variability in hearing and renders loudness as a distribution rather than a single point — analogous to signal-detection theory’s treatment of thresholds as probabilistic.
Standard loudness analysis discards response variability that probabilistic models can exploit — Connects to the role of institutions / information-literature theme that averaging discards distributable information.
Loudness judgments at a fixed level vary probabilistically across categories — Connects to the reader’s interest in cognition/perception—the trial-to-trial variability of loudness judgment is a perceptual-cognitive mechanism, not mere measurement noise.
Maximum-likelihood estimation can predict categorical loudness functions from only 15-30 trials — Connects to adaptive-level clinical testing (e.g., Bekesy tracking, categorical loudness scaling used in hearing-aid fitting), a known clinical psychoacoustic instrument.
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Connects to the soundscape/loudness-perception theme, and to hearing-impaired loudness recruitment as a disruptive case.
Loudness recruitment in hearing loss could be probed via Heschl’s gyrus fMRI — Pairs with the loudness-recruitment mechanisms discussed in impaired-hearing literature.
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Connects to the loudness-vs-intensity distinction relevant to the reader’s interest in physiology, psychoacoustics, and cognition.
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — This is the core fMRI finding contrasting coding of loudness vs. sound pressure level — connects naturally to psychoacoustic discussions of loudness growth and compression in hearing-impaired listeners.
Loudness estimates explain cortical responses better than sound intensity alone — Connects to the loudness-vs-intensity distinction familiar from psychoacoustic treatments (e.g. Fletcher-Munson curves) and to theories that neural responses track perceived magnitude.
Cortical BOLD responses track perceived loudness linearly but show steepening at high levels — The nonlinear steepening at high sound levels echoes the compressive cochlear nonlinearity and basilar-membrane gain findings from earlier hearing physiology chapters.
Perceived loudness grows nearly linearly with intensity until steepening near 90 dB SPL — Connects to psychoacoustic loudness scaling literature and the binaural summation effect from earlier chapters of this volume.
Perceived loudness grows nearly linearly with intensity until steepening near 90 dB SPL — Connects to classic psychoacoustic growth-of-loudness literature (Stevens’ power law, loudness scaling) and to the ‘pedestal’ limits speech perception concepts.
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Links to loudness recruitment and hearing loss mechanisms in the reader’s auditory physiology knowledge; cortical linear-loudness coding contrasts with peripheral compressive nonlinearity.
Contralateral ear dominance weakens higher in the auditory pathway — Connects to the reader’s interest in physiology-to-perception mechanisms and cross-dominance patterns like ocular dominance.
Loudness estimates explain cortical responses better than sound intensity alone — The steep BOLD rise at high levels mirrors the steeper slope of the psychophysical loudness curve above ~90 dB SPL reported at location 2626 — a physiology-perception parallel.
Tinnitus reduces default mode network activity by blocking mind-wandering — Connects to the reader’s interest in auditory pathways and how sustained internally-generated percepts reshape large-scale brain network dynamics; also relates to broader default mode network literature on self-referential processing.
Tinnitus patients show reduced default mode network connectivity and a blunted task effect. — Connects to widely-reported resting-state fMRI findings that the DMN is altered in chronic conditions — auditory, chronic pain, and depression literature all report DMN hypoconnectivity.
Tinnitus patients show reduced functional connectivity in the precuneus region — Connects to default-mode network literature and attentional/cognitive processing in hearing disorders from other chapters on auditory cognition.
Tinnitus reduces default mode network activity by blocking mind-wandering — Connects to the reader’s broader interest in the cognition of normal hearing: DMN suppression as a general pathway by which constant internal or external noise displaces introspective states.
The default mode network may mediate perception of internally generated sound. — Unites meaningful auditory hallucinations in schizophrenia with meaningless tinnitus through a shared DMN mechanism — a broad claim spanning clinical neuroscience.
Tinnitus patients show reduced default mode network connectivity and a blunted task effect. — The claim that ongoing percepts rob the brain of needed rest links to the reader’s prior interest in chronic noise robbing quiet reflection.
Tinnitus reduces default mode network activity by blocking mind-wandering — Directly relevant to the reader’s deep-dive on hearing physiology and cognition; the chronic ‘no rest’ framing ties to broader attention-fatigue and DMN literature.
The default mode network may mediate perception of internally generated sound. — Connects to the reader’s tinnitus and resting-state brain network knowledge; also echoes work linking DMN to psychiatric phantom perception and to the DMN discussion in the wider hearing-research literature.
Pooling data across groups in one ICA improves component estimation reliability — Relates to the reader’s knowledge of neuroimaging methodology and dual regression techniques.
Tinnitus reduces default mode network activity by blocking mind-wandering — Connects to the default mode network literature and to the broad noise/tinnitus theme; echoes findings on internal vs external attention tradeoffs.
Resting-state fMRI reveals connectivity differences in numerous brain regions in tinnitus. — Connects to the reader’s prior tinnitus-and-auditory-cortex material; cross-references how pathology shows up as network-level rather than purely tonal phenomena.
MRI scanning noise can contaminate tinnitus brain-imaging results — Relates to the field of acoustically-evoked fMRI correction and the broader problem of scanner noise biasing auditory neuroimaging.
Tinnitus patients show reduced default mode network connectivity and a blunted task effect. — Plausibly connects to the ongoing chapters on attentional networks and how tinnitus may hijack central auditory gain.
Tinnitus alters memory, attention, and emotion networks, with unknown causality — Relates to the resting-state default mode and network literature in cognitive neuroscience beyond tinnitus.
Barn owl ITD maps depend on phase matching rather than absolute conduction latency — The ‘coarse delay during development, fine tuning modulo 2π’ two-step mechanism parallels later discussion of Hebbian/STDP learning rules in the same chapter.
Barn owl ITD maps depend on phase matching rather than absolute conduction latency — Connects to Jeffress delay-line theory of binaural coincidence detection, the classic account of ITD encoding.
Systematic conduction delay changes along the nucleus laminaris axis encode interaural time difference maps — Connects to Jeffress coincidence-detector models and the classic ITD mapping mechanism in avian auditory brainstem.
Phase delay scatters narrowly while signal-front delay scatters widely in the nucleus laminaris — Connects to how coincidence-detector models distinguish phase locking from signal-front latency — relevant to interaural time difference computation in both avian and mammalian brainstems.
Barn owl delay-line input geometry creates an orderly ITD map in nucleus laminaris — Classic Jeffress model of coincidence detection and delay lines, which this geometry instantiates.
ITD sensitivity depends on phase differences, not absolute conduction latencies — Ties to my death-and-dignity reading theme only loosely, but strongly connects to the chapter’s stated purpose on physiology and psychoacoustics of normal hearing — the surprise that phase, not latency, does the work is the pay-off of the whole delay-line story.
Phase-modulo-2π coding predicts mammalian latency variability as well as owl data — Bridges the owl’s phase-matching mechanism to mammalian auditory-nerve and cochlear-nucleus latency variability — a cross-species generalization the reader’s physiology interest would value.
MSO and LSO neurons have opposite optimal interaural phase responses — Connects to classic auditory neuroscience knowledge of the MSO coincidence detector (Jeffress model) and LSO as IE neuron.
Abrupt sound changes elicit auditory P1-N1-P2 waves at 50, 100 and 175 ms — These P1-N1-P2 latencies tie to auditory evoked potential physiology relevant to the reader’s cognition work.
Neural ITD encoding follows distinct temporal-fine-structure vs envelope mechanisms at low vs high frequencies. — Ties to classical duplex theory of sound localization — ITDs in fine structure for low frequencies, envelope for high frequencies.
MSO and LSO neurons have opposite optimal interaural phase responses — Connects to Grothe et al. sound-localization mechanisms discussed in other physiology sources.
Humans can detect interaural timing differences as small as ten to twenty microseconds — The ‘cocktail party’ framing connects to broader psychoacoustics of auditory scene analysis and Bregman’s work.
EEG significance testing targets the IPD cycle rate, its harmonic, and the AM rate — Connects to standard EEG steady-state response analysis design — anticipating which neural response bins should be tagged follows from the stimulus’s known periodicity.
Normal-hearing MAA is unaffected by pinna cue type or reference angle — NH subjects discriminate equally well with natural, transposed, and Weinrich cues — suggests normal-hearing flexibility in cue use that HI listeners lack.
Level differences at typical MAAs fall below the 1-2 dB just-noticeable difference — Level-cue magnitude below JND isolates frequency-shaping as the effective discrimination cue — connects to level discrimination acuity data in Moore’s Psychology of Hearing.
Hearing-loss compensation boosts stimulus level 15 dB above threshold per band — The 15 dB-above-threshold band compensation strategy connects to audibility-first principles in hearing-aid fitting (NAL/prescriptive gain guidelines).
Adaptive staircase tracks the 70.7 percent correct point on the psychometric function — Parallels the Milgrom/Levitt 1971 transformed up-down method used in audio psychophysics.
Enhanced pinna cues only partially close the spatial-resolution gap in hearing-impaired listeners — Connects to prior discussion of hearing-aid frequency range and impaired spectral resolution limits.
Reduced frequency selectivity hides fine spectral pinna cues above 5 kHz from impaired listeners — Connects to psychoacoustic literature on reduced frequency selectivity in cochlear hearing loss and how it degrades monaural spectral cues for localization.
Median-based MAA thresholds differ from Levitt-style staircase estimates — Methodology caution connects to the Milgrom law-merchant excerpts on measurement/institution choices depending on what question the study actually answers.
Oversized-pinna cue simulation barely improves vertical localization for hearing-impaired listeners — Connects to the broader question of whether hearing-aid frequency-lowering schemes genuinely restore speech/auditory cues or just remap them.
Pinna frequency-shaping cues, not level cues, drive sound localization at typical angular thresholds — Connects to the broader Pinna-related localization literature and to the reader’s interest in psychoacoustic mechanisms underlying spatial hearing.
Exaggerated outer-ear cues improve sound localization only in hearing-impaired listeners. — Connects to the broader hearing-aid literature on whether altered pinna cues aid spatial release from masking and localization in impaired ears.
Listeners re-adapt to modified pinna cues within about six weeks — Adult neural plasticity window for re-learning spectral-spatial mapping, paralleling similar recalibration found in visual prism-adaptation studies.
Auditory motion direction detection requires much larger angular sweeps than static sound localization does. — Connects to the reader’s interest in what neural circuits distinguish — static (stationary) sound location vs. motion direction processing appear to be dissociable systems in the same brain.
The barn owl brain over-represents frontal space with overlapping maps and sharper neural fields. — Relevant to the reader’s goal of understanding how neural population size and receptive-field width determine perceptual acuity — a direct physiological mechanism for the frontal-space behavioral advantage.
Neural adaptation shifts spatial receptive fields to encode auditory motion direction. — Adaptation as a motion-direction mechanism connects to the reader’s interest in cognition/psychoacoustics — a low-level physiological basis for a perceived dynamic property.
Inhibition shapes the barn owl’s binaural temporal window for sound localization — Relates to the interaural-time-difference processing mechanisms discussed elsewhere in this book on binaural hearing in owls.
Barn owls distinguish sound motion only across spans far exceeding their static localization acuity — The motion-amplitude/static-acuity gap parallels human minimum audible movement angle studies (Middlebrooks and Green review), a cross-species constant worth tracking.
Angular range, not velocity, dominates barn owl auditory motion discrimination sensitivity — Connects to the wider psychophysical principle of integration time windows in auditory processing, similar to how human minimum audible movement angle depends on stimulus duration.
Motion perception improves with larger sweep angles more than with faster sweeps — Could connect to the reader’s own parallels between owl and human motion perception — motion-extent vs velocity tradeoffs in human auditory motion studies.
Angular range, not velocity, dominates barn owl auditory motion discrimination sensitivity — Contrasts with the human psychoacoustic literature where velocity and duration matter for minimum audible movement angle.
Barn owls do not decode sound motion by comparing start and end points — Directly parallels the snapshot-model rejection in human minimum audible movement angle literature, suggesting cross-species convergence in motion-hearing mechanics.
Overlapping bilateral neural representation and confined receptive fields make frontal audio localization most accurate — Resonates with psychophysical work showing humans locate frontal/azimuth sources more precisely than lateral ones — a comparative angle across species.
Auditory motion direction detection requires much larger angular sweeps than static sound localization does. — Connects to the reader’s broader interest in human psychoacoustics: the same snapshot-hypothesis dismissal in human MAM studies links owl finding to human auditory motion processing literature (Middlebrooks and Green).
Inhibition shapes the barn owl’s binaural temporal window for sound localization — The inhibition-sharpens-temporal-window mechanism parallels the general binaural temporal window concept relevant to human auditory temporal processing.
Auditory motion-direction neurons in barn owls tolerate very broad velocity ranges — Consistent with the reader’s existing knowledge on inhibition and motion-direction selectivity mechanisms across species (e.g., visual MT/MST motion tuning), suggesting a possible cross-modal commonality.
Natural sounds like wind and rain are recognized through their statistical patterns — Connects to soundscape ecology work on keynotes and ambient sound, and to the deep-listening idea that textures are heard in aggregate.
A minimal tone cloud can isolate statistical predictability as the sole cue — Connects to Milgrom-style methodology thinking and to psychophysics work isolating a single minimal cue to probe a perceptual mechanism.
Exponential change-time distributions prevent timing strategies because hazard rate is constant — A memoryless (exponential) distribution means no hazard-rate peak, so subjects cannot predict likely change moments; useful design insight applicable to any detection experiment design.
Profile analysis compares static spectra while texture change detection needs dynamic statistics — Ties to classic profile-analysis literature (Green and Berg) familiar from psychoacoustics.
Auditory change detection integrates statistical evidence accumulated over time — Parallels sequential sampling and drift-diffusion models in decision making; also bounded accumulation models from cognitive psychology.
Time-dependent signal detection measures are needed when trial outcomes arrive dynamically — The methodological problem of right-censored outcomes resembles survival analysis and event-history statistics from other domains.
Dynamic spectral-comparison tasks reveal integration processes static tests miss — Contrasts with classic profile analysis paradigm (Green and Berg 1991), which the reader may know from psychoacoustics literature.
Some listeners detect texture changes more readily when not actively attending — Connects to the pre-attentive deviance-detection literature (mismatch negativity) and to research on passive vs. active listening in auditory scene analysis.
Change detection in statistical sounds relies on predicting statistics rather than exact occurrences — Directly addresses the reader’s stated interest in how normal hearing cognition handles uncertain, changeful acoustic scenes.
Adding sounds is perceptually more salient than removing them in complex stimuli — Connects to the IMMODESTY article’s theme of asymmetry in perception of presence vs absence, and generally to gain-versus-loss salience asymmetries in perception.
Texture-change experiments kept total tone counts constant by compensating other bins — Plausibly relevant to controlling for intensity/informational masking in the change-detection task.
Roving sound level prevents listeners from using absolute intensity as a cue — Connects to known psychophysical control procedures; matches discussion in the leveled and roved-stimulus literature.
Auditory textures are recognized through their spectrotemporal statistics despite highly variable local structure — Links to the concept of soundscape keynotes and how the ear extracts statistical regularities from ambient sound.
Natural sounds are identified by their statistical properties, not by predictable patterns of elements — Links to the reader’s ongoing interest in how the auditory system abstracts statistical regularities, complementary to louder soundscape material from The Soundscape.
Added sound components are more detectable than removed ones in complex textures — Relates to the general principle that onsets and additions capture attention more than removals, paralleling visual change blindness literature where additions are also more salient.
Signal detection metrics can be computed as time-varying functions of stimulus onset — Relates to signal detection theory (SDT) treats sensitivity and bias independently; time-varying extension differs from the usual single-value d’ in detection experiments.
Impaired temporal resolution degrades fine-structure pitch processing more than reduced frequency selectivity does — Connects to the core theme of this book collection: the envelope/fine-structure dichotomy (Smith et al. auditory chimeras), and to the reader’s interest in how degraded timing vs. spectral cues dissociate in hearing loss.
Auditory neuropathy subjects preserved normal cochlear frequency resolution despite temporal deficits — Connects to the physiology of auditory neuropathy: frequency resolution depends on cochlear mechanics (outer hair cells) while temporal envelope coding depends on neural synchrony — a dissociation also discussed in other chapters of this volume.
Sensory neural hearing loss preserves temporal resolution when audibility is compensated — Ties to the SNHL-vs-ANSD frequency/temporal resolution dissociation; reader’s interest in which peripheral damage types degrade which auditory cues.
ANSD impairs temporal processing while keeping cochlear amplification function intact — Connects to the broader research interest in how temporal-vs-spectral cues map onto perception — this chapter is a case study in cue dissociation.
Manipulating envelope and fine structure separately reveals their competing roles in tone perception. — Auditory chimera technique connects to earlier discussions of envelope/fine-structure separation in the book’s psychoacoustics chapters.
Fine-structure cues carry Mandarin tone perception, not envelope cues — Connects to the reader’s broader physiological/psychoacoustic understanding of how cochlear damage degrades fine-structure coding while envelope coding partially survives.
ANSD patients have triple the temporal gap detection thresholds of normal listeners — The near-complementary dissociation — SNHL poor frequency/temporal normal, ANSD temporal poor/frequency normal — is a clean double dissociation pattern; echoes similar double dissociations in other sensory systems in my notes.
Auditory chimeras swap spectral envelopes between signals to isolate cue contributions — Ties to the Philip Smith 2002 Nature chimeric sounds work this method derives from.
Auditory neuropathy elevates gap detection thresholds threefold above normal — Directly extends the cochlear-normal-but-neural-impaired dissociation from passage 3399: together they confirm that ANSD selectively spares frequency resolution while degrading temporal processing.
Binaural masking level differences reach 25 dB only for overlapping masker and target — Connects directly to the reader’s work on binaural processing and psychoacoustics across multiple books in this library.
Energy-based models of modulation filterbanks fail to predict interferer effects on detection — Relates to the modulation filterbank literature on comodulation masking release (CMR) by Piechowiak et al.
Off-frequency tone detection benefits from envelope modulation cues produced by beating — Ties to modulation filterbank models and envelope-processing work (Dau et al. 1997; Nitschmann and Verhey 2012) the reader is building a mental model of.
Binaural masking level differences reach 25 dB only for overlapping masker and target — Connects to how off-frequency targets lose binaural masking release, relevant to spatial hearing and cocktail party segregation.
Binaural modulation frequency selectivity is coarser than the monaural version — Connects to the wider binaural-vs-monaural processing literature and the reader’s interest in cognition underlying hearing.
Binaural masking level differences reach 25 dB only for overlapping masker and target — Connects to ongoing reading on binaural processing and monaural-vs-binaural cue separation in hearing physiology.
The SII model’s assumed SNR range fails for sharply filtered speech signals — Connects to general psychoacoustic models of speech intelligibility and audibility weighting functions; also relevant to hearing-aid design constraints.
Low-pass filtering a target worsens speech reception linearly until a floor. — The asymmetry between HP and LP target filtering (floor in LP but not HP) relates to known results about which speech cues carry intelligibility — links to classic articulation-index work.
Manipulating the masker’s spectrum affects speech intelligibility more than manipulating the target’s spectrum. — Connects to masking release / informational versus energetic masking literature, and to how hearing-impaired listeners’ SRTs behave under fluctuating noise.
Speech intelligibility models must clamp SNR to realistic floor and ceiling limits — Connects to chapter on the Speech Intelligibility Index and signal-to-noise ratio concepts from the book’s later chapters on speech perception.
Improving speech intelligibility via noise reduction outweighs equivalent improvement of the target signal. — Chimes with general perceptual asymmetry literature (loss aversion and signal/noise asymmetry) and SII critique tradition.
Masker attenuation improves intelligibility more than equal target amplification — Relates to positive-versus-negative SNR weighting — ties to the reader’s earlier finding that subjective loudness perception is nonlinear.
Very high speech levels can reduce word recognition scores. — Connects to loudness discomfort and non-monotonic audibility curves discussed in hearing-aid fitting literature.
Frequency importance varies nonlinearly across signal-to-noise ratios — Links to the reader’s core interest in frequency-band physiology: importance functions differ from SII’s flat weighting across SNRs.
Speech intelligibility models must clamp SNR to realistic floor and ceiling limits — Connects to how psychoacoustic coding models assume bounded neural response ranges and to signal detection limits generally.
Cochlear compression operates via a gain control with finite attack and release times. — Connects to the broad literature on cochlear amplifier dynamics and AGC (automatic gain control) in signal processing.
Cochlear compression shows asymmetric envelope at high beat rates. — Connects to attack-versus-decay asymmetry in standard AGC/compressor circuitry familiar from audio engineering.
Rapid intensity changes linearize the basilar membrane’s compressive response. — Connects to questions of how the ear preserves fast temporal envelope cues (consonants, transients) despite compression.
Basilar membrane linearization trades sensitivity for enhanced coding of fast-changing stimuli. — Echoes the general sensitivity-versus-fidelity tradeoff common to adaptive gain systems in sensing.
Cochlear compression delay implies automatic gain control circuitry — Connects to the reading purpose on physiology and psychoacoustics, and to how compression underlies loudness perception and OAE-based clinical tests.
Cochlear gain control tracks intensity changes only up to about 1 kHz — Connects to the reader’s existing interest in how fast the inner-hair-cell / OHC electromotility cascade can actually respond — relevant to whether timing precision limits in normal hearing are mechanical in origin.
Low-frequency sound’s after-effects arise from calcium homeostasis in outer hair cells — Connects to otoacoustic emission literature and cochlear amplifier research; also relevant to noise-exposure and hearing-safety standards.
The oscillating Bounce is metabolic, not damage-linked, unlike ordinary acoustic overexposure — Contrasts with the monotonic-recovery picture in the acoustic-overexposure literature already in the knowledge base.
Free-field low-frequency sound causes SOAE bouncing without ear canal occlusion — Extends the noise literature: everyday anthropic LF sources perturb the cochlea, not just headphones.
Cochlear amplifier gain oscillates via underdamped OHC calcium cycling — Connects to the cochlear amplifier concept and OHC electromotility literature from earlier chapters on inner-ear mechanics.
Low-frequency sound modulates cochlear sensitivity through OHC calcium oscillations — Connects to the cochlear amplifier and OHC electromotility literature from inner-ear physiology generally.
Otoacoustic emissions non-invasively probe outer hair cell function in humans. — Relevant to the reader’s goal of understanding human hearing physiology — SOAEs are a practical measurement tool.
Low-frequency sound exposure triggers long-lasting damped oscillations in spontaneous otoacoustic emissions. — Connects to the reader’s wider knowledge of otoacoustic emissions and cochlear mechanics; the two-thirds-majority / one-eighth-inverted asymmetry is a striking empirical regularity worth cross-checking against cochlear model literature.
The oscillating Bounce cannot be explained by ordinary acoustic overexposure damage — Distinguishes Bounce from classic noise-induced temporary threshold shift, which recovers monotonically.
Contralateral acoustic stimulation suppresses otoacoustic emissions that recover within under a second — Connects to the broader otoacoustic-emission and efferent-suppression literature on medial olivocochlear reflexes and their effects on cochlear amplification.
Low-frequency after-effects on inner-ear gain are calcium-mediated and olivocochlear-modulable — Connects to the earlier concept of the Bounce following low-frequency sound (chapter on post-stimulatory effects) and to theories that OHC electromotility set-point homeostatically re-regulates after overstimulation.
Wiener-kernel analysis can separate excitatory from suppressive components in auditory nerve responses — Wiener-kernel/system-identification methods connect to the reader’s broader interest in psychoacoustic signal-processing methods and nonlinear system theory.
Noise-damaged cochleas keep low-side suppression normal while high-side thresholds rise — Connects to two-tone suppression physiology and otoacoustic emissions literature on outer-hair-cell function.
Significance of suppressive kernels is tested by bootstrap randomization conserving spike timing statistics. — Connects to the reader’s interest in systems-identification methods for sensory neural coding.
High- and low-side suppression rest on different cochlear mechanisms — Connects to the reader’s broader reading on how the cochlea’s nonlinear compressive amplifier (outer hair cells) is disrupted in hearing loss; both 2TS and DPOAE results in this chapter corroborate that story.
High-side two-tone suppression is lost while low-side suppression survives outer-hair-cell damage. — Connects to the reader’s broader interest in compressive nonlinearity and cochlear amplification mechanics.
Hearing loss reduces cochlear suppression and compressive nonlinearity together — DPOAE magnitude as a noninvasive gauge of compressive nonlinearity ties into the reader’s physiology-of-hearing focus.
Noise-induced hearing loss reduces neural suppression in proportion to tuning broadening — Links cochlear damage physiology directly to why hearing-aid multichannel compression struggles with speech in noise — connects to auditory amplification literature and psychoacoustic speech-in-noise deficits.
Noise-induced hearing loss reduces neural suppression in proportion to tuning broadening — The threshold-elevation-plus-tuning-broadening pattern is the classic signature that discriminates OHC from IHC damage in cochlear physiology texts.
Broadened frequency tuning correlates with loss of suppression in auditory nerve fibers — Connects to the reader’s ongoing thread on psychoacoustic tuning and cochlear damage from prior chapters.
Lost two-tone suppression degrades speech-formant coding in hearing-impaired listeners. — Links cochlear compression loss to speech perception, mirroring themes in the reader’s hearing physiology interests.
Signal degradation delays semantic integration and raises listening effort — Links to auditory effort / listening load research and the broader effortful-listening framework in cognitive hearing science.
Degraded speech delays semantic integration and blocks contextual relief of effort — Connects to the reader’s noise-library theme: degraded signals (noise, degradation) change cognitive processing timing, echoing how altered percepts alter downstream interpretation.
Degraded speech processing raises baseline cognitive effort independently of semantic context — Separates two independent sources of listening effort (perceptual degradation vs. lexical competition), a useful decomposition for the reader’s hearing-cognition framework.
Degraded speech delays lexical competition toward phonological competitors by about 300 ms — Pupil dilation as an index of listening effort connects to the reader’s broader psychophysiology of hearing work, and to Winn et al. 2015 on degraded speech effort.
Degraded speech delays semantic integration and blocks contextual relief of effort — Connects to the reader’s Deep Listening and One Square Inch of Silence work on listening effort and attention; also links to Winn et al. 2015 on effortful listening in degraded speech.
Semantic context can compensate for degraded acoustic speech signals — Relates to the broader ‘listening effort’ and top-down/bottom-up processing theme central to the reader’s stated purpose on cognition in hearing.
Baseline-normalized pupillometry underestimates effort from continuous degradation. — Connects to the reader’s noise/silence work on chronic exposure creating baseline adaptation that masks acute responses (e.g., keynote sounds noticed only when they change).
Whole-signal degradation blocks the top-down cues that help partial masking — Ties to the reader’s interest in the physiology and psychoacoustics of normal hearing vs. impaired hearing — full-signal vs partial degradation is a key distinction.
Envelope vocoders simulate cochlear implant processing by splitting sound into amplitude-modulated subbands — Connects to cochlear implant signal processing literature; the Greenwood frequency-position map on the basilar membrane underlies channel spacing.
Pupil dilation serves as a continuous measure of listening effort during speech comprehension — Links to pupillometry-as-cognitive-load literature (Kahneman); connects to the reader’s interest in effortful listening under degradation.
Pupil dilation serves as a continuous measure of listening effort during speech comprehension — Event-related pupil dilation as effort index connects to the broader cognitive-load and listening-effort literature (e.g., pupillometry in hearing-impaired speech perception studies).
A filterbank model can simulate hearing impairment in normal-hearing listeners — Connects to the reading purpose on normal hearing physiology — this is the methodology that isolates cochlear compression from other auditory factors. Also connects to my notes on psychometric function fitting and simulation approaches.
Auditory models can isolate single hearing-loss components that interact in real patients — Relevant to the reader’s understanding of hearing-impaired versus normal hearing perceptual differences; a model-based isolation approach echoing the reader’s interest in mechanisms.
Compression’s masking-growth effect extends from pure tones to broadband speech — Connects to Moore’s classical psychoacoustic finding on masking growth in compressive systems, generalizing a pure-tone result to realistic stimuli.
Masking grows slower in compressive than non-compressive auditory systems, generalizing to speech — Connects to the reader’s existing knowledge of forward masking and the Moore (2012) model of masking growth; ties peripheral compression to central masking within the reader’s physiology/psychoacoustics focus.
A hearing impairment simulator can isolate cochlear compression loss in normal-hearing listeners — Connects to the broader literature on hearing-loss simulation used in the author’s research programme on compression and perception.
Loss of cochlear compression most harms speech recognition of intensity-attenuated syllables — Directly relevant to the reader’s whole-book project of understanding hearing physiology and perception; the cochlear compression mechanism it’s isolating connects to other chapters on cochlear nonlinearity.
Presbycusis compounds multiple peripheral and cognitive deficits, hindering isolation — Names the three canonical presbycusis factors (loss of frequency selectivity, recruitment, loss of temporal fine structure) that recur throughout hearing-science literature.
Compression’s masking-growth effect extends from pure tones to broadband speech — Extends Moore’s classic finding on masking growth from sinusoidal stimuli to broadband, time-varying speech — the reader’s earlier note on masking growth in compressive vs non-compressive systems would connect here.
Temporal-structure sensitivity declines with age even when audiograms remain normal. — Extends the core hearing theme: age-related loss of temporal fine structure coding in otherwise normal audiograms, connecting to cochlear synaptopathy literature.
Better speech-in-noise ability correlated with worse brainstem encoding — Parallels the predictive-coding/estimation literature where skilled performers show less neural response to predictable input.
Objective hearing measures require linking perception, physiology, and speech ability. — Connects to the reader’s broader work on auditory perception paradigms bridging psychophysics and neurophysiology; also relevant to the ongoing challenge of objective audiometry in infants.
Temporal-structure sensitivity declines with age even when audiograms remain normal. — Relates to the classic literature on age-related hearing decline being driven partly by loss of TFS encoding (e.g., Brian Moore’s work) independent of audiometric thresholds.
Age-related hearing decline begins even with clinically normal pure-tone audiograms — Connects to the ‘hidden hearing loss’ / cochlear synaptopathy literature described by Liberman and Kujawa.
Better speech-in-noise ability correlated with worse brainstem encoding — Connects to perennial questions in the hearing literature about why EEG stimulus encoding and behavioural performance dissociate.
Aging impairs speech-in-noise ability beyond what hearing thresholds predict — Reinforces the growing recognition that the audiogram misses suprathreshold deficits — supports a recurring thread in hearing-science writing about hidden hearing loss.
TFS sensitivity predicts speech-in-noise ability among normally hearing adults — Extends the temporal-fine-structure story (Moore and Sek’s psychophysics) into a functional domain of speech understanding.
Deafness reroutes sensory processing so other modalities recruit auditory brain regions — Connects to broader neuroplasticity literature from earlier reading on how brains rewire.
Deaf CI users recruit the temporal voice area for visual lip-reading — Cross-modal takeover of auditory areas connects to the reader’s interest in how auditory deprivation reshapes cognition.
Resting-state brain activity reflects the brain’s ability to predict environment changes — Default-mode network concept connects to Raichle & Snyder’s work on baseline brain function.
Correlations between brain regions require removal of shared physiological noise. — The confound of shared systemic physiology inflating functional connectivity is central to resting-state fMRI method debates; connects to how any correlated-signal metric must contend with common-source artifacts.
Good CI speech understanding tracks left-hemisphere language network activation while poor understanding does not — Connects to the left-hemisphere dominance for language concept from aphasia literature, and suggests CI outcomes hinge on engaging the normal language network beyond primary auditory cortex.
Cochlear implant users with poor speech understanding show little left-hemisphere language activity. — Connects to hemispheric lateralization of language and how deprivation reshapes it — relates to the reader’s work on normal hearing physiology and cognition.
fNIRS measures brain blood flow via paired scalp light sources and detectors — The near-infrared blood-oxygen contrast mechanism parallels the reader’s earlier physiological reading on how perceptual systems signal.
Cross-modal brain plasticity from deafness variably helps or harms cochlear implant outcomes — Links to the broader debate on whether auditory deprivation plasticity is adaptive reorganization or maladaptive takeover, echoing compensation-versus-degradation debates in other sensory systems.
Auditory cortex in deaf brains gets recruited by visual stimuli, harming later implant success. — Cross-modal plasticity echoes the ‘use it or lose it’ deprivation-competition ideas in visual cortex research (e.g., congenital blindness recruiting visual cortex).
Language processing dominates left-hemisphere networks while the right hemisphere handles communication context. — Connects to the left/right hemisphere division of labor in language, a theme familiar from aphasiology (Broca/Wernicke) and neuroscience of speech.
Visual-cortex contribution to speech grows over time in implant users — Audio-visual integration as a learning mechanism parallels the McGurk-effect literature and multimodal speech perception research.
Neural activity raises oxygenated and lowers de-oxygenated blood locally, enabling optical imaging — Connects to fMRI BOLD-neurovascular coupling literature; both rely on the hemodynamic response following neural activity.
Resting brain activity during deafness predicts later success with a cochlear implant — Pre-implant cortical state predicting implant outcomes connects to the reader’s broader noise/silence library theme of perceptual systems being shaped before an intervention.
Neural activity drives opposite changes in oxygenated and deoxygenated hemoglobin — Relates to the reader’s broader interest in hearing physiology; the HbO/HbR hemodynamic contrast is the shared basis of fNIRS and fMRI BOLD imaging.
Human auditory processing is tuned for continuous speech, not isolated discrete sounds — Connects to the envelope-tracking literature (Lalor and Foxe, Ding and Simon) and to the broader theme that the brain prefers naturalistic continuous stimuli over repeated discrete tokens.
TRF models predict EEG from speech representations via cross-validated regression — Temporal response function approach is the standard method from Di Liberto’s speech-tracking work.
Priming can vary recognition of degraded speech while holding the stimulus fixed — Top-down predictive processing; relates to Hickok’s dual-stream feedback/control model cited here, and to the reader’s interest in cognition-driving-perception mechanisms.
Speech spectrograms are derived using cochlear-scaled band partitioning following Greenwood spacing — Greenwood’s equation implements cochlear place-frequency mapping — ties signal processing to physiological frequency resolution, central to the reader’s purpose.
The auditory system responds differently to continuous speech than to discrete sounds — Connects to research on envelope tracking and the controversial claims of the 2008 Lalor and ongoing work on EEG speech tracking.
Noise-vocoding strips speech intelligibility by replacing fine spectral detail with modulated noise — Connects to the classic Shannon et al. 1995 vocoding literature establishing that spectral detail is dispensable once temporal envelopes are preserved.
Pitch is coded by firing rate constancy, not spike phase — Connects to earlier material on phase-locking and temporal vs rate coding debates in auditory neuroscience.
Entracking neurons keep firing rate clamped to stimulus frequency across SPLs — The ‘no variability — exactly the same number of spikes’ claim invites comparison with spike-count variability statistics familiar from the reader’s broader neuroscience background.
Pitch is coded by firing rate constancy, not spike phase — Contrasts with autocorrelation/delay-line models of pitch such as Licklider’s, likely relevant across the reader’s pitch-perception literature.
Entracking neurons form plateau ‘buttes’ of uniform firing rate across a tonotopic profile — Counterpoint to rate-place/tonotopic coding described elsewhere in the reader’s pool on auditory nerve coding.
Entracking generates horizontally flattened rate profiles that encode pitch as firing rate — Plausibly connects to rate saturation flattening AN rate-place profiles (Kim and Molnar 1979) and to pitch-coding models contrasting place vs. temporal mechanisms.
Fine structure alone does not determine pitch differences in complex tones sharing envelope frequency — Connects to the established pitch-perception assumption that temporal fine structure and temporal envelope act independently, likely intersecting with any prior reading on the place vs. temporal theories of pitch.
Phase-locking limits matter far less when envelope-fine-structure interaction encodes pitch — Relates to the classic phase-locking limit near 4-5 kHz in auditory nerve fibers, and why pitch of unresolved harmonics survives above that.
Periodicity cues for pitch split into fine-structure, envelope, and interaction periodicities — Connects to phase-locking literature on auditory nerve fibers and the classic place vs. timing debate in pitch perception.
Pitch estimates depend on combined envelope-fine structure cues, not envelope alone. — Connects to chapter 11’s earlier discussion of pitch perception models that treat envelope and fine structure separately.
Neuronal firing thresholds shape which pitch periodicities a system can detect — Threshold-dependent periodicity detection connects to auditory nerve fiber rate-level functions (Sachs and Abbas 1974) and general neural spike-threshold gating.
Pitch percepts may only require encoding fine-structure peaks near envelope maxima — Connects to phase-locking limits in auditory nerve coding and unresolved vs resolved harmonics discussions familiar from other pitch-perception readings.
Shifted complex tone pitch depends on envelope-fine-structure interaction, not fine structure alone. — Connects to classic psychoacoustic literature on pitch of unresolved harmonics and the envelope/fine-structure dissociation debate (Moore, Oxenham).
Dichotic split-harmonic presentation enables pitch via resolvability that temporal models cannot capture. — The dichotic even/odd harmonic split is a classic result in the place-versus-temporal coding debate for pitch.
Low-frequency pitch judgments reflect temporal fine structure via neural phase locking — Relates to the refractory-period limits of auditory nerve firing — links to general auditory physiology of phase locking though no chapter in this book names it directly.
Melodic pitch changes shorten rather than lengthen N1m peak latency in auditory cortex — Recalls the established finding that N1m latency is among the most precise, reliable MEG statistics, making these offsets meaningful.
Melody change processing localizes more anteriorly than energy onset in auditory cortex — Joins the broader literature mapping pitch and onset processing onto Heschl’s gyrus vs. planum temporale distinct generators.
Melody processing recruits a distinct generator in anterior Heschl’s gyrus — Connects to broader literature on functional segregation within auditory cortex (parabelt/core organization), and to the reader’s interest in where spectrotemporal analysis yields to pattern/melody processing.
Melodic notes trigger earlier and larger auditory cortex responses than tonic notes — Connects to prior knowledge of ERPs/N1m as the standard auditory evoked component, and to the adaptation/oddball literature showing stimulus novelty modulates N1m latency.
Click detectability depends on which half of a 4-Hz stimulation cycle it falls in — This directly extends the Neuling 10-Hz alpha finding to the delta/theta range — a cross-frequency generalization worth registering.
Transcranial alternating current stimulation entrains cortical oscillations and alters perception — Connects to the reader’s ongoing interest in how external stimuli entrain neural oscillators and gate perception — a mechanism linking physiology to psychoacoustics.
Auditory perception performance varies with the phase of external rhythmic brain stimulation — Connects to sound-brain entrainment literature (Schroeder & Lakatos) and to how neural oscillations align with stimulus envelopes - relevant to the reader’s interest in psychoacoustics and cognition chapters.
Best-phase alignment compensates for inter-individual shifts in neural response phase — The phase-wrapping/alignment problem parallels best-pitch or best-frequency alignment used across psychoacoustic individual-difference studies.
Cranial electrode placement can target both auditory cortices homophasically with matched circuits — Connects to methods for transcranial electrical stimulation protocols; auditory cortex targeting via 10-20 system positions.
Perceptual segregation of target sounds may arise from temporal coherence with cortical oscillations — Connects to streaming/scene-analysis reading on binding by synchrony versus other cues.
Only three human studies have directly manipulated sound-brain entrainment perceptually — Connects to the auditory-scene-analysis and oscillatory-phase themes reading purpose; the phase-lag design mirrors the reader’s broader interest in temporal mechanisms of hearing.
Transcranial alternating current phase modulates auditory click detection at the stimulation frequency — Connects to the reader’s knowledge of auditory scene analysis and entrainment mechanisms from earlier chapters on temporal coherence.
The brain must decompose an unlabelled auditory mixture into coherent perceptual objects — Relates to my audio/soundscape reading — the ‘unlabelled mixture’ problem parallels the lo-fi soundscape/figure-ground discussions in The Soundscape.
Auditory neurons become increasingly selective for complex sounds along the ventral pathway — Connects to the reader’s material on auditory pathway hierarchy from earlier chapters in this same book, and to the broader literature on core vs. belt cortex specialization.
AL neurons carry sensory evidence for pitch decisions while ML do not — Microstimulation causally shifting percepts echoes classic work on motion direction in area MT (Salzman & Newsome) — a strong cross-modal parallel worth following.
Holding stimuli constant while varying perceptual reports can dissociate sensory from perceptual neural activity — Relates to the reader’s interest in how cognition links to physiology — the neural dissociation technique threads across the physiology-psychoacoustics-cognition triangle.
Auditory ventral-pathway processing is hierarchical, with decisions arising only in frontal cortex. — Fits the broader cortical hierarchy/decision literature (e.g., Heekeren et al. on decision-making in prefrontal vs. sensory regions).
Sound source location alone can split one acoustic stream into two perceived sources — Spatial stream segregation is a core mechanism in auditory scene analysis (Bregman), complementing the book’s earlier noise and silence material by showing how location cues bind sounds to sources.
Prefrontal neurons encode call meaning independent of acoustic form — vlPFC referential coding abstracted from acoustics parallels Marr-style representational level separation and the broader ‘what’ pathway literature.
Spatial separation can split identical sound sequences into percepts of distinct sources — Relates to Gestalt grouping principles and the broader auditory-streaming literature (Bregman).
Spatial location cues alone can segregate identical sounds into separate perceived sources — Spatial source segregation parallels visual grouping by location and ties into dorsal-ventral ‘where vs what’ pathway debates from neuroscience of vision.
Neurons along the auditory core-belt pathway respond to progressively more complex sound features. — A concrete instantiation of hierarchical sensory processing, paralleling the visual ventral stream’s progression from simple to complex feature selectivity.
Neural evidence of encoding auditory objects requires modulation by perceptual reports — This report-based dissociation criterion echoes neuroeconomic and vision choice-activity methodology (e.g., Nienborg and Cumming referenced later).
Conflicting findings on A1 decision encoding stem from task demands and analysis artifacts — The imperative to dissociate choice activity from stimulus activity on trial subsets is the same logic used in visual two-alternative forced-choice neurophysiology.
Auditory perceptual decisions are encoded diffusely across frontal and parietal cortex. — Complements the earlier finding that belt neurons lack choice modulation — together they localize decision coding to frontoparietal regions beyond sensory cortex.
Noise and reverberation damage speech segregation through different neural mechanisms — Builds on cocktail-party literature; connects to psychoacoustic work on periodicity pitch as segregation cue and to the differential effects of reverberation vs noise on intonation-dependent stream segregation.
Normal-hearing listeners tolerate moderate reverberation far better than noise — Perceptual tolerance for reverberation pairs with the neural finding that steady-F0 segregation survives moderate rooms but not noise — a two-level congruence.
Reverberation and broadband noise affect neural coding differently. — Connects to the broader body of work on how auditory cortex achieves noise- and reverberation-invariant speech representations (Mesgarani et al. 2014), which contrasts sharply with brainstem sensitivity shown here.
Reverberation erases formant information for intonated vowels but noise only degrades higher formants — Links to articulatory/acoustic phonetics: F1 is amplitude-robust while F2/F3 depend on spectral detail that reverberation smears.
Reverberation and natural pitch modulation combine to impair vowel segregation more than either alone — Connects to classic Auditory Scene Analysis work (Bregman) on concurrent vowel segregation and the ‘double vowel problem’ in psychoacoustics.
Reverberation and natural pitch modulation combine to impair vowel segregation more than either alone — This conditional failure pattern echoes the psychoacoustic knife-edge where two degrading factors interact nonlinearly — comparable to the interactive (not additive) degradations seen in cocktail-party hearing literature.
High-frequency auditory nerve fibers receive far more broadband noise power than low-frequency fibers. — Ties to the reading purpose on hearing physiology: within-band SNR asymmetry across BFs is a mechanical consequence of filter bandwidth.
Multisensory binding decisions depend on a prior coherence estimate between auditory and visual streams. — Parallel to predictive-processing accounts where prior probability of integration (unity assumption) gates multisensory binding.
Voluntary attention modulates how audiovisual contexts affect subsequent fusion — The claim that salient bottom-up comodulations bind pre-attentively while weak coherence needs top-down attention parallels scene-analysis dual-process accounts in vision and hearing.
Audiovisual speech fusion is not an automatic process but context-modulated. — Challenges Massaro’s classic automatic-fusion view of audiovisual speech perception — a useful contrast with the earlier chapter framework in this reader’s library.
Auditory scene analysis segregates sound first and forms percepts later — Connects to Bregman’s Auditory Scene Analysis framework familiar from cognitive psychology texts.
Syllabic speech produces far stronger audio-visual correlation than sentences do — McGurk effect and its relation to audiovisual correlation connects to classic McGurk illusion literature; 0.59 vs 0.10 correlation numbers are a concrete anchor.
Seconds of mismatched audiovisual context weaken the McGurk visual bias. — Directly relevant to the reader’s prior ‘One Square Inch of Silence’ and ‘Soundscape’ work on listening, and to cocktail-party/dual-stream perceptual binding generally.
Short preceding incoherent contexts maximally suppress the McGurk effect — Relates to the reading thread on how context-driven expectations shape audiovisual speech perception.
Attentional focus instructions can be experimentally manipulated to shift audiovisual integration during speech perception. — Connects to the Nahorna et al. (2012, 2015) audiovisual monitoring paradigm and general selective-attention research on speech perception.
Brief prior exposure to mismatched audio-visual speech reduces later visual weight in fusion. — Relates directly to the hearing-in-noise goal: visual weight modulation matters for speech intelligibility in competing scenes.
Visual streams can drive auditory segregation, but grouping order remains contested — Connects to classical auditory scene analysis work of Bregman, an anchoring concept for the reader’s psychoacoustics focus.
Syllable stimuli show far higher audiovisual correlation than sentences do — The 0.59 vs 0.10 correlation gap between speech units is a concrete number relevant to any account of why McGurk is stronger for syllables - useful alongside Mcgurk & MacDonald original findings.
Audiovisual fusion is context-modulated, not automatic, and scales with audio-video coherence — Connects to the browser/mediator debates in speech perception literature and to general multisensory integration theory.
Brief incoherent audiovisual contexts reduce the McGurk effect by decreasing visual weight. — Relates to the McGurk effect, the canonical cross-modal speech illusion, and prior-trial context effects studied in psychophysics.
Large melodic intervals and multiple transpositions each independently impair deviant detection. — Relates to general claims about pattern statistics and interval coding in psychoacoustics reading.
Even trained musicians have great difficulty detecting deviant melodic patterns under transposition — Connects to the broader literature on relative pitch being easier in recognition (melodies in different keys) than in discrimination tasks; parallels findings on absolute vs relative pitch and categorical perception.
Pattern representations become more permissive as component variance grows — Connects to predictive coding accounts of perception and to general object-categorization theory beyond audition.
Learning a tune may require hearing it at many pitch levels across linked contexts — Parallels the trace model of visual invariance to how the reader absorbs embodied/relational knowledge across contexts.
Deviant-pattern detection worsens as within-pattern interval variance rises but ignores interval magnitude — Connects to broader literature that listeners encode relative interval structure of tone patterns, consistent with other chapters on the mechanisms of relative pitch in this same volume and the work on pattern deviance/mismatch negativity paradigms.
Relative pitch perception is more limited than commonly assumed — Connects to the premise that melodic contour and tonality, not exact intervals, carry most pitch-pattern recognition — a mechanism relevant to my hearing research interest.
Randomly generated tone patterns must avoid repeated equidistant intervals to prevent repeated pitches. — Relates to stimulus construction constraints in psychophysics, connecting to how melodic patterns avoid pitch repetition artifacts.
Perceptual discrimination precision falls as stimulus variance rises — The predictive coding/precision-varariance link is central to Friston’s free-energy framework; relevant given the reader’s stated interest in perception and cognition.
Greater interval variance in tone patterns impairs relative pitch detection — Connects to work on working memory load and melodic contour processing — more variable patterns tax the comparisons holding the reference interval.
Zwicker tones support accurate musical interval matching like pure tones — Connects to debates about whether peripheral temporal coding (phase locking) is necessary for pitch — relevant to human cochlear implant pitch limits, where phase locking is absent.
The temporal-to-place mechanism transition occurs near 8 kHz, not 4-5 kHz — Revises the standard textbook figure; relevant to the reader’s broader model of frequency coding limits.
Zwicker tone pitch sits about 10-20 percent above the notch’s lower edge frequency — Connects to the reader’s larger interest in otoacoustic emissions and how listening to loud noise can unmask spontaneous OAEs, which is a mechanism question about the ear’s active amplifier.
Weak musical pitch perception can persist without peripheral phase locking. — Connects to the broader question in hearing science of temporal versus place coding of pitch — the Burns & Feth reference is engaged directly here.
Zwicker tones support accurate musical interval matching like pure tones — Ties to ongoing pitch-perception debate over temporal vs. place coding; relevant to reader’s physiology/psychoacoustics goal.
Musically trained listeners can judge pitch intervals even above the phase-locking limit — Connects to the reader’s understanding of temporal vs place coding theories of pitch across 2-14 kHz frequency ranges.
Zwicker-tone matching gives anomalously high pitches when spontaneous otoacoustic emissions become audible. — Connects to the reader’s core interest: an objective physiological artifact (OAEs) contaminating a purely subjective perceptual judgment — a physiology-cognition interface.
Zwicker tone interval matches are as reliable as those for high-frequency pure tones — Ties to Burns and Feth (1983) on musical interval matching at high-frequency tones, relevant to how the peripheral code for pitch degrades with frequency.
Sensorineural hearing loss reduces contrast in F0-synchronized neural rates across frequency — Connects to the broader theme that hearing loss degrades temporal coding (declining phase-locking, wider tuning) — the very cues that auditory nerve fibres rely on.
Cochlear nonlinearities establish F0-synchronized firing patterns that the midbrain converts into rate codes — Connects to Plack’s discussion of phase-locking degradation and to place vs. temporal theories of pitch; also to the general principle of temporal-to-rate conversion seen in other sensory systems.
Hearing loss degrades formant coding by weakening synchrony capture across auditory-nerve fibres — Directly relevant to the reader’s purpose of understanding auditory coding mechanisms; connects to psychoacoustic work on envelope-F0 coding and to clinical audiometry puzzles of discrepancy between audiogram and speech-in-noise performance.
Inhibitory-excitatory synaptic timing determines a midbrain neuron’s best modulation frequency. — The competitive excitatory-vs-delayed-inhibitory timing explanation parallels neural feature-tuning logic seen in many lateral-inhibition circuits (e.g., visual cortex orientation selectivity).
Sensorineural hearing loss reduces contrast in F0-synchronized neural rates across frequency — Connects to the reader’s broader theme of small degradations in precision — here the loss of across-channel contrast in F0-synchronized rates — as a mechanism underlying perceptual impairment.
Hearing aids should restore F0-synchrony profiles not stimulus spectra — Connects to the degraded-speech-in-noise experience specific to cochlear hearing loss, the practical puzzle behind this whole line of coding work.
Sensorineural hearing loss reduces contrast in F0-synchronized neural rates across frequency — Connects to phase-locking and temporal coding of pitch literatures; ties neural speech encoding to my prior reading on impaired-hearing mechanisms.
SNHL disrupts the fluctuation pattern that normally codes vowel formants. — Connects to the broader mechanism literature on how temporal coding loss in hearing impairment affects speech intelligibility.
Pitch-fluctuation contrast coding may outperform formant-peak rate coding for vowels — Directly complements the reader’s interest in how formant coding / envelope coding works in normal hearing and its limits.
Band-enhanced rate contrast for vowels resists noise and level changes — Connects to the noise-robustness puzzle in the intro: midbrain fluctuation-contrast coding stays robust where AN rate coding fails.
Hearing loss degrades vowel coding primarily through F0-synchrony loss rather than mean firing rates — Connects to temporal coding of AM signals and how wideband peripheral filtering smears low-frequency envelope cues — parallels discussions of envelope coding deficits in the earlier auditory nerve chapters.
Individual normal-hearing listeners differ greatly yet each is internally consistent in consonant perception. — Mirrors the individual-variability findings and the critique of averaging that runs through the deeper soundscape and auditory literature.
Consonant perception varies substantially even across repeated tokens of one talker. — Challenges the assumption that within-talker tokens are interchangeable; relevant to the reading-goal focus on what normal hearing actually processes beyond phonetic identity.
Slow envelope fluctuations up to 16 Hz predict consonant-in-noise perception. — Strengthens the temporal-modulation-processing link; relevant literature includes modulation filterbank theories and the AM (amplitude modulation) perception literature.
Internal perceptual noise rises as signal-to-noise ratio falls. — The ‘internal noise baseline’ here parallels the within-listener reproducibility floor discussed alongside perceptual distances; relevant to the reading-goal interest in how cognition and physiology jointly set perception limits.
Consonant confusions shift with noise type, filtering, and vowel context. — Connects to the classic speech-perception literature (Miller and Nicely) on consonant confusions; relates to psychoacoustic foregrounding of noise-type effects on perception.
Consonant confusions shift with noise type, filtering, and vowel context. — Connects to the reader’s stated interest in psychoacoustics of normal hearing — consonant confusion patterns as a window into acoustic-to-percept mapping independent of higher-language effects.
Consonant identification variability shifts from token-level to listener-level as SNR falls — Directly relevant to the reader’s stated interest in normal hearing cognition: the SNR dependence of internal noise baseline tracks task difficulty, paralleling signal-detection threshold shifts.
Speech recognition can be modeled as matching test signals against noise-masked talker templates — Template-matching with DTW connects to exemplar-based speech perception theory; the repeated-noise-injection averaging parallels psychophysical detection experiments.
Modulation power is captured by filters tuned to 2, 4, 8, and 16 Hz — Connects to the 4-16 Hz AM-rate range emphasized in the envelope-vs-fine-structure literature on speech perception.
Slow envelope fluctuations up to 16 Hz predict consonant-in-noise perception. — Connects to the reader’s broader reading on speech perception models and modulation-based processing in psychoacoustics.
Consonant confusions shift with noise type, filtering, and vowel context. — Connects to the reader’s broader physiology/psychoacoustics reading — consonant perception as a tractable probe into how acoustic cues map to categorical percepts.
Meaningful speech-in-noise benefit requires double the detectable SNR change — The 3 dB JND vs 6 dB JMD gap connects to hearing-aid fitting standards and to general psychophysics of detectability vs meaningfulness across modalities.
Hearing aids rarely deliver meaningful speech-intelligibility benefits exceeding their detectability — Connects to hearing-aid non-use from unmet expectations (McCormack and Fortnum 2013) — measurable benefit without satisfaction.
Listeners may judge SNR changes via perceived intelligibility rather than raw signal level — Connects to level JND literature for older hearing-impaired listeners (Whitmer and Akeroyd 2011) and to the broader distinguishability of level vs relative-level perception.
Listeners value loudness increases as much as SNR improvements they provide — Relates to the broader theme bridging detectability thresholds (JNDs) to meaningfulness judgments in signal processing.
SNR must rise at least 3 dB before listeners tolerate one more conversation — Consistent with the ~3 dB speech-perception-in-noise commonly cited in audiology; ties detectability thresholds to functional outcomes.
SNR just-noticeable difference grows from about 3 to 3.7 dB at positive reference SNRs — This is a core psychophysical figure for any writing about hearing-in-noise and audibility thresholds; connects to the reader’s physiological/psychoacoustic interest in discrimination limits.
Willingness to trade hearing devices requires at least a 6 dB advantage — The difficulty-dependence echoes the finding that near-threshold contexts magnify the value of improvements, paralleling Weber-law sensitivity windows.
Hearing-aid benefit studies lack an agreed threshold for meaningful SNR improvement — Connects to audiology/hearing-aid outcome literature on minimal clinically important difference (MCID); the ‘meaningful benefit’ construct parallels MCID work in other fields.
A repeated sentence is more intelligible on second hearing — Connects to the chapter’s central thesis that detectability thresholds for SNR changes set the floor for meaningful intelligibility ratings.
Listeners may be judging speech intelligibility rather than raw signal-to-noise ratio — Connects to the reader’s interest in cognition of hearing: apparently the listener converts the available cues into whatever domain is most accessible, not necessarily the one the experimenter intends.
Normal-hearing listeners detect a 3 dB change in speech-to-noise ratio — Connects to the classic Weber-fraction finding that JND scales roughly proportionally with stimulus level, here extended to SNR discrimination, and to the reader’s broader interest in psychoacoustic thresholds in normal hearing.
Individual listening styles partially stem from cochlear micromechanical irregularities rather than attention alone — Links auditory physiology to the discrimination/decision-weight psychophysical literature the reader is likely building toward on how perception is shaped below the level of attention.
Pure tone audiograms fail to predict speech-in-noise listening difficulties — Connects to broader hearing-science literature on audiogram insensitivity and to the reader’s stated interest in normal-hearing physiology vs. real-world deficits.
Spontaneous otoacoustic emissions align with peaks in stimulus-frequency fine structure — The 30 dB amplitude variation in cochlear mechanics connects to the ‘threshold microstructure’ and loudness literature.
Pure tone audiograms fail to predict speech-in-noise listening difficulties — Connects to the reader’s broader interest in why standard audiological tests underdiagnose real listening problems.
Loudness perception strongly shapes the decision weights listeners assign to sound components — Ties the reader’s interest in decision weights and loudness into a concrete diagnostic pathway for OAEs.
Each listener has a stable, individual weighting of frequencies when hearing speech in noise — The idea of stable individual perceptual styles and the gap between lab measures and clinical tools echoes the reader’s broader theme of how diagnostic instruments flatten individual variation.
Cochlear irregularity measured via otoacoustic emissions could predict individual listening decision weights — Connects to the reader’s knowledge of otoacoustic emissions from hearing physiology and loudness-perception literature.
Spontaneous otoacoustic emissions align with peaks in stimulus-frequency fine structure — Connects to the physiology of the cochlear amplifier and the source of the fine structure ripples seen in SFOAE measurements.
Behavioral decision weights track cochlear fine-structure depth independent of spontaneous emissions — Connects to the broader literature linking otoacoustic emission microstructure to loudness perception and perceptual decision-making; this is strong evidence that physical cochlear fine structure directly shapes perceptual weighting.
Otoacoustic emission levels predict individual decision weights in level discrimination tasks — Connects cochlear fine-structure measures (SOAEs, SFOAEs) to psychoacoustic decision weights, bridging physiology and cognition; relevant to target-in-noise listening and clinical speech diagnosis.
Otoacoustic emission levels predict individual decision weights in level discrimination tasks — Connects to the broader psychoacoustic theme that individual cochlear microstructure shapes subjective auditory decisions, echoing individual differences work in audiometry and perception literature.
Cochlear emission fine structure may predict individual listening performance — Links to the reader’s broader interest in cochlear mechanics via loudness, threshold microstructure, and individual differences in auditory processing.
Cochlear otoacoustic emissions measurably shift behavioral thresholds, selectivity, and loudness — Links to the reader’s core interest in bridging cochlear mechanics (physiology) to behavioral perception (psychoacoustics).
Cochlear threshold microstructure shapes loudness, which drives hearing decisions — Connects cochlear fine structure and its 30 dB amplitude variation to the reader’s broader interest in links between physiology, psychoacoustics, and decision behavior.
Normal-threshold listeners can show temporal coding deficits localized to the auditory brainstem — Connects to hidden hearing loss / cochlear synaptopathy literature — Kujawa and Liberman’s work on temporary noise-induced AN degeneration
Combined psychoacoustic and envelope-following-response measures can dissociate hearing-loss subtypes — Directly advances the reader’s goal of understanding mechanisms linking cochlear physiology to perception — ties gain loss to temporal envelope coding, a core psychoacoustic mechanism.
Cochlear gain loss can compensate for temporal coding deficits in AM detection — Connects to the broader theme of cochlear neuropathy hidden behind normal audiograms — hidden hearing loss literature.
Cochlear gain loss may paradoxically enhance modulation sensitivity in broadband noise — Connects to the reader’s interest in cochlear compression nonlinearity and envelope coding; echoes loudness growth/compression discussions in prior psychoacoustics literature.
Cochlear neuropathy harms supra-threshold encoding because noise targets high-threshold auditory fibers — Connects to the broader hidden-hearing-loss literature (Kujawa & Liberman synaptopathy) distinguishing OHC gain loss from AN neuropathy, familiar territory given the reading history.
DCOAE measurements isolate the f2 site free of fine structure — Details the DPOAE sweep method tied to Long et al. 2008 and fine-structure artifacts from Mauermann and Kollmeier 2004, useful for understanding cochlear gain assessment methodology.
Cochlear gain loss can compensate for temporal coding deficits in AM detection — Connects to the reader’s broader work on psychoacoustic masking and temporal processing; also cites Moore 1996 and Bharadwaj 2015 for follow-up.
Narrowband noise degrades AM detection mainly in listeners with reduced temporal coding — Relevant to the reader’s interest in how impaired cochlear filters interact with maskers — narrowband/masker-focused effects relate to reduced frequency selectivity in HI listeners.
Cochlear gain loss can compensate for temporal coding deficits in AM detection — Connects to the reader’s understanding of how distinct sensorineural pathologies combine rather than additively degrading perception.
Cochlear compression protects modulation detection from masking noise — Connects to the reader’s wider interest in cochlear gain/compression as the mechanical substrate behind psychoacoustic deficits seen in earlier hearing chapters.
Medial olivocochlear efferent feedback reduces cochlear gain most for frequencies near the signal — Connects to the efferent onset delay and cochlear gain reduction mechanisms, themes central to psychoacoustics and physiology.
Masking measured within the efferent onset delay isolates gain reduction cleanly — Relates to reading purpose on cognition/perception in normal hearing: how experimenters must time stimuli within an ~25 ms physiological delay to cleanly separate efferent from afferent contributions.
Medial olivocochlear efferent feedback reduces cochlear gain most for frequencies near the signal — Connects to efferent-mediated gain reduction and the efferent onset delay literature (Guinan, James); also links psychoacoustic masking thresholds to cochlear physiology as in the reader’s interest in physiology-perception mechanisms. Seed
Apparent temporal-coding correlations across tasks vanish once age effects are removed — Could anchor an article on how age effects can masquerade as genuine perceptual-mechanism relationships in psychophysical experiments.
Reading-Span may be the wrong cognitive measure for predicting speech-in-noise skill — Angle: whether the null result is a true cognitive effect or an artifact of the specific WM measure chosen.
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — A cautionary methodological insight: uncorrected age confounds inflate WMC-SiN correlations, relevant to any meta-analysis of cognitive-auditory studies.
Speech-in-noise working-memory studies are routinely underpowered with null-inclusive confidence intervals — An angle: the contradiction between this null result and the huge research investment in WM-as-predictor for hearing aid outcomes could make a skeptical methods essay.
Working memory correlates with speech-in-noise performance only in middle-aged and older normal-hearing listeners — Angle: the ‘cognitive reserve’ account of why young normal-hearing listeners don’t need working memory for speech-in-noise — an essay on when the brain compensates vs. when it doesn’t.
Cognitive abilities affect speech-in-noise perception situation-specifically while hearing thresholds act uniformly — Angle: why cognitive factors in speech-in-noise research show inconsistent results across studies — task context modulates cognitive contribution.
Basic storage-based working memory tracks intelligibility more broadly than reading span — Contradicts the field convention favoring processing-heavy WM tasks like reading span; angle on when simple memory tests outperform complex ones could make a methodology critique piece.
Hearing aids largely restore affect perception to age-normal levels — The arousal-valence dissociation in emotion perception is a potential angle: most affect-perception research collapses dimensions, but this shows they rest on distinct acoustic substrates that respond differently to intervention.
Acted speech masks true effects of hearing loss on affect perception — Angle: ‘acted vs natural speech’ as a methodological blind spot — how laboratory exaggeration hides real perceptual deficits in patient populations.
Physiological and behavioral binaural measures agree with each other but not with pitch matching. — Potential article: why the clinical default of pitch-matching for bilateral implant pairing may be systematically wrong, and what a better evidence-based protocol could look like.
Closing one ear barely changes perceived loudness of live speech — The finger-in-ear demonstration is a brilliant classroom or public-science demo contradicting textbook summation rules.
Simulated finger occlusion reduces but does not remove binaural loudness summation. — The persistence of binaural summation even under realistic occlusion — with largely intact low frequencies — challenges the simple loudness-summation account of binaural hearing.
Loudness constancy requires ecologically valid, multisensory real-world stimuli — The case for an article: standard lab findings about binaural loudness summation may not reflect real-world perception — an instance of low ecological validity distorting a foundational psychophysical result.
Directional source allocation, not binaural unmasking, explains spatial release — An angle: the finding that SRM survives removal of all low-SNR components argues spatial hearing works via source segregation and attention, not sub-threshold recovery — a candidate for a piece on what actually underlies the cocktail-party effect.
Hearing-impaired listeners’ poor spatial hearing may not reflect actual spatial-processing deficits. — An essay angle: clinical tests of hearing impairment may systematically misattribute an audibility limitation to a ‘higher’ perceptual deficit, with implications for how hearing-loss phenotypes are labeled.
Apparent spatial deficits in hearing loss may really be audibility deficits — A hearing-aid design angle: if SRM deficits are audibility-driven rather than spatial-mechanism-driven, fitting strategy changes meaningfully.
Audibility equalization reduces but cannot eliminate hearing-loss effects on speech-in-speech — An essay on the limits of ‘making speech audible’ as a hearing-rehab end-goal: audibility is necessary but not sufficient, and the talker-perceptual boundary lies in supra-threshold processing.
Hearing-impaired speech deficits stem from using glimpsed information, not spatial processing — Article angle: hearing-loss deficits in cocktail-party hearing persist even when equal audibility is given, pointing toward central rather than peripheral causes.
Spectral valley over-attenuation may explain why contrast enhancement harms CI users — Angle: ‘When a good idea fails in practice’ — how simulation models that omit real processing constraints mislead engineering design in hearing-aid and implant development.
The MOCR’s unmasking role remains unproven in direct human experiments — A lecture or essay on ‘the experiments we cannot yet run’ — using CI users as a natural experiment to test a mechanism that cannot be isolated in healthy ears.
Cochlear implants enable experimental control of the olivocochlear reflex unavailable in normal hearing — An essay on how a clinical device becomes an experimental probe — the CI as a tool for basic science that no natural-hearing subject permits.
A contralateral-efferent-inspired coding strategy enhances sound-source segregation for cochlear implant listeners — The MOC strategy is a concrete application of efferent-reflex physiology to a device—an article on translating auditory-reflex research into cochlear-implant coding could center on this.
Cochlear implant compression mimics cochlear mechanics and can test efferent function — An experimental-design essay: using implant dynamic compression as a ‘switch’ to selectively toggle an absent reflex in humans - a natural experiment unavailable in normal hearing.
Cochlear implants enable experimental control of the olivocochlear reflex unavailable in normal hearing — A valuable design insight for essays on experimental method: populations that “lack” an effect naturally (deaf implanted listeners) can be repurposed as ideal within-subject controls by reinstating the effect digitally.
Cathodic pulses produce larger cochlear implant responses than anodic pulses across pulse shapes — The cathodic blocking hypothesis — where stimulation of the same polarity that excites the periphery also hyperpolarizes central fibers into conduction block — could ground an article on why cochlear implant polarity choice matters clinically.
Cochlear implant growth functions can behave non-monotonically near the top of the dynamic range — The finding that one polarity can be more effective at low levels yet less effective at high levels is a striking counterintuitive result — the polarity-dependence of recruitment is level-dependent, a nuance that complicates any clinical strategy keyed to a single polarity preference.
Deconvolution reveals firing components invisible in raw eCAP waveforms — The claim that signal-processing deconvolution can reveal physiological subcomponents hidden in raw neural waveforms is a strong angle for an accessible essay on how analysis methods make invisible neural structure visible.
Loudness judgments at a fixed level vary probabilistically across categories — The idea that perceptual judgments are inherently probabilistic distributions, not points, could anchor an article on how measurement methodology shapes what we know about hearing.
Standard loudness analysis discards response variability that probabilistic models can exploit — Angle: the information lost when scientists average away noise in perceptual measurement — a general essay on why within-subject variability carries signal.
Testing hearing-impaired listeners could reveal how loudness distortion maps to brain activity — Loudness recruitment as a natural experiment decoupling physical intensity from perception — a potential article on how pathology reveals neural coding.
Loudness recruitment in hearing loss could be probed via Heschl’s gyrus fMRI — Angle: auditory fMRI as a diagnostic readout for loudness recruitment — how brain-imaging could replace subjective psychoacoustic tests.
Posterior medial Heschl’s gyrus activation best predicts individual perceived loudness — The proposed hearing-impaired recruitment study is a natural diagnostic-application angle.
Comparing normal and recruiting-impaired listeners would reveal where loudness is decoded — An essay angle on how pathological ‘natural experiments’ (recruitment) dissect normal perceptual mechanisms.
Loudness recruitment in hearing loss could be probed via Heschl’s gyrus fMRI — Loudness recruitment in hearing-impaired listeners as a natural experiment separating physical intensity from perception — could make an article about measuring subjective auditory experience objectively.
Loudness recruitment in impaired listeners could reveal how cortex encodes perceived level — An essay or research thread on whether loudness recruitment normalizes or distorts cortical coding of subjective level — comparing normal vs recruiting ears could separate physical from perceptual encoding.
Tinnitus reduces default mode network activity by blocking mind-wandering — The parallel between tinnitus blocking mind-wandering and autism-linked DMN dysfunction suggests an angle on ‘intrusive internal stimuli and the loss of daydreaming’ as a cross-condition phenomenon.
Tinnitus reduces default mode network activity by blocking mind-wandering — Possible essay angle: tinnitus as a permanent attention tax that denies the brain its restorative default-mode state — a mechanistic account of why chronic phantom sound is exhausting.
Pooling all subjects’ data in one ICA avoids group-decomposition biases in resting-state fMRI. — Could form the methodological spine of an article on how fMRI analysis choices confound group comparisons.
Scanner noise does not mask chronic tinnitus perception during imaging — Angle: the failure of loud noise to mask tinnitus reveals something about how tinnitus is generated centrally rather than peripherally.
Optimal interaural phase difference for cortical following responses depends on the IPM rate — Angle: the shape of a neural tuning curve as a fingerprint of the underlying mechanism — how response-vs-parameter curve shapes reveal whether a system is peak-picking or level-integrating.
Objective IPM responses can distinguish MSO from LSO pathway activation — The diagnostic/clinical payoff — an EEG-based measure that can tell which binaural brainstem pathway a cochlear implant user relies on — is a strong angle for an article on objective hearing assessment.
Incidental listening may improve statistical change detection over attention — A possible essay angle on whether deliberate attention helps or hinders perceptual statistical learning.
Auditory change detection integrates statistical evidence accumulated over time — A behavioral result on accumulation of noisy perceptual evidence could anchor an essay on how perception trades sampling duration against change magnitude.
Manipulating envelope and fine structure separately reveals their competing roles in tone perception. — The chimerizer swap design is an elegant experimental-control template worth describing in any piece on dissociating perceptual cues.
Cochlear nonlinearity models must reproduce finite-timescale compression dynamics — Could seed an essay on why static compression curves misrepresent how real cochleas process fast-varying sounds like speech.
Low-frequency exposure temporarily activates new otoacoustic emissions that were previously absent in most ears. — The discovery that low-frequency sound can ‘switch on’ previously silent cochlear emission sites is a strong hook for a pop-science article about the inner ear being more labile than textbook models suggest.
Suppression data from impaired auditory nerves could improve hearing aid speech-in-noise amplification — Angle: why hearing aids fail at speech-in-noise — the loss of cochlear suppression nonlinearity is a physiological root cause DRC algorithms only approximate.
Noise-induced hearing loss reduces neural suppression in proportion to tuning broadening — Angle: hearing aids compress the wrong thing — restoring gain but not the suppressive nonlinear interactions that speech-in-noise coding actually depends on.
A filterbank model can simulate hearing impairment in normal-hearing listeners — An article angle: how computational models let researchers isolate single auditory mechanisms (like compression) that are entangled in real patients — a ‘how we isolate variables in complex systems’ story.
Speech can be resynthesized to selectively remove cochlear compression in hearing listeners — An essay angle: prosthetics and simulators that isolate a single sensory mechanism let us run ‘controlled experiments’ on ourselves — a model for how to decompose any complex impairment.
Objective hearing measures require linking perception, physiology, and speech ability. — Angle: ‘Can we detect hidden hearing loss before the audiogram budges?’ — the race to build objective functional-hearing tests that catch deficits before permanent threshold shifts.
Better speech-in-noise ability correlated with worse brainstem encoding — Angle: why the standard audiology pure-tone audiogram misses most of what limits hearing in everyday noisy environments.
Aging impairs speech-in-noise ability beyond what hearing thresholds predict — Angle: the audiogram as an outdated gold standard in audiology — an essay arguing threshold testing systematically under-predicts real-world listening difficulty.
Patient history predicts only 10 percent of cochlear implant speech outcomes — An article angle: the striking failure of patient-history factors to predict cochlear implant success, and why brain imaging may outperform them.
Entracking creates flat rate plateau codes that supplant tonotopic place codes — The butte staircase as a rate code that is ‘orthogonal’ to the tonotopic map is a vivid geometric metaphor for how a second neural code can coexist with the classic place map.
Transcranial stimulation may someday bypass impaired auditory pathways by injecting envelope information — A prosthetic angle: electrically delivering envelope information to the cortex as an alternative to cochlear implants for certain deafness types.
Perceptual segregation of targets from distractors may depend on cortical temporal coherence — Angle: temporal coherence as the neural currency of auditory scene analysis — how the brain builds a ‘stream’ from synchrony rather than from features alone.
Individualized EEG-based tACS frequency matching may improve stimulation effectiveness — An angle on personalized neurostimulation tuning to an individual’s endogenous cortical rhythms is a plausible explainer piece.
Acoustic and electric stimulus phase alignment is the key experimental variable — Angle: can rhythmic neural entrainment from external electric fields genuinely gate auditory perception at specific oscillation phases, or is this a methodological artifact of expectation?
Voluntary attention modulates how audiovisual contexts affect subsequent fusion — Potential angle: the coherence-before-binding architecture as a general account of how attention selects among competing multisensory streams — relevant to cocktail-party problems in both vision and hearing.
McGurk-effects that saturate tend to be excluded from audiovisual experiments — Angle: experimental design choices like ceiling-exclusion silently shape reported effect sizes in perception science.
Learning a tune may require hearing it at many pitch levels across linked contexts — Angle for an essay on how musicality—like vision—emerges from accumulated encounters rather than abstract rules; connects to how ‘ear training’ actually works pedagogically.
Zwicker tones arise centrally, not from cochlear mechanics or phase locking — The key experimental logic — a percept demonstrably free of peripheral mechanics can test whether pitch requires temporal codes — is a model of how to isolate a mechanism.
Hearing aids should restore F0-synchrony profiles not stimulus spectra — Article angle: current hearing aids amplify spectra but the cochlear code for speech may be the F0-fluctuation contrast profile — an explicit design target for better signal processing.
Cochlear implant strategies should aim to restore F0-synchronized profiles, not stimulus spectra — An essay angle: redesigning cochlear implant/hearing aid processing around the pitch-synchrony information that the midbrain actually extracts, rather than reconstructing the full spectrum.
Hearing aids should restore F0-synchrony profiles not stimulus spectra — Angle: why conventional hearing aids fail speech-in-noise — restoring the spectrum isn’t the same as restoring the neural code the midbrain actually reads.
Hearing aids should restore F0-synchrony profiles not stimulus spectra — Potential essay angle on why conventional amplification strategies fail: they re-create spectra, not the fluctuation patterns central neurons actually decode.
Talker identity causes more perceptual variation in consonant perception than noise waveform shifts — Angle: ‘your accent is as hard to parse as someone else’s ear’ — the surprising symmetry between speaker-side and listener-side variability in speech perception.
Individual normal-hearing listeners differ greatly yet each is internally consistent in consonant perception. — Angle: the ‘average listener’ as a fiction in psychophysics — averaging data across individual NH subjects may mask the real structure of perception, parallel to averaging across speakers in speech recognition systems.
A minimum 6 dB SNR change is needed to motivate people to seek intervention — Potential essay: the difference between what hearing devices can technically deliver and what actually changes a user’s quality of life — a lesson for any assistive or enhancement technology.
Hearing aids rarely deliver meaningful speech-intelligibility benefits exceeding their detectability — Angle: the case that ‘easier listening,’ cognitive load, and psychosocial engagement — not raw intelligibility — are where hearing aids actually deliver their value.
Making already-clear speech clearer does not motivate people to seek help — Implication for rehab counseling and product design: ceiling effects mean benefit messaging matters most for severe cases; subtle gains in already-good conditions are worthless to users.
Detectable changes in speech SNR correspond to roughly 26 percent changes in word intelligibility — Potential essay angle: what listeners actually attend to when asked to detect hearing improvement crosses domains (SNR vs. intelligibility), relevant to hearing-aid benefit claims.
Otoacoustic emission levels predict which tones listeners weight more heavily — Essay angle: ‘your inner ear has a fingerprint’ — how individual cochlear quirks silently shape what you hear and how you decide.
Loudness perception strongly shapes the decision weights listeners assign to sound components — Could be the seed of an article on how micro-level cochlear variation (audible as OAEs) silently shapes everyday listening behavior.
Gain loss boosts modulation sensitivity in noise despite reduced temporal coding — Angle: ‘the hearing loss that helps you hear’ — the deficits that paradoxically improve supra-threshold perception.
More cochlear compression loss predicts less degradation from added masking noise — The differential-metric-by-masking-bandwidth logic for parsing distinct components of a compound deficit could generalize beyond audition to other graded signal-detection systems. Fiction
Microstimulating one anterolateral belt site biases monkeys’ pitch reports — The notion that stimulating a single tuned cortical site shifts a perceptual report evokes thought experiments about implant-driven perception. Define
Broadband sounds decompose into a slow envelope riding a fast temporal fine structure carrier. — Defines ENV and TFS as the two components of narrowband cochlear filtering.
Aging reduces envelope processing efficiency but not temporal resolution. — TMTF (temporal-modulation-transfer function) and the 20log10 m measure are introduced within a causal interpretation distinguishing efficiency from resolution.
Binaural TFS sensitivity declines with age even when audiograms are normal — Defines the TFS-LF test as a measure of smallest detectable interaural phase difference.
Cochlear hearing loss raises interaural time discrimination thresholds independent of age — Useful distinction of TFSp vs ENVp cues split by stimulus frequency band.
Frequency difference limens for pure tones worsen two to fourfold with age. — Establishes DLF (difference limen for frequency) as a behavioral measure of temporal fine structure processing up to ~4-5 kHz.
A discrimination task can isolate temporal fine structure sensitivity from excitation-pattern cues. — Defines the TFS1 test and the three control experiments that establish it isolates TFS processing from place/excitation cues.
Binaural masking release is 2 to 4 dB smaller in older listeners — Defines BMLD and why a difference-of-thresholds measure partly cancels processing-efficiency confounds across age.
Envelope and temporal fine structure exist at three distinct processing levels — Clarifies the three-tier ENV/TFS framework distinguishing physical signal, basilar-membrane representation, and neural encoding.
The TFS1 test isolates temporal fine structure processing from excitation-pattern cues — Explains the TFS1 test — its logic, construction, and the three experimental validations that it genuinely measures TFS rather than spectral cues.
Masker durations over 25 ms trigger efferent feedback that reduces measured suppression — Defines the medial olivocochlear reflex as a sound-elicited efferent feedback loop reducing OHC gain.
Two-tone suppression is a direct measure of cochlear nonlinearity — Defines two-tone suppression as the nearly instantaneous BM response reduction to one tone when a second tone is present.
Age-related hearing difficulty in noise stems from reduced cochlear nonlinearity, not just thresholds — Defines two-tone suppression as a near-instantaneous reduction in basilar membrane response to one tone caused by a second tone.
Sound durations longer than 25 ms recruit efferent feedback that reduces measured suppression — Defines the medial olivocochlear reflex (MOCR), an efferent feedback loop reducing OHC gain, with its ~25 ms onset latency.
Two-tone suppression is a direct measure of cochlear nonlinearity — Two-tone suppression clearly defined as a nearly instantaneous BM response reduction to one tone in the presence of a second.
Medial olivocochlear reflex requires roughly 20-70 ms of sound to engage. — Explains what MOCR (medial olivocochlear reflex) is and its temporal elicitation threshold.
Suppression-based gain estimates compare on-frequency masker thresholds against off-frequency baselines — Explains how cochlear gain is operationally estimated: the threshold difference between an on-frequency and a more linear off-frequency masker.
Two-down one-up adaptive tracking estimates the 70 percent correct threshold — Explains the adaptive staircase psychophysical method and exactly which point on the psychometric function it tracks.
Basilar membrane suppression can be altered by the sluggish medial olivocochlear reflex — Defines auditory two-tone suppression and the MOCR with its ~25 ms sluggish onset.
Suppressor levels are set just below masker threshold to avoid contaminating forward masking — Clarifies the experimental logic distinguishing suppression from forward masking, a key methodological distinction in psychoacoustics.
Suppressor duration windows distinguish MOCR-mediated gain from cochlear mechanics. — Clarifies the MOCR (medial olivocochlear reflex) timing window: 20 ms too short, 70 ms long enough for full gain reduction.
Early-elderly listeners show temporal sensitivity decline before audiometric loss appears. — Early-elderly here operationally means people whose temporal-structure sensitivities have begun to decline while their hearing thresholds are still near-normal — a useful working definition.
Normal hearing is defined as thresholds below 30 dB across 125-4000 Hz — Operational definition of normal hearing used in the study - a useful methodological anchor.
Transposed stimuli induce phase locking to the envelope at high carrier frequencies — Defines the transposed stimulus technique that extends envelope cues to high-frequency carriers.
Low-frequency auditory stimuli are built from harmonic complexes with 100 Hz fundamentals. — Defines SSMC, the spectrally shaped multicomponent complex used to probe phase-locking-based coding.
Low-frequency harmonic complexes and transposed high-frequency tones isolate phase-locking cues for lateralization tests — Defines the spectrally shaped multicomponent complex and the transposed stimulus, two core stimulus types used in lateralization research.
Notched-noise masking quantifies frequency selectivity as equivalent rectangular bandwidth — Explains how the notched-noise method uses roex-filter fitting to derive ERB, the standard frequency-selectivity metric.
An interaural phase transition in noise evokes a pitch sensation — Defines the Huggins pitch stimulus: an interaural phase transition in an otherwise diotic noise band produces a pitch percept at that center frequency.
A level-increment detection task isolates sensitivity to brief loudness changes. — Defines the LEVEL task procedure used to measure listeners’ ability to detect brief central-level increments in a 400-ms stimulus.
Peripheral monaural coding efficiency partly predicts binaural lateralization ability — Defines ITD and ILD as the principal horizontal localization cues.
High-frequency transposed stimuli make auditory-nerve firing phase-lock to a low-frequency modulator — Explains the transposed-stimulus technique used to elicit phase-locking cues at high frequencies where neural phase locking to the carrier is normally absent.
High-frequency ITD sensitivity is studied via envelope cues using transposed stimuli — Explains the transposed-stimulus technique for delivering temporal/ITD cues at high carrier frequencies via envelope phase-locking.
Low-frequency harmonic stimuli elicit neural temporal-structure coding absent at high frequencies — Describes the SSMC stimulus design used to probe low-frequency temporal processing.
Reading-span tasks measure working memory by interleaving comprehension judgments with word recall — Clarifies the specific dual-task structure of the reading-span working memory measure.
Speech recognition engages working memory when input mismatches stored phonological representations — Explains the Ease of Language Understanding model’s core mechanism: mismatch between perceptual input and long-term phonological representations triggers effortful working-memory processing.
Reading-Span tests score working-memory capacity by final-word recall after plausibility judgments — Describes the Reading-Span task procedure, the operational measure of WMC used throughout the study.
Speech-in-noise difficulty varies with target predictability and background signal-to-noise ratio manipulations — Clarifies what low- and high-predictability sentences mean as experimental stimuli, and speech-modulated noise as background.
Processing-heavy working memory tasks correlate more strongly with noisy speech perception — Clearly defines the storage vs. processing distinction in working memory tasks, a useful technical vocabulary set for a cognition-and-hearing reader.
Working-memory and temporal-processing contributions to intelligibility depend on listening conditions. — RST is referenced as an abbreviation without being spelled out, worth noting as the reading-span task.
Processing-heavy working memory tasks correlate more strongly with noisy speech perception — Clarifies the storage-vs-processing distinction in working memory task design, a core technical concept for this chapter.
Sensitivity to interaural correlation change estimates loss of neural synchrony — Introduces interaural correlation as a non-invasive suprathreshold probe for neural synchrony, a useful methodological concept.
Sensitivity to interaural correlation change estimates loss of neural synchrony — Introduces a specific technique: sensitivity to change in interaural correlation as an estimate of neural synchrony loss, relevant to understanding SiN deficits.
Interaural correlation-change detection is a viable temporal processing measure — Explains how temporal processing is operationalized via interaural correlation thresholds; also note the adaptive tracking procedure.
Working memory is measured by separate storage-based and manipulation-based cognitive tasks. — Distinguishes LNS (storage-dominant WM) from RST (manipulation-dominant WM), giving the reader a clear operational contrast between two standard test instruments.
Vocabulary knowledge and reading comprehension serve as independent cognitive ability measures. — Clarifies what Mill Hill (verbal knowledge) and Nelson-Denny (reading comprehension) each operationalize.
Working-memory tasks differ by emphasizing storage versus active manipulation — Distinguishes storage-heavy from processing-heavy memory tasks by the kind of response demanded.
Emotion in speech can be measured on arousal and valence dimensions rather than categories — Defines the dimensional approach to affect perception as opposed to categorical emotion labeling.
Prosody alone can invert a spoken message’s meaning from positive to negative — Defines prosodic cues as pitch, intensity, and tempo acoustic parameters carrying emotion.
Affective prosody is quantified via F0, intensity, articulation rate, and spectral slope. — The Hammarberg Index is a precise operational definition for spectral slope/vocal effort in prosody analysis.
Hearing loss severity drives listeners toward alternative acoustic cues for arousal — Clarifies the distinction between the prosodic cues used for arousal (multi-cue, hearing-loss dependent) versus valence (F0 only, hearing independent).
Hearing aid use restores arousal perception but shifts cue weighting — Clarifies the arousal/valence distinction as the two perceptual axes of speaker emotion carried by different acoustic parameters.
Interaural place-of-stimulation mismatch shrinks the binaural interaction component’s magnitude. — Introduces BIC, wave-eV, and interaural place-of-stimulation mismatch — load-bearing terms for the whole paper.
The binaural interaction component isolates binaural processing from summed monaural responses — Defines BIC computation and the amplitude/latency operationalization from eABR peaks.
Auditory brainstem responses to cochlear implants are recorded from occipital scalp sites. — Defines the eABR recording setup for cochlear implant research. eABR is never explicitly expanded here.
Interaural electrode mismatch degrades bilateral cochlear implant binaural function. — The BIC (binaural interaction component) is introduced here as a wave-eV-derived EEG measure that shrinks with interaural stimulation mismatch.
Larger BIC amplitude signals place-matched electrodes only when neural survival is equal across ears — Explains the physiological logic underlying BIC as a pairing signal and its key confound.
Finger occlusion enables rapid switching between monaural and binaural listening — Explains the methodological insight that made direct within-trial loudness matching of monaural/binaural possible.
Binaural loudness summation is not a simple sum of the two ears. — Clarifies the conceptual shift from summation models to inhibitory-mechanism models of binaural loudness.
Binaural sound is judged louder than the same sound heard monaurally — Defines binaural loudness summation quantitatively: the monaural stimulus must be 3-10 dB more intense to match diotic loudness.
Loudness constancy experiments compare speech and noise across headphone, loudspeaker, and virtual presentation — Defines the experimental paradigm: monaural reference vs binaural test with matched long-term excitation for speech and noise.
Virtual acoustic presentation uses convolved room impulse responses with headphone inverse filtering — Clarifies the technique: convolving with manikin-recorded binaural impulse responses plus inverse filters to flatten headphone response.
Binaural loudness summation levels depend strongly on how monaural presentation is achieved. — Defines the ‘summation effect’ as the measured decibel difference in perceived loudness between monaural and binaural presentation.
Loudness equality between monaural and binaural stimuli is measured by bracketing two adaptive thresholds. — PSLE (point of subjective loudness equality) measurement method is spelled out here, useful as procedural grounding for later loudness comparisons.
Modelling shows 52 percent loudness drop at occluded ear is inadequate alone — Distinguishes older additive binaural loudness models from newer inhibitory models.
Spatially separated speech sources improve intelligibility through binaural release from masking. — Defines BMLD and spatial release from masking clearly — the two core terms driving this study.
Binaural unmasking depends on masker duration but not signal duration — Clarifies what a BMLD (binaural masking level difference) mechanism would predict for local spectro-temporal deletion — a specific testable prediction.
Local SNR is computed per 23 ms one-ERB time-frequency unit — Defines the T-F unit granularity (1 ERB wide, 23 ms long) used for local SNR computation, which is the foundational operationalization for the whole chapter.
Auditory frequency analysis uses equivalent rectangular bandwidth units of fixed duration — Explains what an ERB-scaled time-frequency unit consists of: 23 ms length, 1 ERB bandwidth.
Binaural unmasking cannot explain spatial release from masking in multispeaker situations — Defines spatial release from masking and BMLDs with concrete in-phase/reversed-phase threshold contrast.
Speech perception depends on brief time-frequency glimpses where target energy exceeds masker energy. — Defines ‘masked target audibility’ and the ‘glimpsing model’, a core psychoacoustic tool.
Ideal binary masking isolates time-frequency tiles where target dominates masker — Concrete implementation details of ideal binary masking — 128 channels, 80-8000 Hz, 20-ms windows.
Quiet-only performance measures miss masking effects that reduce speech redundancy. — Introduces ‘masked target audibility’ and the glimpsing model concept as a way to quantify available target information in speech mixtures.
Ideal binary masks isolate glimpses where target exceeds masker energy. — Explains exactly how ‘glimpsed stimuli’ are constructed tile-by-tile in both time and frequency.
Spatially separated speech sources improve intelligibility through binaural release from masking. — Defines spatial release from masking and distinguishes its three candidate mechanisms, the conceptual core of the chapter.
Symmetric masker paradigms were designed to remove head-shadow benefits from spatial release from masking — Clarifies what the symmetric-masker configuration is and the methodological reason it exists.
Sensation level and per-band audiogram equalization are distinct ways to control audibility — Clarifies the methodological distinction between sensation level (SL, measured relative to each listener’s SRT) and frequency-band-by-band audibility equalization.
Speech masking thresholds are estimated adaptively by adjusting relative talker levels. — Defines TMR and the one-up/one-down adaptive tracking method used to find masked thresholds.
Masked speech thresholds are measured as the target-to-masker ratio yielding 50 percent correct — Defines TMR operationally: the ratio at which a listener achieves 50% correct in adaptive tracking.
Speech perception depends on brief time-frequency glimpses where target energy exceeds masker energy. — Introduces masked target audibility and the glimpsing concept: regions where target beats masker in the time-frequency plane.
Simulated hearing loss isolates audibility from other hearing-loss effects — Full audibility restoration (100 percent gain) would exceed loudness discomfort, capping achievable HI amplification.
Target audibility in speech mixtures requires measuring glimpsed time-frequency regions, not quiet thresholds — Defines ‘masked target audibility’ and the glimpsing measurement approach in the time-frequency plane.
Glimpsing models quantify target audibility by time-frequency tiles where target exceeds masker — Defines the glimpse/glimpsing model approach to computing target availability in speech mixtures.
Hearing-impaired listeners’ poor spatial hearing may not reflect actual spatial-processing deficits. — Clarifies that the glimpsed-mixture paradigm separates availability of target information from binaural spatial processing as experimental factors.
Spatial release from masking is measured by adapting target-to-masker ratio to threshold — Defines the LiSN-S spatial listening paradigm and how SRM is operationalized as the difference in TMR threshold between colocated and spatial-separated maskers.
Spectral contrast degradation in CIs impairs formant detection for speech — Defines spectral contrast — the central concept of this chapter — as peak-to-valley level difference and explains the causal chain from electrode-field overlap to impaired speech cues.
N-of-M coding strategies enhance spectral contrast by retaining only largest-amplitude bands — Explains the N-of-M/ACE strategy mechanism that any reading of this chapter depends on.
Spectral modulation threshold is the smallest detectable spectral contrast in a stimulus. — Defines spectral modulation threshold as the smallest detectable spectral contrast, a key metric in CI spectral resolution research.
MPI vowel-identification models add Gaussian jnd noise scaled from SMT thresholds — Describes how the model bridges a psychophysical measure (SMT) to a cognitive outcome (vowel identification) via internal noise variance.
Cochlear implants give good speech recognition in quiet but poor performance in noise — Establishes the auditory background the whole chapter’s normative-mechanism question builds on.
Spectral contrast degradation in CIs impairs formant detection for speech — Defines spectral contrast as the peak-to-valley level difference in the spectrum, the core concept of the chapter.
Spectral contrast coding can be enhanced by attenuating valleys while leaving peaks fixed — Defines what spectral contrast enhancement (SCE) actually does mechanistically: valley attenuation with peaks held constant, controlled by a single SCE factor parameter.
Spectral contrast coding can be enhanced by attenuating valleys while leaving peaks fixed — Defines the SCE mechanism: valley attenuation with peaks kept constant, controlled by a single SCE factor.
Spectral resolution closely predicts vowel and consonant recognition in CI users — Defines spectral modulation threshold (SMT) as smallest detectable spectral contrast in a ripple stimulus — a key psychoacoustic measure.
Adaptive 3-down 1-up tracking with 2 dB steps estimates spectral ripple thresholds — Describes the adaptive procedure for SMT measurement — useful methodological detail for psychoacoustics.
Just-noticeable-difference noise, not map settings, drives vowel-identification variability between cochlear implant users — Clarifies that ‘jnd noise’ in the MPI model is the internal perceptual noise on spectral-contrast features, functionally distinct from the user’s threshold/map electrodogram differences.
Spectral modulation thresholds quantify the smallest detectable spectral contrast — Defines the spectral modulation threshold (SMT) metric used to measure spectral resolution in implant listeners.
Cochlear implant spectral smearing flattens formant peaks needed for speech recognition — Defines spectral contrast as the level difference between spectral peaks and valleys, and links formant (spectral dominance) peaks to intelligibility.
Spectral contrast coding can be enhanced by attenuating valleys while leaving peaks fixed — Defines what the SCE processing stage does and the single control parameter (SCE factor) that governs its strength.
Spectral ripple thresholds are measured with a forced-choice flat-versus-modulated comparison — Defines the SMT/spectral ripple paradigm in concrete procedural terms.
Spectral ripple test measures spectral modulation detection thresholds in cochlear implant users — Defines the spectral ripple test procedure with specific parameters (350-5600 Hz band, random phase noise, 60 dB SPL, 400 ms duration).
Output-based rather than input-based inhibition creates self-reinforcing asymmetric suppression. — Clarifies the design principle: inhibition keyed to output (post-compression) rather than input yields an amplifying cascade absent from input-keyed circuits.
Medial olivocochlear efferents actively modulate cochlear gain during binaural listening — Defines the MOC (medial olivocochlear) efferent system and its reflexive inhibition of cochlear mechanical gain.
Stronger contralateral sound causes greater efferent inhibition and more linear cochlear response — Defines the medial olivocochlear reflex as frequency-matched contralateral inhibition that reduces cochlear compression.
The CI compressor functionally replaces cochlear mechanical compression — Draws the functional analogy between a cochlear implant’s back-end compressor and healthy cochlear compression.
Contralateral efferent inhibition is nonlinear and output-dependent, amplifying asymmetries — Clarifies the mechanism by which output-dependent (rather than input-dependent) inhibition creates positive feedback that sharpens the neural contrast between ears.
Medial olivocochlear reflex inhibition varies by cochlear location — Defines MOCR (medial olivocochlear reflex) apical/basal gradient and the half-octave frequency shift between elicitor spectrum and inhibited region.
Cochlear implants enable experimental control of the olivocochlear reflex unavailable in normal hearing — MOCR (medial olivocochlear reflex) effects are introduced functionally via what they do — modulating compression — before any formal definition section.
The contralateral efferent reflex inhibits cochlear compression at matched frequencies — Clarifies that MOCR inhibition acts on-frequency between ears, proportional to cochlear output energy rather than raw acoustic level — a physiological mechanism stated as engineering assumption.
Signal levels in cochlear implant research are measured as RMS in full-scale decibels. — Defines dBFS specification used throughout cochlear-implant signal processing work.
Deactivating pitch-redundant electrodes may benefit some implant users but not others — Introduces n-of-m strategies (ACE or SPEAK) as a device-specific processing mode where the processor selects n of m available electrodes each cycle.
N-of-m stimulation strategies resist improvement from electrode deactivation. — Defines n-of-m coding strategy: only the n highest-amplitude channels are stimulated each cycle.
Cochlear implant re-mapping deactivates poorly placed electrodes to improve spectral resolution. — Explains what electrode-neuron interface (ENI) means and how it causes channel corruption through broad electrical fields.
Cochlear implant re-mapping deactivates poorly placed electrodes to improve spectral resolution. — Defines electrode-neuron interface (ENI) as the quality of electrode placement relative to surviving neurons.
Electrode pairs are judged discriminable by binomial significance on pitch ranking — Defines a pass/fail criterion for ‘discriminable’ electrode pairs based on binomial statistics.
A sealed box can simulate loudspeaker listening for cochlear implant testing — Explains what an Otocube is and why it is used for CI testing.
Pitch ranking relies on two-interval forced choice with random order assignment — Details the two-interval two-alternative forced choice pitch ranking method.
Spectral ripple thresholds are measured with a forced-choice flat-versus-modulated comparison — Details the exact stimulus construction and adaptive procedure of the spectral ripple task.
Word-discrimination tests use minimal pairs to isolate consonant and vowel contrasts. — Defines the CAPT and the minimally-contrastive word design used in speech tests.
Electrode deactivation reallocates frequencies and can harm rather than help — Clarifies n-of-m strategy: n channels with highest amplitudes stimulated out of m possible channels.
Word discrimination testing uses minimal pair contrasts to reveal spectral resolution — Defines the CAPT test structure and its rationale in concrete terms — useful anchor for understanding how spectral hearing is behaviorally assessed.
Word discrimination can be measured with a picture-pointing forced-choice test using minimally contrastive pairs. — Defines the CAPT test and its stimulus structure clearly.
Speech masking in noise stems from inherent temporal-envelope modulations rather than overall noise energy. — Introduces the distinction between ‘inherent temporal-envelope modulations’ and ‘overall noise energy’ as competing accounts of speech masking.
Filter widening preserves Rayleigh envelope statistics while electrode-current summation does not. — Clarifies the technical distinction between broadening filters (preserves Rayleigh distribution) versus summing electrode currents (breaks Rayleigh statistics) — the core mechanism separating HI from CI envelope degradation.
Hearing loss reduces the effect of inherent noise fluctuations on speech masking. — Establishes SL (sensation level) vs SPL (sound pressure level) as distinct comparison methods controlling for overall level effects.
Envelope vocoders simulate cochlear implant processing by splitting sound into amplitude-modulated subbands — Explains the technical method of envelope vocoding used to simulate CI processing — useful for understanding what CI users actually hear via amplitude-modulated envelopes rather than fine structure.
Both anodic and cathodic stimulus phases can elicit auditory nerve responses — Defines the standard symmetric biphasic pulse configuration used in cochlear implant stimulation.
Inferior colliculus electrical responses mirror acoustic response morphology — Defines the N1-P2 amplitude convention and the dynamic-range metric used throughout the study.
Pulse-shape efficiency ordering holds across animal and human studies — Clarifies how pseudomonophasic (with inter-phase gap) vs symmetric vs triphasic pulse shapes rank by current efficiency.
Charge-balanced asymmetric waveforms reduce the current needed to elicit hearing in cochlear implant users. — Clarifies why charge-balanced asymmetric waveforms exist (safety + polarity dominance) and their efficiency advantage over symmetric pulses.
Sisomycin completely destroys cochlear hair cells and causes progressive neural degeneration — Establishes sisomycin as a standard chemical method for deafening/exciting spiral ganglion degeneration in the guinea pig model.
Asymmetry in cochlear implant pulse shapes biases one polarity of stimulation to dominate. — Establishes the pulse-shape/polarity naming scheme (SYM, RPS, RPS-IPG, TRI with C/A suffixes) used throughout the study.
Deconvolving the eCAP reveals two separate auditory nerve firing components. — Defines eCAP and introduces the convolution model approach that lets firing properties be extracted from the compound potential.
A compound action potential equals single-fibre waveform convolved with spike latency distribution — Core definitional framing: the CAP decomposes into a unit response convolved with a spike-timing histogram, tying physiology to recorded waveform.
Electrically evoked compound action potentials show two peaks consistent with neural refractory repriming — Explains what a2/a1, the ratio of two CDLD components, means physiologically: two fibre subpopulations or repeated firing separated by the absolute refractory period.
The second CDLD peak reflects repeated firing of the same auditory neurons — CDLD is used repeatedly here but never expanded on this page; the passage relies on understanding the compound discharge latency distribution metric.
A compound neural response equals single-fiber waveforms convolved with the discharge timing distribution — Defines the mathematical identity relating macro recording to summed single-fiber activity — the fundamental modeling assumption.
Deconvolution of neural responses inherently amplifies high-frequency noise — Explains why deconvolution acts as an inverse low-pass filter and the consequent noise-amplification trade-off.
Electrically evoked auditory nerve responses fit Gaussian rather than gamma latency distributions — Clarifies why the compound latency distribution is Gaussian despite individual fibre responses being gamma-shaped — a convolution effect.
Listeners with identical hearing thresholds differ in suprathreshold loudness perception — Introduces the MCPF as a probabilistic representation relating stimulus level to categorical loudness.
A multi-category psychometric function maps response category probabilities onto stimulus level — Defines MCPF as an extension of the classic psychometric function to multiple response categories.
Boundary-curve slope in a categorical loudness function encodes perceptual category certainty — Defines how the multi-category psychometric function is read: slope of category boundary curves = listener certainty, vertical distance between curves = category probability.
Categorical loudness scaling offers a clinically rapid, low-training test of suprathreshold hearing — Introduces and defines categorical loudness scaling (CLS) as a measurement technique.
Categorical loudness data is probabilistic, not reducible to a median response curve. — Introduces the multi-category psychometric function (MCPF) as a probabilistic alternative to the standard categorical loudness scaling function.
Loudness judgments at a fixed level vary probabilistically across categories — The multi-category psychometric function (MCPF) is introduced as a family of curves mapping category probabilities across stimulus level.
Loudness judgments at a fixed level vary probabilistically across categories — Defines the multi-category psychometric function as a distribution over response categories, and establishes that loudness judgments aren’t fixed to a single category.
An 11-category loudness scale decomposes into 10 parameterized boundary curves. — Explains the parameterization scheme: 4-parameter logistic curves, with 10 category boundaries for an 11-category scale.
Modeling the multi-category psychometric function markedly improves loudness estimation accuracy. — Clarifies the MCPF catalog method: PCA decomposes listener-parameter variability into eigenvectors whose sampled weightings reconstruct the function catalog.
Categorical loudness scaling tests present eleven labeled response categories as on-screen bars. — Explains what the 11-category categorical loudness scaling (CLS) instrument actually looks like and how a participant responds.
Loudness response variability is informative signal, not noise to discard — Defines the multi-category psychometric function (MCPF) representation that treats response variability as structured data.
Categorical loudness scaling delivers rapid clinical loudness estimates without a reference. — Introduces CLS as a specific loudness-measurement technique with its clinical affordances.
Loudness judgments at a fixed level vary probabilistically across categories — Introduces MCPF as a model of the probabilistic nature of categorical loudness responses across stimulus levels.
Categorical loudness scaling uses labeled bars from Not Heard to Too Loud — CLS is categorical loudness scaling, where loudness is judged against discrete labeled categories rather than matched or rated on a continuum.
Categorical loudness scaling uses labeled bars from Not Heard to Too Loud — Describes the categorical loudness scaling (CLS) experimental setup—eleven bars, labels, fixed-level presentation protocol.
Loudness depends on more than physical sound intensity alone — Clarifies that loudness is the perceptual correlate of sound pressure level, not reducible to physical intensity.
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Distinguishes the quadratic intensity-activation versus linear loudness-activation relationship explicitly.
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Clarifies that the representation encoded in auditory cortex is closer to loudness (percept) than to intensity (physical stimulus), a key distinction in hearing physiology.
Loudness depends on more than physical sound intensity alone — Clarifies that loudness is the perceptual correlate of intensity but is modulated by duration, spectral content, temporal modulation, task, context, and hearing status.
Cortical responses to sound are stronger from the opposite ear than the same-side ear — Clarifies the contralateral dominance of auditory cortical responses, a foundational feature of auditory pathway organization.
Loudness growth is linear up to 80 dB then steepens near 90 dB — Establishes the behavioral baseline against which the fMRI activity is interpreted.
Posterior medial Heschl’s gyrus shows the strongest sound-intensity response — Maps the loudness-relevant region onto a named cortical region, useful for anchoring later anatomical discussions.
Perceived loudness grows nearly linearly with intensity until steepening near 90 dB SPL — Sets a concrete reference frame for what categorical loudness curves look like before the fMRI data is interpreted.
Auditory cortex activation linearly reflects perceived loudness rather than physical intensity — Distinguishes quadratic intensity coding from linear loudness coding in BOLD responses.
Pooling subjects before ICA decomposition avoids biased between-group network comparisons — A clear methodological distinction: separate per-group ICA vs pooled ICA affects the validity of group comparisons.
Tinnitus affects 5-10 percent of the population yet its causes remain largely unknown — Provides a crisp definition: tinnitus is sound perception without an external acoustic source.
Resting-state networks reveal functional connectivity patterns without any task — Defines resting-state networks and the ICA technique that identifies them.
Tinnitus severity is measured through distinct handicap, distress, and coping inventories — Useful if reader needs to distinguish THI/TRQ/TCSQ constructs in future hearing research.
Task-subtraction fMRI paradigms cancel out tinnitus-related brain activity — Explains why resting-state fMRI is methodologically preferred for studying tonic percepts like tinnitus — the subtraction logic only surfaces task-modulated differences.
Task-subtraction fMRI paradigms cancel out tinnitus-related brain activity — Explains why subtraction-based task fMRI is structurally blind to chronic, condition-invariant neural activity — a key methodological insight for understanding tinnitus’s physiology.
Neural ITD coding could use either equal conduction latencies or phase matching. — Lays out the two coding strategies (latency vs. phase) that the whole chapter discriminates between.
Auditory nerve spike PSTH peaks recur at 2π phase intervals tied to best frequency — Clarifies what ‘2π phase interval’ means: PSTH peaks separated by the inverse of best frequency.
The auditory system uses coarse physical delay lines plus fine phase-based matching for ITD — Explains the coarse/fine two-stage mechanism for achieving microsecond interaural time precision.
Microsecond ITD precision arises from phase-delay matching, not absolute latency — Clarifies the two coding strategies — equal absolute latency vs. phase-matched (modulo 2π) delays — that distinguish how coincidence detectors achieve timing.
Neural delay matching proceeds by coarse developmental matching followed by precise activity-dependent refinement — Explains what ‘matching modulo 2π’ and the two-step delay-matching hypothesis actually mean.
Interaural time difference maps derive from precise phase delays, not absolute latencies — Clarifies the two distinct neural strategies by which coincidence detectors might encode interaural time differences: matched absolute latency vs. matched phase only.
The periodic ringing of cochlear inputs sustains phase-based ITD coding for nonperiodic sounds — Distinguishes the two competing hypotheses for how microsecond ITD precision is achieved: matching absolute conduction latencies versus matching only relative phase delays.
MSO and LSO neurons have opposite optimal interaural phase responses — The excitatory-excitatory vs. excitatory-inhibitory wiring explains each nucleus’s preferred ITD range.
Stimulus design can minimize monaural cues to isolate pure binaural phase processing — IPM (interaural phase modulation) is defined here as periodic switching of which ear leads, holding the IPD magnitude constant.
Slow IPM sweeps evoke transient P1-N1-P2 responses while faster rates evoke steady-state following responses. — Defines the IPM-FR (interaural phase modulation following response) as the steady-state response at the IPM rate, contrasting it with the transient P1-N1-P2 to each transition.
Abrupt ITD changes elicit auditory-evoked responses with distinct latencies — Defines the P1-N1-P2 evoked-response complex and how ITD changes elicit it, and clarifies the diotic vs statistically-independent comparative method.
Envelope ITDs at high carrier frequencies are tested using transposed tones. — Transposed envelope stimulus technique for presenting ITDs at high carrier frequencies.
Abrupt interaural phase changes evoke P1-N1-P2 cortical responses — Defines IPM-FR: the steady-state evoked cortical response that follows interaural phase modulation at the modulation rate.
Neural ITD encoding follows distinct temporal-fine-structure vs envelope mechanisms at low vs high frequencies. — Defines IPM-FR (interaural phase modulation following-response) as an EEG measure of binaural ITD sensitivity.
MSO and LSO neurons have opposite optimal interaural phase responses — Clarifies the functional distinction between excitatory-excitatory MSO neurons and excitatory-inhibitory LSO neurons for binaural processing.
ITD sensitivity can be measured via auditory-evoked potentials using sound-onset changes — Explains what diotic sounds are — identical input at both ears — key terminology for the hearing chapter.
Phase transitions hidden at envelope minima decrease monaural detection of interaural cues — Explains what IPM (interaural phase modulation) is and why transitions are placed at envelope minima — a technique ensuring the EEG response measures binaural rather than monaural processing.
Envelope ITDs in high-frequency tones are generated with transposed stimuli carrying a second-order AM — Transposed stimuli let experimenters deliver ITD information in the envelope of high-frequency carriers, evoking perception like low-frequency TFS ITDs.
Hearing loss impairs pinna-cue discrimination for vertical sound localization — Defines pinna cues as direction-dependent spectral patterns above ~5 kHz enabling vertical localization — a prerequisite concept for the whole study.
Unconverged adaptive measurement runs are excluded as unusable data — Clarifies that adaptive MAA measurements are validated by whether performance plateaus — a methodological criterion in psychoacoustic data exclusion.
Minimum Audible Angle measures the smallest discriminable angular change — Defines MAA as the smallest angular change from a reference that can be discriminated, a key psychoacoustic measure.
Adaptive staircase tracks the 70.7 percent correct point on the psychometric function — Defines the 3-AFC one-up-two-down staircase tracking the 70.7% point — classic psychoacoustic procedure.
Vertical-plane MAA discrimination is tested with three-interval forced choice — Concrete operationalization of the 3-AFC task for spatial angle discrimination.
Vertical-plane discrimination may improve when pinna cues are shifted to a perceptible band — Establishes MAA (Minimum Audible Angle) as the study’s core outcome measure in the vertical plane.
Pinna spectral notch frequency increases with sound elevation in the median plane — Explains the ‘pinna cue’ concept — the spectral notch whose frequency encodes elevation, central to understanding how the outer ear itself shapes localization.
One-up-two-down adaptive staircases can report lower thresholds than other estimation methods — Defines the one-up-two-down staircase and notes its bias toward lower thresholds versus Levitt and Neher methods.
Hearing-impaired listeners need a 15 dB UCL-to-threshold gap for audible amplified stimuli — UCL and HTL defined in context as the usable dynamic-range criterion for recruiting hearing-impaired subjects into an experiment.
Adaptive staircase averaging of lower reversal points yields lower MAA thresholds than Levitt’s method. — Clarifies the 3-AFC staircase procedure and its 70.7%-correct tracking point.
Minimum Audible Angle is measured with a 3-AFC adaptive staircase task — Concretely defines the MAA experimental procedure used throughout this study.
Pinna cues can be transposed one octave down for hearing-impaired listeners — Explains the technique of transposing pinna spectral cues down an octave to reach frequency regions where hearing-impaired listeners retain sensitivity.
A low-frequency spectral notch in pinna cues shifts upward with elevation angle — Explains the pinna-notch mechanism for vertical sound localization: a moving low-frequency notch encodes elevation angle.
Natural hearing in the wild involves constantly changing binaural cues rather than static ones — Concise map of which binaural cue carries which spatial dimension: ITD for azimuth, ILD for azimuth/elevation, spectral peaks/notches for elevation.
Sound source location is encoded by distinct dynamic binaural cues — Clarifies the mapping of ITD, ILD, and spectral notch cues onto azimuth vs elevation.
Sound source azimuth is coded by interaural time and sound elevation by spectral cues — Useful crisp anchor distinguishing which binaural cue codes which spatial dimension (ITD=azimuth, ILD=azimuth/elevation, spectral notches=elevation).
A minimal tone cloud can isolate statistical predictability as the sole cue — Defines the key construct: ‘solely statistical predictability’ as the operational core of an auditory texture.
Natural sound identity is defined by its statistical probabilities, not exact patterning — Defines ‘auditory textures’ as sounds whose identity comes from statistics rather than exact spectrotemporal patterning.
Standard d-prime must be modified for time-variable change detection tasks — Explains a methodological subtlety: in dynamic-decision paradigms a single trial counts as different outcomes at different analysis times.
Profile analysis compares static spectra while texture change detection needs dynamic statistics — Distinguishes profile analysis from dynamic statistical-integration paradigms by three concrete differences.
Auditory change detection integrates statistical evidence accumulated over time — Explains the ‘statistical integration’ framework: detection improves with observation time but saturates as estimation converges, and bigger changes need less estimation precision.
Standard signal-detection measures fail for time-evolving detection tasks — Explains why a time-dependent d’ measure is needed; classical d’ assumes the whole trial outcome is known at decision time.
Time-dependent signal detection measures are needed when trial outcomes arrive dynamically — Explains why classical d prime fails in dynamic decision paradigms and how a time-dependent d-prime resolves the censoring problem.
Randomly timed tones model how new sound sources appear in a natural scene — Defines the change-detection stimulus as a stochastic analog of a sound source appearing in a natural auditory scene.
Acoustic textures can be modeled as tone clouds predictable only through their statistical properties — Defines what an ‘acoustic texture’ means operationally: a tone cloud whose predictability derives only from statistical properties of its spectrotemporal structure.
Sensitivity to statistical changes rises over time from stimulus onset — Explains the time-resolved d’ computation for change detection, where hit and false-alarm rates are both binned as functions of time since stimulus onset.
Sound textures are predictable only through their statistical spectrotemporal structure. — Establishes what makes a stimulus a ‘texture’ in auditory research: predictability residing only in statistical properties, not in individual tokens.
Statistical change detection requires predicting stimulus statistics rather than sequence events — Introduces a minimal paradigm for studying statistical hearing: tone-pips constrained only by a frequency marginal probability, clean of other acoustic structure.
Natural sounds are identified by their statistical properties, not by predictable patterns of elements — Defines auditory textures as sounds whose identity is carried by spectrotemporal statistics rather than deterministic elements — a key concept for the reader’s physiology/psychoacoustics focus.
Change detection in stochastic textures depends on statistical prediction rather than spectrotemporal predictability — Contrasts deterministic spectrotemporal predictability with statistical prediction as two distinct detection mechanisms, a useful working distinction.
Signal detection metrics can be computed as time-varying functions of stimulus onset — Defines time-resolved d’(t) as a function of onset-to-change latency using cumulative Gaussian inversion of hit and false-alarm rates.
Auditory chimera technique swaps envelope and fine structure across frequency channels — Explains the auditory chimera technique and its Hilbert-transform-based envelope/fine-structure split — a core method for separating the two cues.
Impaired temporal resolution degrades fine-structure pitch processing more than reduced frequency selectivity does — Distinguishes fine structure (FS) from temporal envelope (E) cues and ANSD from SNHL — clarifies the core technical scheme of the chapter.
Impaired temporal resolution degrades fine-structure pitch processing more than reduced frequency selectivity does — Clarifies the ANSD vs SNHL distinction: ANSD impairs temporal resolution, SNHL impairs frequency resolution.
ANSD impairs temporal processing while keeping cochlear amplification function intact — Defines ANSD as a dys-synchrony disorder with normal cochlear amplification but impaired temporal processing.
Temporal envelope suffices for speech while fine structure carries pitch information — Establishes the E/FS decomposition of acoustic signals and which is sufficient for which perceptual function.
Normal hearing relies on fine structure for tones; neural disease forces reliance on envelope — Distinguishes temporal fine structure (FS) from envelope (E) and shows which cue each hearing group depends on.
Impaired temporal resolution degrades fine-structure pitch processing more than reduced frequency selectivity does — Clarifies how E (envelope) and FS (fine structure) cues map differentially onto temporal vs frequency resolution deficits across two hearing disorders.
Sensorineural and auditory neuropathy hearing losses impair opposite spectral and temporal abilities — Introduces SNHL and ANSD as two hearing disorders that dissociate frequency from temporal resolution — core terminology for the reading purpose.
Auditory chimeras swap spectral envelopes between signals to isolate cue contributions — Explains the auditory chimera technique used to dissociate temporal envelope and fine structure cues.
Mandarin distinguishes word meaning through four syllable-level pitch contour tones — Defines the four Mandarin lexical tones by their F0 contour patterns — useful background for any tone-language perception discussion.
Auditory chimera technique swaps envelope and fine structure across frequency channels — Explains the auditory chimera technique as a tool for independently manipulating temporal envelope (E) and fine structure (FS) cues.
Hilbert decomposition splits sound into envelope and fine structure for controlled re-synthesis — Concrete step-by-step description of how chimeric auditory stimuli are physically synthesized via Hilbert transform.
Manipulating envelope and fine structure separately reveals their competing roles in tone perception. — Clearly explains the E/FS chimerization technique — swapping Hilbert-derived envelopes across channels to dissociate envelope from fine-structure cues.
Off-frequency tone detection benefits from envelope modulation cues produced by beating — Explains the mechanism whereby off-frequency beating between tone and narrowband masker introduces higher-frequency envelope cues detectable beyond peripheral filtering.
A tone matching the masker-signal spectral distance degrades beating-cue detection more than other interferers — MDI = modulation detection interference; defines the phenomenon of modulation spectral selectivity in plain terms.
Interaural phase disparities lower masked thresholds by up to 25 dB for overlapping targets — Defines binaural masking level difference (BMLD) and its sharply quantified effect: 25 dB threshold reduction from a pi interaural phase shift.
Interferers partially rather than fully suppress the modulation cues used for detection — Explains what Modulation Detection Interference (MDI) is and its across-frequency property.
Modulation in off-frequency channels can suppress on-frequency modulation processing — Distinguishes energy-based vs modulation-based across-channel models — a useful conceptual framework for understanding masker-interferer psychacoustics.
Across-channel masking is modeled either by energy subtraction or by suppressing adjacent modulation frequencies. — Distinguishes two rival modeling mechanisms for across-channel processing: energy-based subtraction versus modulation-frequency suppression.
Binaural processing can be modeled by equalisation-cancellation followed by low-pass filtering — Identifies the standard binaural model structure: equalization-cancellation then temporal integration via an 8 Hz low-pass.
Across-frequency modulation cues contribute to signal detection in masking. — Defines the binaural masking pattern paradigm and introduces interfauernce-based modulation cue hypothesis in one coherent frame.
Modulation-based analysis underlies the AC model’s matching of measured thresholds. — Distinguishes ‘modulation-based AC model’ from ‘energy-based AC model’ as the mechanism accounting for interferer effects.
Across-channel processes can be modeled as energy subtraction or modulation interference — Distinguishes the energy-based AC model from the modulation-based AC model, two competing mechanistic accounts of across-channel processing.
SNR-based intelligibility models require imposed floor and ceiling limits. — Explains what the -15/+15 dB floor and ceiling in the SII standard are and where they come from.
The Speech Intelligibility Index fails for sharply filtered stimuli. — Clarifies the boundary of SII applicability — it was not designed for sharply filtered speech or noise.
Speech intelligibility saturates beyond certain signal-to-noise ratio bounds. — Introduces SNR and the floor/ceiling concept central to the chapter.
The SII band importance function positions roughly equal speech importance at 1400 Hz. — Clarifies how the SII (Speech Intelligibility Index) band importance function guides filter cut-off choice in speech perception experiments.
Semantically unpredictable sentences with fixed keyword slots assess speech intelligibility without context cues. — Defines the specific type of speech test material used: semantically unpredictable sentences with four scored keywords.
Speech models incorrectly assume uniform SNR importance within fixed floor and ceiling bounds. — Introduces the SNR floor/ceiling concept used throughout the chapter.
The SII model mispredicts intelligibility of sharply filtered speech and noise — Clarifies what the Speech Intelligibility Index (SII) assumes versus what measured data show.
The 1400 Hz cut-off equalizes spectral contribution based on the SII importance function — Defines the SII band importance function as the basis for choosing a filter cut-off frequency around 1400 Hz.
Stationary speech-shaped noise can be synthesized by phase-randomizing a speech spectrum — Explains the construction of speech-shaped noise maskers, a standard psychoacoustic stimulus.
Cochlear compression operates via a gain control with finite attack and release times. — Defines hysteresis in the BM response envelope and what it implies about the temporal dynamics of cochlear gain control.
Cochlear mechanical compression is not instantaneous but has finite timing — Defines hysteretic compression as evidence of a gain control mechanism with finite attack and release times.
Beating tone pairs can reveal cochlear compression dynamics at single BM sites — Introduces ‘zwuis stimuli’ and the periodic-cancellation beat-pair technique as measurement tools.
Low-frequency sound’s after-effects arise from calcium homeostasis in outer hair cells — Defines the Bounce phenomenon as slow oscillating changes in cochlear function after low-frequency sound offset, traced to OHC modulation.
Outer hair cells encode displacement and therefore stay responsive at low frequencies. — Clearest statement of why OHC (displacement-coupled) vs IHC (velocity-coupled) determines low-frequency limits.
The SOAE Bounce reflects a calcium-driven homeostatic readjustment in outer hair cells. — Defines the mechanism behind the Bounce phenomenon in terms of OHC calcium homeostasis.
The Bounce is caused by OHC calcium homeostasis disruption, not stria damage — Clarifies what the Bounce phenomenon is and its hypothesized cellular mechanism.
Low-frequency sound triggers larger cochlear responses than comparable higher frequencies. — Defines the IHC vs OHC transduction distinction (velocity vs displacement sensitivity) which is load-bearing for the whole chapter.
Loud low-frequency tones cause a characteristic two-phase threshold change called the bounce — Defines the psychoacoustic term ‘bounce’ as bimodal threshold change: sensitisation then equal-duration desensitisation.
Otoacoustic emissions non-invasively probe outer hair cell function in humans. — Defines SOAEs and DPOAEs as measurement tools for OHC function.
Low-frequency exposure measurably alters cochlear amplifier gain via outer hair cell activity — Defines the Bounce phenomenon: cyclic changes of inner-ear function indicators after LF stimulus offset.
Wiener-kernel analysis can separate excitatory from suppressive components in auditory nerve responses — Introduces Wiener-kernel system identification as a method for decomposing excitatory and suppressive sub-kernels in broadband responses.
Noise-damaged cochleas keep low-side suppression normal while high-side thresholds rise — Defines two-tone suppression and the Wiener-kernel decomposition into excitatory versus suppressive sub-kernels.
Multiple distinct neural mechanisms can underlie what is measured as suppression. — Clarifies that ‘suppressive’ in their analysis is an umbrella term covering mechanical suppression, adaptation, and refractoriness.
Suppression regions flank excitation in time-frequency and predate it on-CF — Explains what a second-order Wiener-kernel decomposition reveals — excitatory vs suppressive sub-kernels — useful groundwork for the psychoacoustic concept of suppression.
Peripheral hearing damage may trigger compensatory excitation-inhibition shifts in brainstem — The passage defines ‘central gain change’ as an altered excitation/inhibition balance in brainstem nuclei following peripheral damage.
Two-tone suppression reflects saturation of outer-hair-cell receptor currents. — Defines suppression operationally as suppressive interactions between acoustic-stimulus components rooted in outer-hair-cell saturation — the key term for the reader’s hearing physiology study.
A normalized ratio quantifies the balance of excitation and suppression in auditory fibers — Defines the excitatory-suppressive ratio metric, a scalar descriptor of each fiber’s suppressive balance.
Wiener-kernel analysis can separate excitatory from suppressive components in auditory nerve responses — Explains the systems-identification method built on singular-value decomposition of the second-order Wiener kernel h2.
Second-order Wiener kernels can be decomposed into excitatory and suppressive components via singular-value decomposition. — Defines how h2 kernels get parsed into excitatory vs suppressive sub-kernels via SVD.
Suppression measured via Wiener kernels bundles together three distinct neural mechanisms — Defines what ‘suppressive influences’ means in this analysis — a conflation of mechanical suppression, adaptation, and refractoriness.
Second-order Wiener kernels reveal cochlea suppression overlapping excitation in time and frequency — Explains how Wiener-kernel decomposition yields separate excitatory and suppressive sub-kernels, a technical concept in auditory systems identification.
Second-order Wiener kernels can be decomposed into excitatory and suppressive components via singular-value decomposition. — Explains the SVD-based decomposition that separates the second-order Wiener kernel into excitatory and suppressive sub-kernels.
Auditory nerve fibers are classified by the high-side-slope method of determining characteristic frequency — Names the Liberman (1984) method for estimating CF from the tuning curve’s high-frequency slope.
Top-down semantic knowledge compensates for degraded or inaudible speech — Establishes the top-down vs bottom-up framing central to the whole chapter.
Speech perception subconsciously activates overlapping word candidates that compete — Defines lexical competition and explains how it is normally resolved — the key construct the whole study manipulates.
Listeners subconsciously activate multiple word candidates before resolving lexical decisions — Defines lexical competition — the core mechanism all speech perception models agree on — and separates fast (200-500ms) luminance-driven pupil responses from slow (900ms) mental effort responses.
Envelope vocoders simulate cochlear implant processing by splitting sound into amplitude-modulated subbands — Defines the noise-band-vocoder technique for simulating cochlear-implant signal processing — extraction of amplitude envelopes to modulate noise carriers across Greenwood-spaced filters.
Pupil dilation indicates cognitive effort on a distinct 900 ms timescale — Defines pupil dilation as a two-component signal with separable timescales, distinguishing effort responses from light reflexes.
Baseline pupil dilation already differs before any target word because degraded listening is effortful — Explains the methodological convention of normalizing pupil dilation to a 200 ms pre-item baseline and why baseline differences between conditions are meaningful.
Envelope vocoders simulate cochlear implant processing by splitting sound into amplitude-modulated subbands — Explains the noise-band-vocoder technique used to simulate cochlear-implant processing for normal-hearing listeners.
Envelope vocoders simulate cochlear implant processing by splitting sound into amplitude-modulated subbands — Explains the noise-band-vocoder technique step by step — envelope extraction, modulation of broadband noise, channel summation — which is the standard simulation of CI sound.
A hearing impairment simulator can isolate cochlear compression loss in normal-hearing listeners — Explains how the HI simulator functions as an experimental isolation tool rather than a simple amplification device.
A hearing impairment simulator can isolate cochlear compression loss in normal-hearing listeners — Explains how the hearing-impairment simulator works: resynthesizing speech that counteracts compression in the normal-hearing listener’s ear.
Comparing syllable-level dips against whole-word level changes isolates peripheral compression effects — Clarifies the dip vs constant paradigm used to measure the behavioral effect of cochlear compression on syllable perception.
The Japanese Audiological Society standardized a 57-syllable list for clinical hearing studies. — Establishes the standardized 57-S syllable set and its purpose of cross-study comparability — a useful methodological norm in audiology.
A hearing impairment simulator can isolate cochlear compression loss in normal-hearing listeners — Explains how the simulator separates two distinct components of age-related hearing loss: cochlear compression reduction versus simple level attenuation.
Forward masking operates centrally on auditory nerve firing patterns — Clarifies that forward masking is modeled as central processing subsequent to peripheral compression.
Elderly listeners’ multiple impairments and cognitive decline block isolating presbycusis components — Defines the HI simulator approach: using normal-hearing listeners as controlled ‘patients’ via resynthesis that strips cochlear compression.
A standardized Japanese syllable set enables cross-study comparison in audiology — Introduces the 57-S syllable list, a standard clinical test set from the Japanese Audiological Society.
Model-based hearing-impairment simulation isolates the compression component of auditory processing — Explains the ‘dip’ vs ‘constant’ experimental contrast designed to reveal cochlear compression effects on syllable perception.
Brainstem encoding strength is quantified by cross-correlating stimulus envelope with neural response — Defines the operational measure of neural stimulus-encoding strength used throughout this study.
Future speech-intelligibility studies should pair EEG with matching psychophysical stimuli — A methodological self-critique noting that the EEG stimuli did not match psychophysical stimuli, an important design caveat for objective hearing measures.
Brainstem and steady-state responses measure audibility but not hearing ability. — Clarifies the distinction between audibility (threshold-based) and hearing ability (functional/cognitive) as separate constructs measured by different objective techniques.
Speech-in-noise ability is scored as the added SNR a listener needs — Defines the QuickSIN SNR loss metric, useful as a reference standard for what ‘normal’ speech-in-noise performance means operationally.
EFR signal quality is quantified by response-to-prestimulus RMS ratio — Clarifies the standard EFR SNR computation and why alternating polarities are averaged, a technical definitional element worth retaining.
TFS sensitivity is measured by a standard staircase procedure that converts to d-prime. — Explains what the TFS1 test is and how temporal fine structure sensitivity gets quantified.
Normal hearing is operationally defined as a four-frequency average threshold below 25 dB HL. — Operational definition of ‘normal hearing’ used in audiology research.
Brainstem responses can be quantified by correlation against the stimulus’s Hilbert envelope — Clarifies that EFR fidelity is scored against the Hilbert envelope of the actual delivered stimulus, measured via artificial ear, not the nominal waveform.
Brainstem envelope-following responses can index neural coding of periodicity and envelope — EFRs are defined here implicitly as brainstem responses that follow the stimulus envelope, elicited by periodic or modulated tones.
Brainstem responses can be quantified by correlation against the stimulus’s Hilbert envelope — Defines the stimulus-encoding-accuracy metric used throughout the chapter.
Speech-in-noise ability is measured as SNR loss relative to normal hearing — Defines the QuickSIN SNR-loss metric: the extra decibels of signal-to-noise ratio a listener needs versus a normal-hearing reference.
Deafness reroutes sensory processing so other modalities recruit auditory brain regions — Defines cross-modal plasticity with a clear concrete example.
Neural activity drives opposite changes in oxygenated and deoxygenated hemoglobin — Explains the hemodynamic basis of fNIRS measurement—the core physiology the reader wants.
NIR light absorption differs between oxygenated and de-oxygenated haemoglobin — The optical-physiological mechanism underpinning fNIRS that the reader needs for understanding.
Resting-state connectivity reflects neural plasticity from deafness and reorganisation — Defines resting-state/default-mode activity as predictive tracking of environmental changes.
fNIRS measures brain blood flow via paired scalp light sources and detectors — Concise mechanics of how fNIRS channels and the banana-shaped scattering pathway operate.
Language processing dominates left-hemisphere networks while the right hemisphere handles communication context. — Establishes LH/RH division of language labor and that post-lingual deafness reshapes language brain areas.
Brain activity can be measured optically because haemoglobin absorbs near-infrared light differently when oxygenated. — Explains the physical basis of fNIRS: differential HbO/HbR absorption spectra are what make the whole technique possible.
Neural activity raises oxygenated and lowers de-oxygenated blood locally, enabling optical imaging — Explains the hemodynamic basis (HbO/HbR opposites) that underpins all fNIRS measures.
fNIRS avoids electrical artifacts and works with implanted devices where fMRI and EEG fail — Clearly delineates fNIRS’s methodological niche relative to PET, fMRI, and EEG/MEG for implanted-device populations.
Neural activity drives opposite changes in oxygenated and deoxygenated hemoglobin — Establishes the key neuroimaging contrast mechanism (HbO vs HbR spectral absorption) that underpins fNIRS measurement.
Resting-state connectivity reflects the brain’s predictive ability and plasticity — Defines resting-state/default-mode activity and resting-state connectivity as measures for tracking environmental prediction and neural plasticity.
Phonemic and articulatory-feature speech representations can be encoded as multivariate EEG-regression predictors — Explains how phonemic and phonetic-feature representations are constructed from speech for temporal response function modeling.
Priming can render identical noise-vocoded speech stimuli intelligible or not — Explains the priming paradigm for producing physically identical but perceptually different speech, useful for isolating phonetic processing.
TRF regression weights reflect cortical activation at specific speech delays — Clarifies that linear-regression TRF weights index macroscopic cortical activation at specific time-lags relative to speech features.
Noise-vocoding strips speech intelligibility by replacing fine spectral detail with modulated noise — Explains what noise-vocoding is — a standard technique for degrading speech intelligibility while preserving its amplitude envelope.
Phonemic and articulatory-feature speech representations can be encoded as multivariate EEG-regression predictors — Defines the five speech-representation schemes used for EEG-to-speech mapping, a methodological framework for neural speech processing.
TRF regression weights reflect cortical activation at specific speech delays — Explains how model weights from TRF regression are interpreted as cortical activation across time-lags.
Speech phonemes reduce to 18 articulatory-acoustic features in a linear mapping — Clarifies what a phonetic-feature encoding of speech consists of and how it is derived.
EEG tracks continuous speech’s amplitude envelope, but its causes remain unclear — Introduces the envelope-tracking phenomenon central to the chapter.
Noise-vocoding strips speech intelligibility by replacing fine spectral detail with modulated noise — Clear procedural definition of noise-vocoding, the core stimulus-manipulation technique in intelligibility research.
No neural evidence supports autocorrelation models of pitch at the brainstem — Introduces the key term ‘entracking’ (entrained phase-locking) and defines it as generating a scalar rate code for pitch.
Pitch can be coded by monotonic firing rate instead of tuned delay circuits — Defines scalar vs vector codes; the scalar code requires no delays and needs no periodicity tuning.
Uniform cross-neuron firing rate distinguishes true entracking from mere low rate — Gives the operational test for distinguishing an entracking code from an ordinary low-rate response: uniformity across the population.
Entracking creates flat rate plateau codes that supplant tonotopic place codes — Defines the ‘butte’ — a horizontally flattened rate-place profile produced by entracking, distinct from the tonotopic ‘hilly’ resolved/unresolved profile.
Entracking codes low-frequency pitch through uniform firing across neurons while high-frequency firing varies — Clarifies how an entracking neuron’s uniform low firing rate distinguishes genuine pitch coding from merely low firing on high-frequency components.
Pitch is coded by firing rate constancy, not spike phase — Clarifies the key conceptual claim distinguishing the butte/rate code from temporal phase codes for pitch.
The dominant interspike interval may be coded as predominant neural firing rate. — Introduces the ‘butte hypothesis’ — that dominant ISI in the auditory nerve is converted by central neurons into a firing rate that codes pitch.
Periodicity entracking is distributed across brainstem structures, not localized in one center. — Clarifies entracking as a distributed, multi-stage convergence process requiring multiple AN inputs.
Autocorrelation pitch schemes lack neural evidence for periodicity-tuned neurons — Clarifies the autocorrelation-type scheme: coincidence detection combined with a source of delay.
Pitch may be encoded by entrained phase-locking producing a rate code — Introduces the term ‘entracking’ as contracted from entrained phase-locking.
Brainstem entracking neurons fire one spike per stimulus cycle — Defines ‘entracking’ precisely: a neuron firing one spike per cycle so firing rate equals stimulus repetition frequency, forming a horizontal rate-place ‘butte’ profile.
Pitch is coded by firing rate constancy, not spike phase — Defines the vector-code vs scalar-code distinction central to pitch-encoding theories.
Entracking neurons form plateau ‘buttes’ of uniform firing rate across a tonotopic profile — Introduces the chapter’s key ‘butte’ concept as a horizontal rate-place profile created by entracking.
Brainstem neurons can fire one spike per sound cycle, unlike auditory nerve fibers — Defines the core term ‘entracking’ as firing one spike per stimulus cycle.
Entracking generates horizontally flattened rate profiles that encode pitch as firing rate — Defines the ‘butte’ hypothesis — entracking producing horizontally flattened rate-place profiles whose firing rate is clamped by dominant interspike interval.
Pitch-period codes divide into delay-based vector codes and rate-based scalar codes — Defines the vector-code vs scalar-code distinction for pitch encoding, a key theoretical taxonomy.
Harmonically shifted complex tones isolate fine structure cues while holding envelope constant — Describes the standard stimulus design used to separate envelope from fine structure cues in pitch perception research.
Shifting harmonic components changes fine structure while keeping envelope identical — Explains the shifted-complex-tone experimental logic, the load-bearing tool for separating envelope from fine structure.
Periodicity cues for pitch split into fine-structure, envelope, and interaction periodicities — Establishes the three-way taxonomy of temporal periodicity cues (TSF, TE, and their interaction) that structures the whole chapter.
Auditory spiketrain periodicities can encode both envelope and fine structure — Explains the three periodicity cue types (fine-structure, envelope, and their product) discriminated in pitch tests.
Pitch perception involves both spectral and temporal representations of sound — Introduces TE, TSFperiod, and TEperiod as the distinct temporal periodicity constructs under test.
Neuronal firing thresholds shape which pitch periodicities a system can detect — Clarifies how a threshold model maps onto auditory nerve fiber firing thresholds, linking a computational parameter to a physiological mechanism.
Pitch percepts may only require encoding fine-structure peaks near envelope maxima — Explains the pitch-shifted unresolved-harmonic-complex experimental tool and its interpretive logic, which this passage then challenges.
A third temporal periodicity depends jointly on envelope and fine structure — Introduces the interaction periodicity TE × TSF, the conceptual core of the chapter’s argument.
Envelope and fine structure cues are mathematically separable but perceptually entangled — Explains the Hilbert-transform orthogonality of envelope and fine structure, and the De Boer (1956) shifted-complex signal that dissociates them.
Permutation tests and bootstrapping are used to establish significance in evoked-response MEG data — Explains the t-sum permutation and bootstrap critical-t approach used throughout this MEG literature.
Melody processing recruits a distinct generator in anterior Heschl’s gyrus — Clarifies PT (planum temporale) vs anterior HG (Heschl’s gyrus) as distinct generator sites for energy onset vs melodic processing.
Melody processing recruits a distinct generator in anterior Heschl’s gyrus — Defines the transient response (TR) with its P1m, N1m, P2m components and the two auditory-cortex source locations (medial HG and anterior PT).
Sustained cortical fields can be measured over long musical sequences. — Introduces the sustained field (SF), a longer-timescale cortical response measured over tonic and melodic bars, distinct from the transient response to individual notes.
Electrical brain stimulation may enhance sounds temporally coherent with entrained oscillations — Introduces the concept of ‘envelope enhancement’ as a mechanistic interpretation tying tACS to auditory scene analysis.
Transcranial stimulation may someday bypass impaired auditory pathways by injecting envelope information — Explains the rationale for individualized tACS parameter selection — matching stimulation frequency to the person’s own dominant cortical oscillation.
Transcranial alternating current stimulation entrains cortical oscillations and alters perception — Defines tACS: non-invasive scalp-electrode current that forces neuronal excitability to oscillate with its waveform and may entrain cortical rhythms.
Best-phase alignment compensates for inter-individual shifts in neural response phase — Explains ‘best-phase alignment’: aligning each participant’s best-performance phase to 90° handles individual phase-offset variability before averaging.
Cranial electrode placement can target both auditory cortices homophasically with matched circuits — Clarifies the tACS montage: what ‘homophasic’ stimulation means and how electrode geometry (T7/T8 vs Cz) targets auditory cortex.
Modulating target sounds at tACS frequency lets listeners sample at fixed phase — Explains the design rationale: why target modulation must match stimulation frequency and what ‘sampling at fixed phase’ means experimentally.
Maskers in auditory experiments must eliminate amplitude cues to isolate detection accuracy — Describes the construction constraints (critical-band separation, amplitude-cue elimination) used to build a proper masking stimulus.
Hearing experiments track performance as accuracy or d-prime sensitivity measures — Defines the two performance measures used across the experiments: raw accuracy and the signal-detection sensitivity index d-prime.
Best-phase alignment normalizes individual variability in tACS phase effects — Defines the ‘best-phase alignment’ normalization method that compensates for inter-individual phase differences before testing the phase-modulation hypothesis.
The brain must decompose an unlabelled auditory mixture into coherent perceptual objects — Names and defines auditory-scene analysis as the field’s core problem.
The same acoustic stimulus can be sorted by different features for different perceptual goals — Defines perceptual grouping and auditory categorization as distinct computational processes in the auditory brain.
Task-engaged neural recordings clarify how the ventral pathway serves perception — Clarifies the review’s methodological scope: only studies where neural activity and a behavioral task were measured together.
Holding stimuli constant while varying perceptual reports can dissociate sensory from perceptual neural activity — Defines auditory objects as perceptual representations and derives the operational test for whether a neuron encodes them.
Auditory ventral-pathway processing is hierarchical, with decisions arising only in frontal cortex. — Condenses the chapter’s central model: feature encoding early, judgment-related activity only later in frontal lobe.
Speech categories are represented hierarchically along the auditory cortex — This passage maps phoneme, word, and phrase representations onto specific anatomical positions in the superior temporal gyrus — a useful spatial catalog of speech-category processing.
The prime auditory problem is separating and identifying sounds from an unlabeled mixture — Defines auditory-scene analysis as conversion of unlabelled sound mixtures into coherent perceptual representations.
Neural evidence for sound-object encoding requires holding the stimulus constant — Defines the operational test that distinguishes feature-responsive neurons from genuine perceptual-representation neurons.
Listeners categorize the same speech sound differently depending on the information sought — Illustrates auditory categorization with the speaker-gender vs word-content example.
Auditory perception runs parallel bottom-up and top-down processes — Defines the four core computational processes underpinning auditory perception.
Perceptual grouping decides whether acoustic events fuse into one or split into several sounds — Clear working definition of perceptual grouping, the classic Bregman auditory-scene-analysis concept.
Auditory perception engages four distinct computational processes operating in parallel or serial — Defines perceptual grouping and auditory decision-making as the two named computational processes.
Neural evidence of encoding auditory objects requires modulation by perceptual reports — Defines what would count as evidence a neuron encodes an auditory object: modulation by perceptual report under a constant stimulus, a crucial methodological criterion.
Noise and reverberation reduce modulation depth but preserve signal shape — Clarifies the acoustic distinction between reverberation (linear filtering, no new frequencies) and additive noise.
Periodicity-based sound segregation begins at the auditory brain stem — Defines the ventral cochlear nucleus as the first processing station in the auditory brain stem.
Correlograms track how interspike-interval statistics change over time in cochlear nucleus responses — Defines ‘correlogram’ as a time-varying shuffled interspike-interval distribution computed in sliding short windows.
Periodicity-tagged firing rates use template contrast to separate concurrent-vowel responses — Defines the periodicity-tagged firing-rate metric — how the study isolates each vowel’s contribution from a double-vowel mixture.
Synthetic speech studies use controlled formant and F0 parameters to isolate cues — Explains the standard stimulus construction (Klatt formant synthesizer, formant frequencies, F0 contours, SNR levels) used in speech-segregation research.
Periodicity-sieve analysis statistically tags which vowel of a mixture drives a neuron. — Introduces template-contrast functions and periodicity-tagged firing rate as the key measures for separating simultaneous vowels.
Shuffled inter-spike-interval analysis captures time-varying periodicity in single neurons. — Defines ‘correlograms’ as time-varying shuffled inter-spike-interval distributions computed across spike-train pairs in sliding time windows.
Reverberation in experiments is graded by physical source-receiver distance — Lets the reader understand what experimentally realized ‘mild’ vs ‘strong’ reverberation actually means physically
Periodicity sieves estimate which vowel dominates a unit’s firing in mixtures. — Defines periodicity-sieve analysis, template-contrast functions, and periodicity-tagged firing rate, the core analysis methods of this chapter.
Statistical bootstrapping validates periodicity detection in neural spike-interval data — Defines ‘template-contrast functions’ as outputs of periodicity-sieve analysis on correlograms.
Multisensory binding decisions depend on a prior coherence estimate between auditory and visual streams. — Defines the AVSSA concept and its two-stage architecture: coherence assessment precedes fusion.
Auditory scene analysis segregates sound first and forms percepts later — Defines the two-stage structure of Auditory Scene Analysis — segmentation/binding then decision — the framework this whole chapter extends.
McGurk-effects that saturate tend to be excluded from audiovisual experiments — Clarifies the McGurk effect as a perceptual fusion where the auditory ‘ba’ and visual ‘da’ are perceived as ‘da.’
McGurk responses are processed 70-80 ms slower than matching audiovisual targets — Establishes “McGurk targets” as audiovisual stimuli producing the illusory fusion percept, contrasted with matching “ba” targets.
Syllabic speech produces far stronger audio-visual correlation than sentences do — Introduces the AVSSA hypothesis as a first speech scene analysis stage that evaluates audiovisual coherence before fusion.
Two-stage scene analysis extends from auditory to audiovisual speech scenes. — Defines the AVSSA framework: an audiovisual binding stage whose output gates the decision stage.
Temporal coherence of audiovisual signals predicts how strongly vision captures auditory perception. — Introduces AVSSA “binding and fusion” as a framework for how audiovisual sources get associated.
Competing audiovisual and audio-only sources can be experimentally arranged to test integration — Clarifies the experiment’s key manipulation: which stream is audiovisual vs. audio-only flips, isolating how visual binding steers auditory perception.
A coherence index computed before fusion decides whether audiovisual inputs bind — Introduces and defines AVSSA and its central mechanism of pre-fusion coherence computation.
Visual streams can drive auditory segregation, but grouping order remains contested — Introduces the AVSSA ‘binding stage’ as a coherence-evaluation mechanism paralleling auditory scene analysis in AV speech perception.
Speech perception begins with an audiovisual scene analysis stage — Defines the AVSSA (audio-visual speech scene analysis) framework as an early grouping/coherence-estimation stage.
Two-stage scene analysis extends from auditory to audiovisual speech scenes. — Introduces the AVSSA extension of Bregman’s ASA to combined audiovisual sources, unifying two normally separate research strands.
End-of-experiment content quizzes effectively enforce directed attention in audiovisual speech tasks — Explains the attentional-manipulation technique: instructing attention toward syllables vs. sentences, backed by a threatened content quiz, and how responses are gated to a 200-1200ms window.
Learning a tune may require hearing it at many pitch levels across linked contexts — Explains the trace model and how invariance is learned through many-location exposure, not hardwired.
Relative pitch recognition is an auditory case of translation-invariant object perception — Frames relative pitch perception as translation-invariant object perception, connecting auditory recognition to a general perceptual principle.
Deviant-tone-pattern detection trials present four six-tone patterns with one interval altered. — Defines the oddball paradigm: four tone patterns of six tones, one deviant interval in the last or penultimate.
Deviant-pattern detection required discriminating penultimate versus final patterns. — Defines the two-alternative judgment paradigm used to measure pattern deviation detection.
An oboe-like timbre is reproduced by four harmonics with exponentially decreasing amplitudes — Concise description of the spectral shape that defines oboe-like timbre in synthesized stimuli.
Zwicker tones produce pitch without peripheral phase locking. — Defines Zwicker tone as a faint decaying tonal percept arising after band-stop noise, with pitch in the notch range.
Zwicker tones have no cochlear mechanical or otoacoustic correlate. — Introduces Zwicker tones: faint decaying audible pitches inside a noise notch, with no matching cochlear mechanical activity.
Phase-locked neural timing underlies musical pitch and is limited above 4-5 kHz. — Defines phase locking as the temporal code of auditory nerve spikes thought to underlie pitch, and fixes its upper frequency limit at about 4-5 kHz.
Zwicker tones arise centrally, not from cochlear mechanics or phase locking — Defines what a Zwicker tone is and its key properties.
Listening to notched noise can unmask spontaneous otoacoustic emissions — Introduces the concept of spontaneous otoacoustic emissions as a confound in psychoacoustic pitch-matching experiments.
Zwicker tones require a notched noise with edges separated by a fixed ratio — Establishes the stimulus parameters (notched noise, LEF/HEF ratio of 1.5) that reliably evoke Zwicker tones, useful baseline for understanding the phenomenon.
Weak musical pitch perception can persist without peripheral phase locking. — Defines Zwicker tones and explains their hypothesized non-mechanical, non-phase-locked origin.
Zwicker tones arise centrally, not from cochlear mechanics or phase locking — Defines what a Zwicker tone is and establishes the crucial negative fact (no cochlear mechanics) that makes it a probe for non-temporal pitch.
Zwicker tones arise centrally, not from cochlear mechanics or phase locking — Clarifies the mechanistic argument for why ZTs bypass cochlear mechanics — the crux of the chapter.
Zwicker tones are faint tonal afterimages arising from notched noise — Defines what a Zwicker tone is — a decaying pitch percept following notched broadband noise.
Zwicker-tone matching gives anomalously high pitches when spontaneous otoacoustic emissions become audible. — Explains what spontaneous otoacoustic emissions (OAEs) are in this context — self-generated cochlear sounds that can confound psychoacoustic pitch matches.
Cochlear nonlinearities establish F0-synchronized firing patterns that the midbrain converts into rate codes — Defines the mechanism by which temporal phase-locking information becomes a spatial/rate code across neurons — the key transformation in midbrain speech coding.
Vowels are distinguished by the two lowest vocal tract resonance frequencies. — Provides baseline definitions of F0, formants, and F1/F2 needed to understand later coding details.
Formant frequencies are encoded by F0 fluctuation amplitude across auditory nerve fibres. — Explains synchrony capture: how one harmonic dominates near a spectral peak, flattening that fibre’s envelope.
Inhibitory-excitatory synaptic timing determines a midbrain neuron’s best modulation frequency. — Clarifies how the SFIE model and the three MTF types (band-enhanced, band-suppressed, hybrid) are mechanistically derived from excitatory/inhibitory synaptic timing.
Sensorineural hearing loss disrupts auditory-nerve phase-locking to modulated stimuli — Clarifies that COHC and CIHC model parameters simulate outer and inner hair cell dysfunction, with specific threshold-elevation values.
Cellular synaptic timing parameters determine a neuron’s preferred modulation frequency — Defines the SFIE model and grounds best modulation frequency (BMF) in measurable synaptic timing parameters.
Inferior colliculus neurons show only three types of modulation tuning that can be modeled — Defines the three MTF types — band-enhanced, band-suppressed, hybrid — that classify IC neuron behavior to amplitude-modulated stimuli.
Sensorineural hearing loss disrupts F0-synchrony coding while sparing mean firing rates. — Introduces synchrony capture and the modulation-spectrum mechanism linking peripheral bandwidth to low-frequency fluctuation amplitude.
Vowel coding relies on F0-synchronized rate contrasts, not just formant peaks. — Clarifies what F0-synchronized rate profile means versus formant-based representation in AN coding.
The midbrain converts temporal phase-locking codes into a mean firing-rate profile for speech. — Explains how temporal coding in the auditory nerve becomes rate coding in the inferior colliculus — key physiology concept.
SNHL disrupts the fluctuation pattern that normally codes vowel formants. — Explains ‘synchrony capture’: the healthy ear’s dominance of a single harmonic near a spectral peak in AN responses to voiced sounds.
Vowel identity depends on the locations of the two lowest vocal tract formants — Defines F0, harmonic spectra, and formants F1/F2 as the acoustic basis of vowel identity.
Synchrony capture near formants suppresses F0 phase locking while fibers between formants encode F0 — Defines ‘synchrony capture’ — dominance of a fiber’s temporal response by a single harmonic near the formant peak.
Perception variability splits into source-induced and receiver-related components — Defines the source-induced vs receiver-related variability distinction that structures the study.
Dynamic time warping measures the distance between internal speech representations. — DTW as the alignment metric used to compare test signal to templates in the auditory model back end.
Internal listener noise rises steeply as signal-to-noise ratio worsens — Clarifies what ‘internal noise’ means in speech perception modeling — a stochastic component in the decision stage that grows as SNR drops.
Sound-source differences dominate consonant perception variability more than listener uncertainty — Introduces the study’s core taxonomy distinguishing source-induced (speech/noise) from receiver-related (across/within-listener) variability.
Speech perception models decompose subband signals into acoustic and modulation envelopes — Gives concrete parameters of energy- vs modulation-based representations (gammatone filters, modulation bandwidths 4-16 Hz) — useful reference detail.
Consonant perception can be modeled by matching noisy test signals to talker-specific speech templates. — Describes the template-matching methodology: talker-specific template sets, noise-mixing at test SNR, and repeated runs as a Monte Carlo approach.
Speech perception models decompose subband signals into acoustic and modulation envelopes — Concrete specification of how subband power features are extracted in a gammatone-based speech model.
Listeners only rate a speech-quality change as meaningful when it exceeds detectability thresholds — Introduces the better/worse scaling procedure and the JMD (just-meaningful-difference) concept.
Clinical significance of hearing gains is measured by patients’ motivation to seek care — Defines ‘clinical significance’ operationally as motivation to seek intervention rather than a statistical threshold.
Hearing aid SNR improvements fall below the just-noticeable threshold for speech understanding — Introduces JND (just noticeable difference) and JMD (just meaningful difference) as the relevant thresholds distinguishing detectable from meaningful speech-intelligibility change.
Speech-in-noise clarity discrimination can be measured with identical masked sentences while cueing levels — Describes the JND-for-SNR experimental paradigm: two-interval forced choice between identical sentences at different SNRs, with level roving to strip loudness cues.
Detectability alone fails to capture what hearing benefit users deem meaningful — Introduces the JMD (just meaningful difference) as distinct from the JND (just noticeable difference).
Just meaningful difference extends JND by adding subjective comparison tasks — Defines the just meaningful difference (JMD), an extension of the classic JND into subjective, clinically meaningful territory.
Detection threshold for SNR change in sentences averages about 3 dB — Defines the SNR JND as the proposed psychophysical unit for meaningful hearing benefit, at roughly 3 dB for sentence material.
Willingness to swap hearing devices requires at least 6 dB SNR improvement — JMD = just meaningful difference, the smallest speech-intelligibility gain worth a behavior change, here quantified behaviorally.
A psychophysical JND in signal-to-noise ratio does not guarantee subjectively meaningful benefit to patients — Distinguishes the JND (psychophysical discriminability) from clinically/subjectively meaningful benefit as separate constructs.
Conversation-tolerance is a novel scale for speech-in-noise meaningfulness — Introduces the conversation-tolerance rating task as a methodological option for quantifying meaningful hearing aid benefit.
Cochlear back-propagating waves create measurable emissions affecting perception — Explains what SOAEs and SFOAEs are and that they arise from backward-propagating cochlear waves.
Pure tone audiograms fail to predict speech-in-noise listening difficulties — Defines the cocktail party effect and the pure-tone audiogram’s clinical gold-standard status in one compact passage.
Cochlear otoacoustic emissions measurably shift behavioral thresholds, selectivity, and loudness — Defines OAEs as reverse-propagating cochlear sounds measurable in the ear canal, grounding later argument.
Spontaneous otoacoustic emissions align with peaks in stimulus-frequency fine structure — Defines the SFOAE fine structure pattern of amplitude maxima and minima.
Otoacoustic emissions non-invasively probe outer hair cell function in humans. — Defines OAE as faint reverse-traveling sounds out the ear canal, the standard noninvasive probe of OHC function.
Each listener has a stable, individual weighting of frequencies when hearing speech in noise — Introduces ‘individual listening styles’ and ‘perturbation analysis’ as technical concepts for how listeners weight frequencies in noise.
Cochlear irregularity measured via otoacoustic emissions could predict individual listening decision weights — Defines SFOAE fine structure: replicable individualistic amplitude maxima/minima pattern, up to 30 dB level differences.
Cochlear otoacoustic emissions measurably shift behavioral thresholds, selectivity, and loudness — Defines otoacoustic emissions as faint sounds traveling back out of the cochlea, measurable in the ear canal.
Spontaneous otoacoustic emissions cluster near SFOAE fine-structure maxima but remain undetectable there. — Clarifies the relation between SOAEs and SFOAE fine-structure maxima, key to interpreting which cochlear irregularity listeners weight.
Listener decision weights derive from regression on response perturbations — Clarifies how the behavioral experiment converts trial outcomes into per-frequency decision weights.
Spontaneous otoacoustic emissions align with peaks in stimulus-frequency fine structure — Operational definition of how SOAE peaks are detected and distinguished from noise.
EFR slope across modulation depth reflects supra-threshold temporal coding fidelity — Explains the key differential metric: instead of absolute EFR strength, take the EFR change per dB of modulation-depth reduction — the slope indexes temporal-coding fidelity.
DPOAE thresholds measure peripheral function unaffected by auditory nerve deficits — Defines the unique value of DPOAEs: a purely peripheral measure that can be subtracted from psychophysical or EEG measures to separate components.
Cochlear gain loss can compensate for temporal coding deficits in AM detection — EFR (envelope following response) and AM detection thresholds defined implicitly as objective and psychoacoustic measures of temporal coding.
Mild hearing-impaired listeners retain measurable DPOAEs — Clarifies DPOAE (distortion product otoacoustic emission) as a usable measure only in mild, not profound, hearing loss.
Distortion-component OAEs probe the f2 cochlear site free of fine-structure interference — Clarifies what the DCOAE extraction method measures and why it avoids the fine-structure contamination that afflicts conventional DPOAE input-output functions.
Hearing loss combines two distinct pathologies that impair separate processing stages — Defines the two-component model of sensorineural hearing loss: cochlear gain loss vs cochlear neuropathy.
DCOAE recordings isolate f2 site information free of distortion-product fine structure — Clarifies why the DCOAE sweep is used: it selectively probes the f2 characteristic site and avoids distortion-product fine structure contamination of the I/O function.
Medial olivocochlear efferent feedback reduces cochlear gain most for frequencies near the signal — Defines the efferent effect filter concept and its key quantitative parameter (10 dB bandwidth of 1300 Hz, Q10 of 3.1 at 4 kHz).
The human medial olivocochlear efferent reflex operates with specific millisecond timing constants. — Concrete quantitative timing parameters of the human MOCB reflex response.
Psychoacoustic forward masking can probe the efferent reflex without its own contamination. — Explains the FDMC method’s design logic: exploiting the 25-40 ms efferent delay as a measurement window.
Efferent gain effects were mapped by precursors swept across nine centre frequencies — FDMC (forward masking with a difference masker? unclear) is used without a reader-level definition here; the precursor/onset/offset terminology is explained via the timing specs.
Efferent cochlear gain reduction is greatest when the precursor matches the signal frequency — Clarifies how a precursor sound (prior stimulus) is used to manipulate cochlear gain via the efferent system before a test stimulus arrives.
Inferred basilar-membrane gain in humans is about 41 dB at 4 kHz — Explains how inferred cochlear gain is derived operationally — as the threshold difference between on- and off-frequency maskers, a psychoacoustic proxy for basilar-membrane amplification.
Medial olivocochlear efferent feedback reduces cochlear gain most for frequencies near the signal — Establishes what FDMC (Fixed Duration Masking Curve) and the efferent-effect filter mean as a measurement framework.
Medial olivocochlear efferent feedback reduces cochlear gain most for frequencies near the signal — MOCB defined as the brainstem efferent bundle reducing cochlear gain; OAE defined as the measurement window into it.
Efferent cochlear gain reduction is greatest when the precursor matches the signal frequency — Clarifies that a ‘precursor’ sound activates the efferent system and FDMC is the measurement method.

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Dijk, Pim van — Physiology, Psychoacoustics and Cognition in Normal and Impaired Hearing

Dijk, Pim van — Physiology, Psychoacoustics and Cognition in Normal and Impaired Hearing

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