Temporal integration may sample informative snapshots rather than integrating all energy

Evidence

The above results are consistent with predictions from statistical integration of information. First, unsurprisingly, change detection should improve with greater change sizes. Second, change detection should improve, but saturate, as the estimation of the statistics of the first stimulus converges, i.e. if longer observation time is available. Third, change detection of bigger changes should require less precision in the estimation, translating to higher hit rate already for shorter observation times. — Dijk, Pim van, Physiology, Psychoacoustics and…, 3285 (#corroborates, via linked claim)

Categorization

• XREF: connects to: Detection of statistical change requires temporal integration of the marginal distribution; Two model classes explain temporal integration via long versus short time constants; Temporal processing divides into resolution, masking, and integration categories (+4 more)

Related

• Detection of statistical change requires temporal integration of the marginal distribution (similarity 0.94)
• Auditory change detection integrates statistical evidence accumulated over time (similarity 0.90)
• Change-detection sensitivity rises with observation time but stays low for small changes. (similarity 0.90)
• Listeners detect statistical changes in ongoing sound based on time available for evidence integration (similarity 0.89)
• Human change detection follows a statistical-integration model as estimation precision improves with observation (similarity 0.89)
• Time available for integrating evidence is the main predictor of change detection success (similarity 0.87)
• Larger change sizes steepen and left-shift hit-rate curves, shortening detection time constants. (similarity 0.86)
• Reaction times reveal how listeners estimate post-change statistics in integration tasks. (similarity 0.85)
• Detecting changes in auditory textures depends mainly on integration time available (similarity 0.84)
• Longer listening time to initial texture statistics improves change detection up to an asymptote (similarity 0.83)
• Change detection in stochastic textures depends on statistical prediction rather than spectrotemporal predictability (similarity 0.83)
• Detecting statistical changes in sound improves with listening time and change size (similarity 0.83)
• The ear integrates energy over time to lower detection thresholds for longer sounds (similarity 0.82)
• Temporal integration models divide on time constant assumptions of hundreds versus single-digit milliseconds. (similarity 0.82)
• Clinical audiogram testing standardizes stimulus durations to exploit temporal integration (similarity 0.82)

Broader Connections

• Two model classes explain temporal integration via long versus short time constants #responds-to (0.85)
• Auditory change detection integrates statistical evidence accumulated over time #corroborates (0.85) — Dijk, Pim van, Physiology, Psychoacoustics and…, 3285
• Reaction times reveal how listeners estimate post-change statistics in integration tasks. #corroborates (0.80) — Dijk, Pim van, Physiology, Psychoacoustics and…, 3333
• The two integration models differ on how intervening noise affects thresholds #expands (0.80)
• Detecting changes in auditory textures depends mainly on integration time available #corroborates (0.78)
• Temporal processing divides into resolution, masking, and integration categories #qualifies (0.75)