Emotion recognition reduces feelings to pattern-recognition problems for computers.
Evidence
The key to affective computing, Picard argued, was to render both conscious and unconscious emotion as observable behavior for coding and calculation. A computer, she reasoned, would be able to render your emotions as behavioral information. Affect recognition, as she put it, is “a pattern recognition problem,” and “affect expression” is pattern synthesis. The proposition was that “computers can be given the ability to recognize emotions as well as a third-person human observer.” — Shoshana Zuboff, The Age of Surveillance Capitalism, 889
Categorization
- DEFINE: Distills the operational definition of affect recognition — a pattern-recognition framing that underlies the whole chapter.
- XREF: connects to: Affective computing treats unconscious emotions as measurable behavioral signals. — Shoshana Zuboff, The Age of Surveillance Capitalism, 888; Emotion-detecting chips claim to classify twelve emotions across 64 trillion states — Shoshana Zuboff, The Age of Surveillance Capitalism, 900; Affect perception is measured on arousal and valence dimensions rather than emotion labels (+2 more)
Broader Connections
- Emotion-detecting chips claim to classify twelve emotions across 64 trillion states
#exemplifies(0.90) — Shoshana Zuboff, The Age of Surveillance Capitalism, 900 - Facebook patented emotion-detection software that images can classify into many feeling states
#exemplifies(0.85) — Shoshana Zuboff, The Age of Surveillance Capitalism, 893 - Affect perception is measured on arousal and valence dimensions rather than emotion labels
#qualifies(0.80) - Affective identification reduces complexity but renders the system rigid and non-transferable.
#qualifies(0.75) — Niklas Luhmann, Trust and Power, 288