Determination of “Neutral”–“Pain”, “Neutral”–“Pleasure”, and “Pleasure”–“Pain” Affective State Distances by Using AI Image Analysis of Facial Expressions

(1) Background: In addition to verbalizations, facial expressions advertise one’s affective state. There is an ongoing debate concerning the communicative value of pain and pleasure, what extent humans can distinguish between these. We introduce a novel method analysis by replacing human ratings with outputs from image software. (2) Methods: use software extract feature vectors neutral, pain, pleasure displayed 20 actresses. dimension-reduced these vectors, used singular decomposition eliminate noise, then hierarchical agglomerative clustering detect patterns. (3) Results: The vector norms for pain–pleasure were rarely less than distances pain–neutral pleasure–neutral. Weibull-distributed noise contributed 10% signal. noise-free clustered in four clusters two isolates. (4) Conclusions: AI methods recognition are superior abilities distinguishing pleasure. Statistical offer possible explanations as why fail. reliability commercial software, which attempts identify states, be improved using results our analyses.