Recognition and discrimination of prototypical dynamic expressions of pain and emotions
Bibliographic record
Abstract
Facial expressions of pain and emotions provide powerful social signals, which impart information about a person's state. Unfortunately, research on pain and emotion expression has been conducted largely in parallel with few bridges allowing for direct comparison of the expressive displays and their impact on observers. Moreover, although facial expressions are highly dynamic, previous research has relied mainly on static photographs. Here we directly compare the recognition and discrimination of dynamic facial expressions of pain and basic emotions by naive observers. One-second film clips were recorded in eight actors displaying neutral facial expressions and expressions of pain and the basic emotions of anger, disgust, fear, happiness, sadness and surprise. Results based on the Facial Action Coding System (FACS) confirmed the distinct (and prototypical) configuration of pain and basic emotion expressions reported in previous studies. Volunteers' evaluations of those dynamic expressions on intensity, arousal and valence demonstrate the high sensitivity and specificity of the observers' judgement. Additional rating data further suggest that, for comparable expression intensity, pain is perceived as more arousing and more unpleasant. This study strongly supports the claim that the facial expression of pain is distinct from the expression of basic emotions. This set of dynamic facial expressions provides unique material to explore the psychological and neurobiological processes underlying the perception of pain expression, its impact on the observer, and its role in the regulation of social behaviour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".