Emotions are understood from biological motion across remote cultures.
Bibliographic record
Abstract
Patterns of bodily movement can be used to signal a wide variety of information, including emotional states. Are these signals reliant on culturally learned cues or are they intelligible across individuals lacking exposure to a common culture? To find out, we traveled to a remote Kreung village in Ratanakiri, Cambodia. First, we recorded Kreung portrayals of 5 emotions through bodily movement. These videos were later shown to American participants, who matched the videos with appropriate emotional labels with above chance accuracy (Study 1). The Kreung also viewed Western point-light displays of emotions. After each display, they were asked to either freely describe what was being expressed (Study 2) or choose from 5 predetermined response options (Study 3). Across these studies, Kreung participants recognized Western point-light displays of anger, fear, happiness, sadness, and pride with above chance accuracy. Kreung raters were not above chance in deciphering an American point-light display depicting love, suggesting that recognizing love may rely, at least in part, on culturally specific cues or modalities other than bodily movement. In addition, multidimensional scaling of the patterns of nonverbal behavior associated with each emotion in each culture suggested that similar patterns of nonverbal behavior are used to convey the same emotions across cultures. The considerable cross-cultural intelligibility observed across these studies suggests that the communication of emotion through movement is largely shaped by aspects of physiology and the environment shared by all humans, irrespective of differences in cultural context. (PsycINFO Database Record
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".