Perception of emotion in the facial expressions and body language of athletes
Bibliographic record
Abstract
Facial expressions are commonly believed to reliably convey emotional information, but some research suggests that people are better at perceiving emotions through body language. We hypothesized that individuals' emotional perception would improve when body imagery was presented, relative to viewing the face alone. Following musical mood induction, participants were shown images of winning and losing tennis players that were cropped to show either (a) only the face, (b) only the body, or (c) both the face and the body, before rating each player's perceived level of arousal and emotional experience. Results showed there was a reciprocal emotional rating effect for face imagery, with participants mistakenly rating losing faces as experiencing more positive emotion than winning faces did; when body imagery was shown along with the face, participants' emotional perception was more accurate. Significant gender differences were observed in ratings of female versus male players. Our study indicates that without further context, emotional perception is unreliable from the face alone. Theoretical and practical implications are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".