Confidence in Emotion Perception in Point-Light Displays Varies with the Ability to Perceive Own Emotions
Bibliographic record
Abstract
One central issue in social cognitive neuroscience is that perceiving emotions in others relates to activating the same emotion in oneself. In this study we sought to examine how the ability to perceive own emotions assessed with the Toronto Alexithymia Scale related to both the ability to perceive emotions depicted in point-light displays and the confidence in these perceptions. Participants observed video scenes of human interactions, rated the depicted valence, and judged their confidence in this rating. Results showed that people with higher alexithymia scores were significantly less confident about their decisions, but did not differ from people with lower alexithymia scores in the valence of their ratings. Furthermore, no modulating effect of social context on the effect of higher alexithymia scores was found. It is concluded that the used stimuli are fit to investigate the kinematic aspect of emotion perception and possibly separate people with high and low alexithymia scores via confidence differences. However, a general difference in emotion perception was not detected in the present setting.
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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 | 0.001 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".