The Influence of Postural Emotion Cues on Implicit Trait Judgements
Bibliographic record
Abstract
Perceptions of traits (such as trustworthiness or dominance) are influenced by the emotion displayed on a face. For instance, the same individual is reported as more trustworthy when they look happy than when they look angry. This overextension of emotional expressions has been shown with facial expression but whether this phenomenon also occurs when viewing postural expressions was unknown. We sought to examine how expressive behaviour of the body would influence judgements of traits and how sensitivity to this cue develops. In the context of a storybook, adults (N = 35) and children (aged 5 to 8 years; N = 60) selected one of two partners to help face a challenge. The challenges required either a trustworthy or dominant partner. Participants chose between a partner with an emotional (happy/angry) face and neutral body or one with a neutral face and emotional body. As predicted, happy over neutral facial expressions were preferred when selecting a trustworthy partner and angry postural expressions were preferred over neutral when selecting a dominant partner. Children’s performance was not adult-like on most tasks. The results demonstrate that emotional postural expressions can also influence judgements of others’ traits, but that postural influence on trait judgements develops throughout childhood.
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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 | 0.012 |
| 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 | 0.000 |
| 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".