Individual differences in antisocial and prosocial traits predict perception of dynamic expression
Bibliographic record
Abstract
Successful everyday, social interactions are mediated by the accurate perception of dynamic facial expressions. For example, the decoding of distress cues have been shown to inhibit antisocial behaviour while simultaneously eliciting empathetic responses. The import of this recognition-behaviour connection has been revealed by psychiatric research demonstrating that disorders characterized by interpersonal deficits are associated with impairments in processing facial affect. This area of research has generated speculation that the neurocognitive mechanisms specific to social behaviour are also pivotally involved in expression recognition, but it is unknown whether this relationship also holds in nonclinical populations. To explore this question, we examined how individual differences in antisocial traits relate to the recognition of facial affect. Antisocial behaviour was assessed using the Inventory for Callous-Unemotional Traits (ICU, Kimonis et al., 2008), a scale which designates a subgroup of antisocial individuals who are more likely to show deficits in processing emotional stimuli relative to other antisocial individuals. Perceptual sensitivity to facial affect was probed using a Dynamic Expression Recognition Task (DERT, Deriso et al., 2012) in which participants were shown 75ms, 150ms, or 225ms reveals of a dynamic face morph progressing from neutral to one of sad, happy, angry, fear, surprise, or disgust. The main finding was that participants who scored higher in callous-unemotional traits were significantly less accurate in expression recognition compared to participants who scored low on the ICU. This result supports the hypothesis for a common mechanism underlying affect recognition and antisocial behaviour across clinical and nonclinical groups, while also highlighting the diagnostic potential of facial affect processing. Meeting abstract presented at VSS 2015
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".