Perceptual discrimination of action formidableness and friendliness and the impact of autistic traits
Bibliographic record
Abstract
The ability to determine whether the actions of other individuals are friendly or formidable are key decisions we need to make to successfully navigate our complex social environment. In this study we measured perceptual performance when discriminating actions that vary in their friendliness or formidableness, and whether performance was related to the autistic traits of individuals. To do this, we developed an action morphing method to generate novel actions that lied along the action quality dimensions of formidableness and friendliness. In Experiment 1 we show that actions that vary along the formidableness or friendliness continua were rated as varying monotonically along the respective quality. In Experiment 2 we measured the ability of individuals with different levels of autistic traits to discriminate action formidableness and friendliness using adaptive 2-AFC procedures. We found considerable variation in perceptual thresholds when discriminating action formidableness (~ 540% interindividual variation) or friendliness (~ 1100% interindividual variation). Importantly, we found no evidence that autistic traits influenced perceptual discrimination of these action qualities. These results confirm that sensory enhancements with autistic traits are limited to lower level stimuli, and suggest that the perceptual processing of these complex social signals are not affected by autistic traits.
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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 | 0.005 |
| 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 | 0.001 |
| 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".