The association between socio-communicative traits associated with Autism and pupillary responses to dynamic, audiovisual emotional speech
Bibliographic record
Abstract
Autistic individuals commonly exhibit difficulties with emotion recognition, difficulties that contribute to social issues in Autism. Both emotion recognition and social abilities are distributed along a spectrum in Autism and the general population. We explored this relationship between emotional processing, as measured via the physiological response of pupil dilation, and Autistic traits, including social abilities. We presented participants with dynamic, audiovisual stimuli of actors uttering a semantically neutral phrase. These utterances were expressed with either neutral tone of voice and facial expression, or with angry, fearful, disgusted, sad, or surprised tone and expression. Each emotion was presented with high- and low-intensity depictions. Participants were asked to identify the emotion and rate its intensity. Participants' pupillary responses were recorded during viewing. Traits associated with Autism were measured through a battery of self-report scales. We then tested for associations between Autistic traits and pupillary response to emotional stimuli. Broadly speaking, individuals with higher Autistic traits exhibited smaller overall pupillary responses to emotional stimuli. More specifically, emotional responses were related to socio-communicative Autistic traits. Further, social traits were especially correlated with physiological responses to low-intensity emotional presentation. This reflects previous studies suggesting that Autistic individuals show more pronounced difficulties with emotion recognition when emotional expression is more subtle. Finally, social Autistic traits were related to physiological responses to anger, fear, surprise, and to a lesser extent happiness, but not to sadness or disgust.
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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 | 0.003 |
| 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 | 0.000 | 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".