Perception of Emotion in Autism with Controlled Elicitation (PEACE)
Bibliographic record
Abstract
It is possible that autistic people find it easier to socialize within their own peer group (Beaupré & Hess, 2006), by recognizing socio-emotional nonverbal cues, due to a shared neurodivergence. To assess this, it would be helpful for researchers to have a database of emotional stimuli, validated for use with autistic individuals. Unfortunately, this does not exist. This current study’s objective is to create such a database that evokes natural emotion, rated by autistic individuals to be used in future research. All participants completed the Alexithymia Scale (TAS-20) which measures the participants’ ability to identify and describe their own emotions (Taylor et al., 2003), the Marlowe-Crowne Social Desirability Scale to ensure participants are answering authentically (Crowne & Marlowe, 1960), and the SRS-2 scale which indicates the participants’ social ability (Constantino et al., 2003). Participants watched 45 short videos (Ack Baraly, 2020) chosen to elicit either a positive, negative, or neutral reaction. After each video, participants reported emotional responses using the Affect, Anxiety, Pride, and Energy (AAPE) scale (Riccio, 2020). Preliminary analysis included 70 participants. 41% of participants scored above the SRS-2 clinical cutoff. Participants' videos were then clustered using k-means, based on the ratings and SRS-2 scores. Results reveal both overlap and nonintersecting responses between participants above and below the SRS-2 cutoff..The next step is to use hierarchical analysis within each k-means cluster, allowing exploration of the variability. Further insight on these differences will allow production of a video database labeled with the emotions they evoke in autistic participants.
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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 | 0.003 |
| 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 | 0.001 |
| 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".