AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism
Understanding perceptual differences between autistic and neurotypical adults requires behavioral assays that are sensitive, reliable, and mechanistically informative. Facial emotion perception is a useful test case because group differences have been reported, but findings vary across studies. Here we show that this variability may reflect image-level sparsity: autistic-neurotypical differences in emotion judgments were concentrated in a small subset of diagnostic facial expressions rather than spread uniformly across stimuli. We trained population-specific artificial neural network models to predict image-level judgments for autistic and neurotypical participants, then used these models...
| Authors | Kushin Mukherjee, Na Yeon Kim, Maren Wehrheim et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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