Developing a non-human primate model to dissect the neural mechanisms of facial emotion processing relevant in autism spectrum disorder
Bibliographic record
Abstract
Understanding facial expressions is crucial for human social interaction as they convey emotions and intentions. Autistic adults often show marked differences (compared to neurotypically developed adults) in interpreting these cues, impacting communication and empathy. To aid autistic individuals, it’s essential to comprehend the neural basis behind these differences. However, the diverse nature of behavioral reports in autism impedes efficient study design. Current models for interpreting facial emotion judgments overlook individual image-level sensory representations, pivotal in understanding differences between neurotypical and autistic adults. In a recent study by Kar (2022), behavioral disparities between these groups were more evident at the image level rather than through broad categorical descriptors like “happiness” or “fear.” To investigate this further, we established an image-level framework using 360 diverse facial expression images from the Montreal Set of Facial Displays of Emotion (MSFDE). Through binary emotion discrimination tasks, we observed subtle yet significant differences in image-level behavioral error patterns between neurotypical and autistic individuals. Addressing the challenge of heterogeneity, we pinpointed shared variances in our developed image-level metrics, serving as a critical behavioral benchmark. To delve into the neural underpinnings, we conducted extensive neural recordings in the inferior temporal (IT) cortex of rhesus macaque monkeys. Initial findings align with previous predictions (Kar 2022), indicating stronger correlations between macaque IT-based decodes of facial emotion responses and neurotypical behavior compared to autistic behaviour. Our study aims to create an innovative framework merging non-human primate neural investigations with the autistic behavioral phenotype. By focusing on shared variances in image-level behavioral metrics, we aim to identify more sensitive neurobehavioral markers
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".