Abstract
Recognizing others' emotions based on facial expressions is a core component of human social interactions. Previous studies (Wang and Adolphs 2017) have suggested that autistic individuals show differences in their facial emotion recognition compared to neurotypical adults. What are the neural mechanisms that account for these observed differences? Here we lay the groundwork for a new approach combining cutting-edge computational and empirical non-human primate work to test theories of atypical facial emotion recognition in autistic adults. In a recent study, the author(s) observed that artificial neural network (ANN) models of vision developed to achieve a myriad of visual objectives (e.g., object, emotion and face identification) could be fine-tuned to perform facial emotion judgments. Interestingly, the ANNs' image-level behavioral patterns better matched the neurotypical subjects' compared to autistic adults. This behavioral mismatch was most remarkable when the ANN behavior was constructed from units that correspond to the primate inferior temporal (IT) cortex. Here we directly test these two predictions in the rhesus macaques. First, we trained two macaques to perform a binary facial emotion (happy vs. fearful) discrimination task. Consistent with ANN predictions, the macaque image-level behavioral patterns better matched the behavior obtained in human Controls than in autistic individuals. Second, we implanted multi-electrode arrays in the IT cortex of two macaques and performed large-scale neural recordings while they fixated on images (used in the Wang and Adolphs study). Using the recorded neural multiunit spiking activity, we built regression models (165 IT-based models tested) to predict facial emotion ground truth ("level of happiness") on held-out images. Consistent with ANN-IT predictions, macaque IT population decodes of facial emotions better matched the neurotypical behavior compared to autistic individuals. Our results, therefore, establish the rhesus macaque as an appropriate species to further probe the neurobehavioral markers with ANN-guided hypotheses and experiment design.