Abstract
The mechanism underlying the emergence of emotional categories from visual facial expression information during the developmental process is largely unknown. Therefore, this study proposes a system-level explanation for understanding the facial emotion recognition process and its alteration in autism spectrum disorder (ASD) from the perspective of predictive processing theory. Predictive processing for facial emotion recognition was implemented as a hierarchical recurrent neural network (RNN). The RNNs were trained to predict the dynamic changes of facial expression movies for six basic emotions without explicit emotion labels as a developmental learning process, and were evaluated by the performance of recognizing unseen facial expressions for the test phase. In addition, the causal relationship between the network characteristics assumed in ASD and ASD-like cognition was investigated. After the developmental learning process, emotional clusters emerged in the natural course of self-organization in higher-level neurons, even though emotional labels were not explicitly instructed. In addition, the network successfully recognized unseen test facial sequences by adjusting higher-level activity through the process of minimizing precision-weighted prediction error. In contrast, the network simulating altered intrinsic neural excitability demonstrated reduced generalization capability and impaired emotional clustering in higher-level neurons. Consistent with previous findings from human behavioral studies, an excessive precision estimation of noisy details underlies this ASD-like cognition. These results support the idea that impaired facial emotion recognition in ASD can be explained by altered predictive processing, and provide possible insight for investigating the neurophysiological basis of affective contact.
Highlights
The mechanism underlying the emergence of emotional categories from visual facial expression information during the developmental process is largely unknown
It is noteworthy that the emotional labels were not provided to the S-CTRNNPB in the learning process, and higher-level neuronal representations were self-organized based on the similarity among the sensory inputs
To the best of our knowledge, the current study is the first to evaluate facial emotion recognition based on a predictive processing framework related to autism spectrum disorder (ASD)
Summary
The mechanism underlying the emergence of emotional categories from visual facial expression information during the developmental process is largely unknown. Consistent with previous findings from human behavioral studies, an excessive precision estimation of noisy details underlies this ASD-like cognition These results support the idea that impaired facial emotion recognition in ASD can be explained by altered predictive processing, and provide possible insight for investigating the neurophysiological basis of affective contact. The neural basis of facial emotion recognition has been intensively investigated by functional neuroimaging studies in healthy subjects[5,6,7] These previous studies have revealed a hierarchical structure among several brain regions; namely, the activities in the visual cortex correspond to the processing of lower-level sensory information, including features of faces, and the activity patterns in higher-level brain areas such as the fusiform gyrus or superior temporal sulcus correspond to the emotion category[6, 7]. The current study investigated the relationship between the alteration in emotional recognition in ASD and the estimation of sensory precision using computational psychiatry methods
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