Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yuta Takahashi, Shingo Murata, Hayato Idei, Hiroaki Tomita, Yuichi Yamashita
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/767b485d95b94e68ba75dbc60c2b2426
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:767b485d95b94e68ba75dbc60c2b2426
record_format	dspace
spelling	oai:doaj.org-article:767b485d95b94e68ba75dbc60c2b24262021-12-02T18:46:57ZNeural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework10.1038/s41598-021-94067-x2045-2322https://doaj.org/article/767b485d95b94e68ba75dbc60c2b24262021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94067-xhttps://doaj.org/toc/2045-2322Abstract 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.Yuta TakahashiShingo MurataHayato IdeiHiroaki TomitaYuichi YamashitaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yuta Takahashi Shingo Murata Hayato Idei Hiroaki Tomita Yuichi Yamashita Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework
description	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.
format	article
author	Yuta Takahashi Shingo Murata Hayato Idei Hiroaki Tomita Yuichi Yamashita
author_facet	Yuta Takahashi Shingo Murata Hayato Idei Hiroaki Tomita Yuichi Yamashita
author_sort	Yuta Takahashi
title	Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework
title_short	Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework
title_full	Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework
title_fullStr	Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework
title_full_unstemmed	Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework
title_sort	neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/767b485d95b94e68ba75dbc60c2b2426
work_keys_str_mv	AT yutatakahashi neuralnetworkmodelingofalteredfacialexpressionrecognitioninautismspectrumdisordersbasedonpredictiveprocessingframework AT shingomurata neuralnetworkmodelingofalteredfacialexpressionrecognitioninautismspectrumdisordersbasedonpredictiveprocessingframework AT hayatoidei neuralnetworkmodelingofalteredfacialexpressionrecognitioninautismspectrumdisordersbasedonpredictiveprocessingframework AT hiroakitomita neuralnetworkmodelingofalteredfacialexpressionrecognitioninautismspectrumdisordersbasedonpredictiveprocessingframework AT yuichiyamashita neuralnetworkmodelingofalteredfacialexpressionrecognitioninautismspectrumdisordersbasedonpredictiveprocessingframework
_version_	1718377686777200640

Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework

Ejemplares similares