Neural network model shows why people with autism read facial
expressions differently

Date:
August 5, 2021
Source:
Tohoku University
Summary:
People with autism spectrum disorder interpret facial expressions
differently. Researchers have revealed more about how this comes
to be.

They induced abnormalities into a neural network model to explore
the effects on the brain's learning development.



FULL STORY ========================================================================== People with autism spectrum disorder have difficulty interpreting facial expressions.


========================================================================== Using a neural network model that reproduces the brain on a computer,
a group of researchers based at Tohoku University have unraveled how
this comes to be.

The journal Scientific Reports published the results on July 26, 2021.

"Humans recognize different emotions, such as sadness and anger by looking
at facial expressions. Yet little is known about how we come to recognize different emotions based on the visual information of facial expressions,"
said paper coauthor, Yuta Takahashi.

"It is also not clear what changes occur in this process that leads
to people with autism spectrum disorder struggling to read facial
expressions." The research group employed predictive processing theory
to help understand more. According to this theory, the brain constantly predicts the next sensory stimulus and adapts when its prediction is
wrong. Sensory information, such as facial expressions, helps reduce
prediction error.

The artificial neural network model incorporated the predictive processing theory and reproduced the developmental process by learning to predict how parts of the face would move in videos of facial expression. After this,
the clusters of emotions were self-organized into the neural network
model's higher level neuron space -- without the model knowing which
emotion the facial expression in the video corresponds to.

The model could generalize unknown facial expressions not given in the training, reproducing facial part movements and minimizing prediction
errors.

Following this, the researchers conducted experiments and induced
abnormalities in the neurons' activities to investigate the effects
on learning development and cognitive characteristics. In the model
where heterogeneity of activity in neural population was reduced, the generalization ability also decreased; thus, the formation of emotional clusters in higher-level neurons was inhibited. This led to a tendency to
fail in identifying the emotion of unknown facial expressions, a similar symptom of autism spectrum disorder.

According to Takahashi, the study clarified that predictive processing
theory can explain emotion recognition from facial expressions using a
neural network model.

"We hope to further our understanding of the process by which humans
learn to recognize emotions and the cognitive characteristics
of people with autism spectrum disorder," added Takahashi. "The
study will help advance developing appropriate intervention
methods for people who find it difficult to identify emotions." ========================================================================== Story Source: Materials provided by Tohoku_University. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. 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. Scientific Reports, 2021; 11 (1) DOI:
10.1038/s41598-021- 94067-x ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210805115455.htm

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