Simulating Attention Disorder in Autistic Patients based on a Computational Model with Neural Networks Reinforcement Learning Approach

Abstract

Autism is an advanced neurological disease that affect communication and social behaviors, including attention -one of the fundamental skills to learn about the world around us. Autistic people have difficulty moving their attention from one point to another fluently. Due to the high prevalence of autism and its increasing progression, and the need to address common disorders in patients, this study aimed to implement and simulate a computational model for attention deficit disorder in autistic patients using MATLAB. This computational model has three components: context-sensitive reinforcement learning, contextual processing, and automation that can teach a shift-shift task automatically. At first, the model functions like normal people, but its performance gets closer to autistic people after changing a single parameter. This study demonstrates that even a simple computational model can be used for normal and abnormal developmental cases using a neural network reinforcement learning approach and provide valuable insights into autism.