Iterated Rock-Scissors-Paper Game Play Predictive of Subscores of Self-Reported Measure of Autistic Personality Traits

Abstract

AbstractQuantification of human behavior in a social context may lead to discovery of subtle behavioral variations in a population that can be used to improve classification and screening for psychiatric disorders, and provide more accurate targeting in the development of interventions and biomedical treatments. However, it is difficult to study social interaction in a controlled, reproducible environment, as well as analyze the resulting behavior. In this research, we describe an experimental framework that utilizes a game of iterated Rock-Scissors-Paper played against an artificial intelligence agent, and a behavioral hypothesis of rule-switching to motivate analytical methods, that will extract behavioral features from game data. Subjects in the study also completed the Autism Quotient Abridged survey, and subscores from the survey were found to be predicted these behavioral features. Finding quantifiable, observable behavior that displays a spectrum in a population may be useful to differentiate and diagnose psychiatric illness.