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Adapted to explore: Reinforcement learning in Autistic Spectrum Conditions
Authors:Eldad Yechiam  Olga Arshavsky  Simone G Shamay-Tsoory  Shoshana Yaniv  Judith Aharon
Institution:1. Technion – Israel Institute of Technology, Haifa, Israel;2. Haifa University and Rambam Medical Center, Haifa, Israel
Abstract:Recent studies have recorded a tendency of individuals with Autism Spectrum Conditions (ASC) to continually change their choices in repeated choice tasks. In the current study we examine if this finding implies that ASC individuals have a cognitive style that facilitates exploration and discovery. Six decision tasks were administered to adolescents with ASC and matched controls. Significant differences in shifting between choice options appeared in the Iowa Gambling task (Bechara, Damasio, Damasio, & Anderson, 1994). A formal cognitive modeling analysis demonstrated that for about half of the ASC participants the adaptation process did not conform to the standard reinforcement learning model. These individuals were only coarsely affected by choice-outcomes, and were more influenced by the exploratory value of choices, being attracted to previously un-explored alternatives. An examination of the five simpler decision tasks where the advantageous option was easier to determine showed no evidence of this pattern, suggesting that the shifting choice pattern is not an uncontrollable tendency independent of task outcomes. These findings suggest that ASC individuals have a unique adaptive learning style, which may be beneficial is some learning environment but maladaptive in others, particularly in social contexts.
Keywords:Autism  Reinforcement  Learning  Modeling  Decision making
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