Proceedings of the Annual Meeting of the Cognitive Science Society

While many recent studies have successfully used reinforcement learning (RL) frameworks to explain large portions ofvariance within neurobiological and decision-making datasets, the relatability of such models to the true mechanisms anddynamics underlying human learning, cognition, and behavior is arguably still quite limited–in part due to the exclusion ofwell-defined mechanisms controlling the dynamics of sensory-model updating (particularly during exploratory behavior)and sensory-model extraction (for use of exploitative behavior) processes. In an attempt to mend this gap, the currentstudy investigates the diameter of the pupil as a potential signature of both ongoing sensory-model updating and sensory-model extraction processes. With the use of a hybrid Q-learning model, these hypothesized correlates are found to accountfor discrepancies in pupil diameter between model-based and model-free learning strategies during exploratory and ex-ploitative behavior, and simultaneously frame human learning experience as a dynamic interplay between sensory-modelupdating and recollection processes.