Reinforcement Learning With Parsimonious Computation and a Forgetting Process.

ABSTRACT: Decision-making is assumed to be supported by model-free and model-based systems: the model-free system is based purely on experience, while the model-based system uses a cognitive map of the environment and is more accurate. The recently developed multistep decision-making task and its computational model can dissociate the contributions of the two systems and have been used widely. This study used this task and model to understand our value-based learning process and tested alternative algorithms for the model-free and model-based learning systems. The task used in this study had a deterministic transition structure, and the degree of use of this structure in learning is estimated as the relative contribution of the model-based system to choices. We obtained data from 29 participants and

SUBMITTER: Toyama A 

PROVIDER: S-EPMC6520826 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature