Mouse tracking reveals structure knowledge in the absence of model-based choice.

ABSTRACT: Converging evidence has demonstrated that humans exhibit two distinct strategies when learning in complex environments. One is model-free learning, i.e., simple reinforcement of rewarded actions, and the other is model-based learning, which considers the structure of the environment. Recent work has argued that people exhibit little model-based behavior unless it leads to higher rewards. Here we use mouse tracking to study model-based learning in stochastic and deterministic (pattern-based) environments of varying difficulty. In both tasks participants' mouse movements reveal that they learned the structures of their environments, despite the fact that standard behavior-based estimates suggested no such learning in the stochastic task. Thus, we argue that mouse tracking can reveal whether

SUBMITTER: Konovalov A 

PROVIDER: S-EPMC7170897 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature