Developmental Differences in Probabilistic Reversal Learning: A Computational Modeling Approach.

ABSTRACT: Cognitive flexibility helps us to navigate through our ever-changing environment and has often been examined by reversal learning paradigms. Performance in reversal learning can be modeled using computational modeling which allows for the specification of biologically plausible models to infer psychological mechanisms. Although such models are increasingly used in cognitive neuroscience, developmental approaches are still scarce. Additionally, though most reversal learning paradigms have a comparable design regarding timing and feedback contingencies, the type of feedback differs substantially between studies. The present study used hierarchical Gaussian filter modeling to investigate cognitive flexibility in reversal learning in children and adolescents and the effect of various feedback

SUBMITTER: Weiss EO 

PROVIDER: S-EPMC7848134 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature