Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices.

ABSTRACT: The recent years have witnessed a dramatic increase in the use of reinforcement learning (RL) models in social, cognitive and affective neuroscience. This approach, in combination with neuroimaging techniques such as functional magnetic resonance imaging, enables quantitative investigations into latent mechanistic processes. However, increased use of relatively complex computational approaches has led to potential misconceptions and imprecise interpretations. Here, we present a comprehensive framework for the examination of (social) decision-making with the simple Rescorla-Wagner RL model. We discuss common pitfalls in its application and provide practical suggestions. First, with simulation, we unpack the functional role of the learning rate and pinpoint what could easily go wrong when in

SUBMITTER: Zhang L 

PROVIDER: S-EPMC7393303 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature