Neural signatures of reinforcement learning correlate with strategy adoption during spatial navigation.

ABSTRACT: Human navigation is generally believed to rely on two types of strategy adoption, route-based and map-based strategies. Both types of navigation require making spatial decisions along the traversed way although formal computational and neural links between navigational strategies and mechanisms of value-based decision making have so far been underexplored in humans. Here we employed functional magnetic resonance imaging (fMRI) while subjects located different objects in a virtual environment. We then modelled their paths using reinforcement learning (RL) algorithms, which successfully explained decision behavior and its neural correlates. Our results show that subjects used a mixture of route and map-based navigation and their paths could be well explained by the model-free and model-based

SUBMITTER: Anggraini D 

PROVIDER: S-EPMC6031619 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature