文章基本信息

标题：Neural signatures of reinforcement learning correlate with strategy adoption during spatial navigation
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作者：Dian Anggraini ; Stefan Glasauer ; Klaus Wunderlich 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2018
卷号：8
期号：1
页码：10110
DOI：10.1038/s41598-018-28241-z
语种：English
出版社：Springer Nature
摘要：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 RL algorithms. Furthermore, the value signals of model-free choices during route-based navigation modulated the BOLD signals in the ventro-medial prefrontal cortex (vmPFC), whereas the BOLD signals in parahippocampal and hippocampal regions pertained to model-based value signals during map-based navigation. Our findings suggest that the brain might share computational mechanisms and neural substrates for navigation and value-based decisions such that model-free choice guides route-based navigation and model-based choice directs map-based navigation. These findings open new avenues for computational modelling of wayfinding by directing attention to value-based decision, differing from common direction and distances approaches.