The structure of reinforcement-learning mechanisms in the human brain. (February 2015)

• Record Type: Journal Article
• Authors: O'Doherty, John P; Lee, Sang Wan; McNamee, Daniel
• Is Part Of: Current opinion in behavioral sciences. Volume 1(2015)
• Page Start: 94
• Page End: 100
• Publication Date: 2015-02
• Subjects: Psychology -- Periodicals; 150.5
• DOI: 10.1016/j.cobeha.2014.10.004
• Languages: English
• ISSNs: 2352-1546

Abstract

Highlights: Reinforcement-learning algorithms are a useful framework for investigating the neurobiology of action selection for reward. Multiple approaches have been taken in the literature, including the distinction between model-based and model-free RL, hierarchical reinforcement-learning and state-space or structure learning. There is evidence for both model-based and model-free RL signals in the human striatum, parietal and frontal cortices and elsewhere. Arbitration between model-based and model-free RL mechanisms may involve regions of lateral and frontopolar cortex. Each of these approaches focus on different problems in neural RL, and an outstanding challenge is to find a way to integrate them. Abstract : Here we review recent developments in the application of reinforcement-learning theory as a means of understanding how the brain learns to select actions to maximize future reward, with a focus on human neuroimaging studies. We evaluate evidence for the distinction between model-based and model-free reinforcement-learning and their arbitration, and consider hierarchical reinforcement-learning schemes and structure learning. Finally we discuss the possibility of integrating across these different domains as a means of gaining a more complete understanding of how it is the brain learns from reinforcement.