Journal
PSYCHOLOGICAL SCIENCE
Volume 28, Issue 9, Pages 1321-1333Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/0956797617708288
Keywords
reinforcement learning; decision making; cognitive control; open data; open materials
Categories
Funding
- Office of Naval Research [N00014-14-1-0800]
- Center for Brains, Minds and Machines
- National Science Foundation Science and Technology Center Award [CCF-1231216]
- Pershing Square Fund for Research on the Foundations of Human Behavior
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Human behavior is sometimes determined by habit and other times by goal-directed planning. Modern reinforcementlearning theories formalize this distinction as a competition between a computationally cheap but inaccurate modelfree system that gives rise to habits and a computationally expensive but accurate model-based system that implements planning. It is unclear, however, how people choose to allocate control between these systems. Here, we propose that arbitration occurs by comparing each system's task-specific costs and benefits. To investigate this proposal, we conducted two experiments showing that people increase model-based control when it achieves greater accuracy than model-free control, and especially when the rewards of accurate performance are amplified. In contrast, they are insensitive to reward amplification when model-based and model-free control yield equivalent accuracy. This suggests that humans adaptively balance habitual and planned action through on-line cost-benefit analysis.
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