Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.
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| Title: | Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems. |
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| Authors: | Kool, Wouter, Gershman, Samuel J., Cushman, Fiery A. |
| Source: | Psychological Science (0956-7976). Sep2017, Vol. 28 Issue 9, p1321-1333. 13p. 2 Diagrams, 2 Charts, 3 Graphs. |
| Subjects: | Arbitration & award, Reinforcement learning, Human behavior |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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