Abstract
A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. In these accounts, a flexible but computationally expensive model-based reinforcement-learning system has been contrasted with a less flexible but more efficient model-free reinforcement-learning system. The factors governing which system controls behavior-and under what circumstances-are still unclear. Following the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrated that having human decision makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement-learning strategy. Further, we showed that, across trials, people negotiate the trade-off between the two systems dynamically as a function of concurrent executive-function demands, and people's choice latencies reflect the computational expenses of the strategy they employ. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources.
Original language | English (US) |
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Pages (from-to) | 751-761 |
Number of pages | 11 |
Journal | Psychological Science |
Volume | 24 |
Issue number | 5 |
DOIs | |
State | Published - May 2013 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- General Psychology
Keywords
- cognitive neuroscience
- decision making