Reinforcement learning signals predict future decisions.
paper
Cited
Public
Unavailable
- Authors
- Cohen, Michael X; Ranganath, Charan
- Year
- 2007
- Journal
- The Journal of neuroscience : the official journal of the Society for Neuroscience
- PMID
- 17215398
- DOI
- 10.1523/JNEUROSCI.4421-06.2007
- PMCID
- PMC6672075
Optimal behavior in a competitive world requires the flexibility to adapt decision strategies based on recent outcomes. In the present study, we tested the hypothesis that this flexibility emerges through a reinforcement learning process, in which reward prediction errors are used dynamically to adjust representations of decision options. We recorded event-related brain potentials (ERPs) while subjects played a strategic economic game against a computer opponent to evaluate how neural responses to outcomes related to subsequent decision-making. Analyses of ERP data focused on the feedback-related negativity (FRN), an outcome-locked potential thought to reflect a neural prediction error signal. Consistent with predictions of a computational reinforcement learning model, we found that the magnitude of ERPs after losing to the computer opponent predicted whether subjects would change decision behavior on the subsequent trial. Furthermore, FRNs to decision outcomes were disproportionately larger over the motor cortex contralateral to the response hand that was used to make the decision. These findings provide novel evidence that humans engage a reinforcement learning process to adjust representations of competing decision options.
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| Title | Year | PMID |
|---|---|---|
| Reward processing deficits and impulsivity in high-risk offspring of alcoholics: A study of event-related potentials during a monetary gambling task. | 2015 | 26388585 |
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