Reinforcement learning signals predict future decisions.

@article{citeulike:1115478, abstract = {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.}, address = {Department of Epileptology and Center for Mind and Brain, University of Bonn, 53105 Bonn, Germany. mcohen@ucdavis.edu}, author = {Cohen, M. X. and Ranganath, C. }, citeulike-article-id = {1115478}, doi = {10.1523/JNEUROSCI.4421-06.2007}, issn = {1529-2401}, journal = {J Neurosci}, keywords = {decision, eeg, games, human, reinforcement-learning}, month = {January}, number = {2}, pages = {371--378}, posted-at = {2007-07-30 10:51:55}, priority = {2}, title = {Reinforcement learning signals predict future decisions.}, url = {http://dx.doi.org/10.1523/JNEUROSCI.4421-06.2007}, volume = {27}, year = {2007} }

TY - JOUR ID - citeulike:1115478 L3 - citeulike-article-id:1115478 TI - Reinforcement learning signals predict future decisions. JF - J Neurosci VL - 27 IS - 2 SP - 371 EP - 378 AD - Department of Epileptology and Center for Mind and Brain, University of Bonn, 53105 Bonn, Germany. mcohen@ucdavis.edu SN - 1529-2401 N2 - 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. KW - decision KW - eeg KW - games KW - human KW - reinforcement-learning AU - Cohen, MX AU - Ranganath, C PY - 2007/01/10/ UR - http://dx.doi.org/10.1523/JNEUROSCI.4421-06.2007 ER -