Feedback-related Negativity Codes Prediction Error but Not Behavioral Adjustment during Probabilistic Reversal Learning

• Published in: Journal of cognitive neuroscience Vol. 23; no. 4; pp. 936 - 946
• Main Authors: Chase, Henry W., Swainson, Rachel, Durham, Lucy, Benham, Laura, Cools, Roshan
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.04.2011; MIT Press Journals, The
• Subjects: Adaptation, Psychological - physiology; Analysis of Variance; Brain; Brain Mapping; Cognition & reasoning; Electroencephalography - methods; Evoked Potentials, Visual - physiology; Feedback, Psychological; Female; Humans; Learning; Male; Models, Statistical; Neurosciences; Photic Stimulation - methods; Physiology; Predictive Value of Tests; Reinforcement, Psychology; Reversal Learning - physiology; Sensory perception; Time Factors; Young Adult
• ISSN: 0898-929X; 1530-8898; 1530-8898
• DOI: 10.1162/jocn.2010.21456

We assessed electrophysiological activity over the medial frontal cortex (MFC) during outcome-based behavioral adjustment using a probabilistic reversal learning task. During recording, participants were presented two abstract visual patterns on each trial and had to select the stimulus rewarded on 80% of trials and to avoid the stimulus rewarded on 20% of trials. These contingencies were reversed frequently during the experiment. Previous EEG work has revealed feedback-locked electrophysiological responses over the MFC (feedback-related negativity; FRN), which correlate with the negative prediction error [Holroyd, C. B., & Coles, M. G. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. 679–709, 2002] and which predict outcome-based adjustment of decision values [Cohen, M. X., & Ranganath, C. Reinforcement learning signals predict future decisions. 371–378, 2007]. Unlike previous paradigms, our paradigm enabled us to disentangle, on the one hand, mechanisms related to the reward prediction error, derived from reinforcement learning (RL) modeling, and on the other hand, mechanisms related to explicit rule-based adjustment of actual behavior. Our results demonstrate greater FRN amplitudes with greater RL model-derived prediction errors. Conversely expected negative outcomes that preceded rule-based behavioral reversal were not accompanied by an FRN. This pattern contrasted remarkably with that of the P3 amplitude, which was significantly greater for expected negative outcomes that preceded rule-based behavioral reversal than for unexpected negative outcomes that did not precede behavioral reversal. These data suggest that the FRN reflects prediction error and associated RL-based adjustment of decision values, whereas the P3 reflects adjustment of behavior on the basis of explicit rules.