An MEG signature corresponding to an axiomatic model of reward prediction error.

摘要: Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features an outcome: its inherent value and prior expectation occurrence. To uncover magnetic signature prediction in human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon axiomatic model (Caplin Dean, 2008a), determine presence timing profile MEG that express errors. We report analyses at sensor level, implemented SPM8, time locked outcome onset. identified, for first time, error, emerged approximately 320 ms after expressed as interaction between valence probability. This signal followed earlier, separate probability, 200 ms outcome. Strikingly, course error signal, well early resembled Feedback-Related Negativity (FRN). In simultaneously EEG obtained robust FRN, but win loss comprised this difference wave did not comply with model. findings motivate explicit examination critical issue embodied computational models seen electrophysiological data.