In sharp Gamma-secretase inhibitor contrast to real feedback, we observed an early occipital PE-related EEG modulation following fictive feedbacks that even precedes the FRN time window, which has previously been interpreted as the fastest cortical correlate of feedback processing (Gehring and Willoughby, 2002 and Philiastides et al., 2010). Its very short latency and localization to extrastriate visual areas and

PMC (Figure S2A) seem to suggest that fictive outcomes engage a specific mechanism that might ease counterfactual learning. Although EEG does not allow precise localization, the found source fits well with findings from fMRI studies in which PMC has been associated with tracking values and PE signals of alternative unchosen options coding a counterfactual PE (Boorman et al., 2011). In monkeys (Leichnetz, 2001) and humans (Mars et al., 2011), the PMC is intensely interconnected with the more lateral part of the parietal cortex that has been shown to code fictive PE signals this website defined as the value difference between outcomes that could have been attained by optimal investments and actually attained outcomes (Chiu et al., 2008 and Lohrenz et al., 2007).

Furthermore, afferent projections from the basal forebrain as well as reciprocal projections with the anterior cingulate cortex shown in macaques (Parvizi et al., 2006) permit a role of the PMC in value processing and a causal role in choice behavior has been shown by microstimulation of this region in monkeys that leads to behavioral adaptation (Hayden et al., 2008). Additionally, the PMC has been suggested as part of a network tracking evidence for future adaptations to pending options (Boorman et al., 2011) in humans. Importantly, our results presented here differ from these previous findings, since we describe how the same stimulus value representation is updated by different signals depending only on whether feedback was fictive or real. We suggest that this

signal might reflect a process that converts fictive outcomes to subjective value signals (Gold and Shadlen, 2007), effectively facilitating counterfactual learning that can more easily guide subsequent decisions. This fictive PE effect cannot be interpreted as a surprise signal (Ferdinand et al., 2012), as it was not unaffected when outcome and surprise, measured as the absolute PE value, were included into the same regression model (Figures S3E and S3F). Additionally, the effect cannot be interpreted as a consequence of repetition suppression (Summerfield et al., 2008), as it would then be expected to also occur following real feedback. In order to further disentangle contributing factors of the different PE correlates, we decomposed the PE into its components—the outcome and the expected value—and submitted both to the same multiple regression analysis.