, 2009). Similarly, in the medial PFC, the Bayesian model predicted decision-related activity in the SMA, and the WM model in the pre-SMA. Although the relative functional significance of the SMA and pre-SMA remains controversial

(Nachev et al., 2008), one possibility is that there exists a rostro-caudal gradient in the medial PFC, by which more anterior regions respond more vigorously when decisions are based on motivational information that is more conditionally complex (Badre and D’Esposito, 2009 and Nachev et al., 2009), or that arose further in the past (Kouneiher et al., 2009 and Summerfield and Koechlin, 2009). One interpretation find more of these findings is that cognitive strategies for categorization in a volatile environment involve maintaining recent exemplar-based representations active across several intervening trials at the expense of their competitors and thus recruit PFC structures known to support cognitive and motivational control across a discrete behavioral episode. By contrast, when decisions are XAV-939 datasheet made without the benefit of explicit working memory information, but based on approximations of the mean and variance of the categories, they recruit more posterior

zones within the PFC, as well as the striatum. The latter finding is surprising from the perspective of theories that have emphasized a role for the basal ganglia in habit learning (Daw et al., 2005 and Dickinson and Balleine, 2002) but squares well with the finding that the risk associated with an economic prospect (i.e., the variance of the outcome) scales with signals in both caudate and putamen (Preuschoff et al., 2006 and Schultz et al., 2008). Indeed, dopaminoceptive neurons of the striatum are known to encode uncertainty

associated with an economic decision, in addition to its value (Bunzeck et al., 2010, Fiorillo et al., 2003 and Tobler et al., 2007); not our results imply that this may extend to situations where the uncertainty pertains to the category of the stimulus. Finally, the QL model activated preferentially the left ventrolateral PFC, in tune with a substantial literature implicating this region in stimulus-response learning (Toni et al., 2001). Our analysis of the interaction between model fits and environmental stability/volatility offered insight into the factors that prompt participants to switch between memory-based and higher-order learning strategies for categorical choice. Specifically, the Bayesian model (but not the WM model) fit the choice data better when the environmental volatility is low, as if participants gradually acquired information about category variances. In our task, “stable” environments consisted of runs of 20–30 trials in which the category mean remained constant—a far shorter training interval than the hundreds or even thousands of trials offered in many categorization or sensorimotor tasks (Ashby and Gott, 1988 and Körding and Wolpert, 2004).