Recording brain activity during the task with functional magnetic resonance imaging (fMRI) allowed us to compare observed choices, decision latencies, and brain activity to those predicted by three computational models that embodied different hypotheses about how humans learn about and choose between categories. The first model learned the mean and variance of the categories in an optimal Bayesian framework (Bayesian model), the second model learned the value of action in a given

state, i.e., angle (Q-learning [QL] model), and the third model simply maintained the most recent category information in memory (working memory [WM] model). These models allow us to compare the hypotheses that category judgments in an unpredictable environment are driven by strategies that rely on “model-based” optimal estimation buy Apoptosis Compound Library of uncertainty (Bayesian), “model-free” habit learning (Q-learning), or a cognitive strategy based on short-term maintenance (working memory). We report a number of new findings. First, both the Bayesian and the WM models encoded unique variance in choice, reaction time (RT), and brain activity, suggesting that participants use a mixture of model-based categorization strategies. Second, participants’ tendency to use a decision policy Selleckchem PLX4032 that incorporated category

variance depended aminophylline on the volatility of the environment, with the Bayesian model approximating human performance more closely in relatively unchanging environments, and neural signatures of choice and learning modulated by category variability only during stable periods; by contrast, the WM model prevailed when the environment was more volatile. Finally, different strategies

were associated with dissociable patterns of decision-related brain activity, with fMRI signals predicted by the Bayesian model observed in the striatum and medial prefrontal cortex (PFC), but brain activity predicted by the working memory strategy activating visual regions, and the dorsal frontal and parietal cortex. Together, these results suggest that participants use cognitive strategies involving the short-term maintenance of information when making decisions in volatile environments but gradually come to rely on information about category uncertainty to make more optimal choices as learning progresses. On each of 600 trials, 20 participants viewed an oriented stimulus (full-contrast Gabor patch) that was drawn from one of two categories defined by orientation, with angular means on trial i   of μˆia and μˆib and variances σˆia and σˆib ( Figure 1A). Subjects received no instructions regarding the categories but were required to learn about them by trial and error via an auditory feedback tone following each decision epoch of 1500 ms.