01 < c < 0.99) to the choice probability values under each model and compared the resulting binary trial classification (model choices) to human choices. Resulting χ2 values for each model across values of c are shown for individual subjects in Figure 2C. Comparing find more models under best-fitting values of c, the WM model again outperformed the Bayesian (t(19) = 2.69; p < 0.05) and the QL models (t(19) = 2.87; p <

0.01) in pairwise comparison at the group level. The task was structured such that the true category statistics jumped every 10 or 20 trials. We wanted to determine whether participants learned this periodic jump structure, because if so, this could have disadvantaged the Bayesian model, which has no way of inferring the http://www.selleckchem.com/products/Vorinostat-saha.html periodic structure of the task. Our approach was 2-fold. First, we asked whether learning rates (fit by a simple delta rule) differed for the first 8 trials following switch (when an observer with full knowledge of the 10 trial cycle should not learn any new information), relative to trials 9–13 following a switch. In fact, participants learned faster immediately following a switch (t(19) =

3.15; p < 0.004)—behavior that is well captured by the WM model but that would not be approximated with a variant of the Bayesian model that optimally inferred the cyclic task structure. Second, we compared learning rates for different phases of a 10 trial harmonic across each run (i.e., trials 3–7, 13–17, 23–27, etc. versus trials 1–2, 8–12, 18–22, regardless of when jumps occurred). These revealed almost identical learning rates (0.73 versus 0.69, t(19) < 1). If participants had been explicitly using

knowledge about the structure of the sequence (to which the current Bayesian model has no access), then we would expect them to learn faster in a period where jumps were more probable. Together, these two results strongly suggest that participants do not learn the periodic structure of the task and that the Chlormezanone Bayesian model is not unfairly disadvantaged by being blind to the 10–20 trial jump cycle. In fact, because the Bayesian model outperforms the human participants, and a model with perfect knowledge of the jumps would perform even better, the latter would approximate human behavior yet more poorly. We converted choice probability values into a quantity that scales with the probability of making a correct response (Experimental Procedures) and correlated these choice values with trial-by-trial RT values for each participant (Figure 3A). Slopes were more negative for the WM model than the Bayesian (t(19) = 11.2; p < 1 × 10−9) and QL models (t(19) = 15.9; p < 1 × 10−12), suggesting that choice values from the WM model captured the most variability in RT (indeed, the slope for the QL model did not deviate significantly from zero: p = 0.48).