One caveat to this idea, however, is that dopamine works through metabotropic ion channels (Gazi and Strange, 2002), and the dynamics of the second messenger cascades activated by dopamine receptors are apparently not fast enough to affect neural activity on rapid time scales (Lavin Ivacaftor concentration et al., 2005). The effects could, however, be driven by glutamate co-released from dopamine neurons (Lavin et al., 2005). A second possibility is that the value signal is carried by the substantial input from the centromedian/parafascicular

(CM-PF) thalamic nuclei (Nakano et al., 1990). A majority of neurons in CM-PF respond when low-value actions are required (Matsumoto et al., 2001 and Minamimoto et al., 2005). An additional possibility is that the increased value representation is coming from other areas of frontal cortex, for example dorsal anterior cingulate projections to the striatum. This area has a strong value representation (Kennerley and Wallis, 2009), and it sends projections into the striatum that slightly overlap with the lPFC projection (Haber et al., 2006). The projections from this area, however, do not appear to project directly to the portion of the dorsal striatum from which we recorded (Haber et al., 2006). Overall, then, the mostly likely candidates for a fast value-related signal in the striatum would be glutamate coreleased from dopamine neurons, or the CM-PF input. Examination of the

neural representation of color bias and sequence in the fixed condition Linsitinib cell line showed that they followed complementary patterns, such that sequence information increased in lPFC and color

bias information decreased in dSTR as the monkeys learned within each block. The increase and decrease were significantly related to the relative behavioral weight of sequence and color information, estimated by a Bayesian behavioral model. Thus, when the sequence switched, the animals reverted to using either the pixel information as they relearned the sequence, and this could be seen in both the behavioral and neural data. As they learned the sequence they transitioned to using less pixel information, which was less accurate, and more sequence information, which was more accurate. This tradeoff is consistent, at a high level, with a model which has suggested that dual control systems, one in lPFC and one in the dSTR, compete for control of behavior (Daw et al., 2005). This model suggests that the tradeoff between these systems is mediated by optimal integration based on the uncertainty associated with the predictions of each system. In other words, if one system is producing uncertain estimates, it is weighted less in the decision process. Thus, our data is consistent with this aspect of the model. What is less clear from our data, however, is whether the dSTR does action selection when action values are high, and the sequences can be executed like habits. A different task structure might make this clearer.