That is, not only must the control system determine what task is best to perform, but also the amount of control that must be allocated to that task so as to optimize EVC. This follows from the assumption that control is costly, as discussed earlier (see Figure 4). There is longstanding

evidence for adaptive adjustments in control in the behavioral literature, for example changes in the speed-accuracy tradeoff observed following errors in simple decision tasks (see Danielmeier and Ullsperger, 2011). Gratton et al. (1992) suggested that such adaptive adjustments extend to the allocation of attention, showing check details that the response to an incongruent stimulus is faster when it follows another incongruent stimulus than when it follows a congruent one. This was interpreted as evidence of an enhancement of attention to the task-relevant dimension in response to the interference produced by a prior incongruent one. In computational work, Botvinick et al. (2001) demonstrated that the behavioral effects described above could be explained by a mechanism that monitors conflict elicited by lapses in performance and/or interference and uses this to adjust the intensity of the task-relevant control signals in order to maintain task performance. However, the EVC model makes a stronger claim: that such adjustments

serve to optimize the allocation of control. A modest, but growing corpus of work has begun to address this stronger claim and its Obeticholic Acid relation to dACC function. Optimization of Control Intensity. The most extensive analyses of control optimization have focused on simple two-alternative choice tasks, such as those used to demonstrate adaptive changes in the speed-accuracy tradeoff mentioned above. Such tasks have been modeled extensively using simple accumulator models, in which the intensity of the control signal influences two parameters of the decision process:

the decision threshold and initial bias. Together, these determine the speed-accuracy tradeoff. Botvinick et al. (2001) showed Adenosine that monitoring response conflict in such models and using this to adjust the intensity of the control signal accurately accounted for adaptive changes in the speed-accuracy tradeoff observed in behavior. In that model, the intensity of the control signal determined the decision threshold. More recently, formal analyses by Bogacz et al. (2006) have shown that there is an optimal threshold (i.e., speed-accuracy tradeoff) that maximizes reward rate for a given set of task conditions, and similarly for initial bias. Furthermore, behavioral studies show that participants adapt their behavior to changes in task conditions in ways that often approximate adoption of the optimal threshold and bias (reviewed in Cohen and Holmes, 2013).