From this perspective, cognitive control can be viewed not only as adaptive, but also as motivated. An Selleckchem FG-4592 emphasis on motivation also aligns with the ubiquitous observation that the exertion of cognitive control carries an inherent subjective cost. From the earliest definitions, controlled processing was described as effortful, and

like physical effort, mental effort is assumed to carry intrinsic disutility. That is, people spontaneously seek to minimize it. Recent empirical work bears out this assumption, linking effort specifically to the exertion of cognitive control (Kool and Botvinick, 2012 and Kool et al., 2010). Human decision makers show a bias against tasks demanding top-down control, and within certain bounds they will delay task goals or even forego reward in order to avoid such tasks (Dixon and Christoff, 2012, Kool et al., 2010 and Westbrook et al., 2013). These effects imply an intrinsic “cost of control,” which scales with the intensity of the control required to perform the task (Dixon and Christoff, 2012 and Kool et al., 2010). These ideas, combined with the idea that control signals are specified based on the reward potential of the task they support, suggest that the allocation of control is driven by a cost-benefit analysis, weighing potential payoffs

against attendant costs, including those inherently associated with the exertion of control itself. Previous work has established links between components of the Stroop model and specific neural structures involved in cognitive control. In particular, Androgen Receptor Antagonist concentration lateral prefrontal cortex (lPFC) together with associated structures (e.g., basal ganglia and brainstem dopaminergic nuclei) have been proposed to implement the regulative component of the model Adenylyl cyclase (Braver and Cohen, 2000, Cohen and Servan-Schreiber, 1992, Frank et al., 2001 and Miller and Cohen, 2001), while dACC has been proposed

to implement the monitoring component (Botvinick, 2007, Botvinick et al., 2001 and Botvinick et al., 2004). According to this mapping, the key step of control-signal specification arises in the communication from dACC to lPFC (Botvinick et al., 2001 and Kerns et al., 2004). That is, the model assigns to the dACC responsibility for monitoring and specification, evaluating current demands for control and using the relevant information to decide how to allocate control. The specified control signals are then implemented by lPFC and associated structures, which are assumed to be responsible for the regulative function of control—that is, actually effecting the changes in processing required to perform the task. The EVC model elaborates this proposal, structuring it in a normative description of how both the identity and the intensity of control signals are determined and placing new emphasis on optimization (i.e., reward maximization) in understanding the relationship, within dACC, between monitoring and specification.