Similarly, McGuire and Botvinick (2010) found that the degree to which performance

of a cognitively demanding task engaged dACC predicted the extent to which that same task would later be avoided. Collectively, these findings are consistent not only with dACC encoding of control costs, but also with a role for dACC in cost-sensitive control signal specification. Figure 4 illustrates how the optimal control signal intensity predicted by the EVC model is determined by the relationships of control costs and payoffs to control signal intensity. These relationships determine the function relating EVC click here to intensity, and the optimum occurs at a point where the slope of that function is zero. Under plausible assumptions about the shape of the payoff check details and cost functions (see Kool and Botvinick, 2012), the optimal control signal intensity will rise with the magnitude of task incentives (see Figures 4A and 4B). This predicts that dACC activity should

grow both with task difficulty and with the stakes associated with task performance. This dual effect was reported by Kouneiher and colleagues (2009), who had participants perform a series of trials in which a colored letter cued them to perform a letter discrimination task or to simply press a single key unrelated to letter identity (“default” trials). Each trial was also cued with whether or not a correct response would carry a monetary bonus, and the value of these bonuses differed by trial block. The authors found that dACC activity increased with the difficulty of the trial as well as with the average stakes for the trial block (regardless of whether a bonus was available on a particular

unless trial; see Figures 4C and 4D). The prediction of a monotonic relationship between control-signal intensity and the cost of control means that the output of the dACC can be interpreted in either of two ways: as directly reflecting the specified intensity of the current control signal, or as indirectly reflecting the cost that has been licensed for this control signal. The latter follows from the assumption of the EVC model that the dACC specifies the optimal control signal; accordingly, its intensity should indicate the amount of control that was determined to be “worth” the expected payoff. This relationship between intensity and cost can be understood by analogy to the economic concept known as willingness-to-pay, which refers to the amount worth trading for a good. Recent work has characterized orbitofrontal cortex as carrying a willingness-to-pay signal during economic choice (Levy and Glimcher, 2012, Padoa-Schioppa, 2011 and Plassmann et al., 2007). The EVC theory suggests that the output of dACC can be thought of as a willingness-to-pay signal in the currency of cognitive control.