Sometime in the past two decades, neuroimaging and behavioral research converged on pFC as an important locus of cognitive control and decision-making, and that seems to be the last thing anyone has agreed on since. Every year sees an increase in the number of roles and functions attributed to distinct subregions within pFC, roles that may explain behavior and neural activity in one context but might fail to generalize across the many behaviors in which each region is implicated. Emblematic of this ongoing proliferation of functions is dorsal ACC (dACC). Novel tasks that activate dACC are followed by novel interpretations of dACC function, and each new interpretation adds to the number of functionally specific processes contained within the region. This state of affairs, a recurrent and persistent behavior followed by an illusory and transient relief, can be likened to behavioral pathology. In Journal of Cognitive Neuroscience , 29:10 we collect contributed articles that seek to move the conversation beyond specific functions of subregions of pFC, focusing instead on general roles that support pFC involvement in a wide variety of behaviors and across a variety of experimental paradigms.

pFC is generally regarded as a region critical for abstract reasoning and high-level cognitive behaviors. As such, it has become the focus of intense research involving a wide variety of subdisciplines of neuroscience and employing a diverse range of methods. However, even as the amount of data on pFC has increased exponentially, it appears that progress toward understanding the general function of the region across a broad array of contexts has not kept pace. Effects observed in pFC are legion, and their interpretations are generally informed by a particular perspective or methodology with little regard with how those effects may apply more broadly. Consequently, the number of specific roles and functions that have been identified makes the region a very crowded place indeed and one that appears unlikely to be explained by a single general principle. In this theoretical article, we describe how the function of large portions of pFC can be accommodated by a single explanatory framework based on the computation and manipulation of error signals and how this framework may be extended to account for additional parts of pFC.

Recent work on the role of the ACC in cognition has focused on choice difficulty, action value, risk avoidance, conflict resolution, and the value of exerting control among other factors. A main underlying question is what are the output signals of ACC, and relatedly, what is their effect on downstream cognitive processes? Here we propose a model of how ACC influences cognitive processing in other brain regions that choose actions. The model builds on the earlier Predicted Response Outcome model and suggests that ACC learns to represent specifically the states in which the potential costs or risks of an action are high, on both short and long timescales. It then uses those cost signals as a basis to bias decisions to minimize losses while maximizing gains. The model simulates both proactive and reactive control signals and accounts for a variety of empirical findings regarding value-based decision-making.

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response–outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338–1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354–2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC–DLPFC interactions. We derive behavioral predictions that describe how effort and reward information is coded in PFC and how changing the configuration of such environmental information might affect decision-making and task performance involving motivation.