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.

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.