Monthly
208 pp. per issue
8 1/2 x 11, illustrated
ISSN
0898-929X
E-ISSN
1530-8898
2014 Impact factor:
4.69

Journal of Cognitive Neuroscience

October 2017, Vol. 29, No. 10, Pages 1633-1645
(doi: 10.1162/jocn_a_01160)
Predicting Motivation: Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease
Article PDF (600.86 KB)
Abstract

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.