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Predicting motivation : computational models of PFC can explain neural coding of motivation and effort-based decision-making in health and disease

Eliana Vassena UGent, James Deraeve UGent and William H. Alexander (2017) JOURNAL OF COGNITIVE NEUROSCIENCE. 29(10). p.1633-1645
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.
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author
organization
year
type
journalArticle (original)
publication status
published
keyword
ANTERIOR CINGULATE CORTEX, MEDIAL FRONTAL-CORTEX, PREFRONTAL CORTEX, COGNITIVE CONTROL, NUCLEUS-ACCUMBENS, EFFORT ALLOCATION, REWARD-PROSPECT, INDIVIDUAL-DIFFERENCES, DEPRESSIVE SYMPTOMS, RESPONSE-INHIBITION
journal title
JOURNAL OF COGNITIVE NEUROSCIENCE
J. Cogn. Neurosci.
volume
29
issue
10
pages
13 pages
publisher
Mit Press
place of publication
Cambridge
Web of Science type
Article
Web of Science id
000408651200001
ISSN
0898-929X
1530-8898
DOI
10.1162/jocn_a_01160
language
English
UGent publication?
yes
classification
A1
id
8552706
handle
http://hdl.handle.net/1854/LU-8552706
date created
2018-03-01 15:06:57
date last changed
2018-05-15 12:59:45
@article{8552706,
  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.},
  author       = {Vassena, Eliana and Deraeve, James and Alexander, William H.},
  issn         = {0898-929X},
  journal      = {JOURNAL OF COGNITIVE NEUROSCIENCE},
  keyword      = {ANTERIOR CINGULATE CORTEX,MEDIAL FRONTAL-CORTEX,PREFRONTAL CORTEX,COGNITIVE CONTROL,NUCLEUS-ACCUMBENS,EFFORT ALLOCATION,REWARD-PROSPECT,INDIVIDUAL-DIFFERENCES,DEPRESSIVE SYMPTOMS,RESPONSE-INHIBITION},
  language     = {eng},
  number       = {10},
  pages        = {1633--1645},
  publisher    = {Mit Press},
  title        = {Predicting motivation : computational models of PFC can explain neural coding of motivation and effort-based decision-making in health and disease},
  url          = {http://dx.doi.org/10.1162/jocn\_a\_01160},
  volume       = {29},
  year         = {2017},
}

Chicago
Vassena, Eliana, James Deraeve, and William H. Alexander. 2017. “Predicting Motivation : Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease.” Journal of Cognitive Neuroscience 29 (10): 1633–1645.
APA
Vassena, E., Deraeve, J., & Alexander, W. H. (2017). Predicting motivation : computational models of PFC can explain neural coding of motivation and effort-based decision-making in health and disease. JOURNAL OF COGNITIVE NEUROSCIENCE, 29(10), 1633–1645.
Vancouver
1.
Vassena E, Deraeve J, Alexander WH. Predicting motivation : computational models of PFC can explain neural coding of motivation and effort-based decision-making in health and disease. JOURNAL OF COGNITIVE NEUROSCIENCE. Cambridge: Mit Press; 2017;29(10):1633–45.
MLA
Vassena, Eliana, James Deraeve, and William H. Alexander. “Predicting Motivation : Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease.” JOURNAL OF COGNITIVE NEUROSCIENCE 29.10 (2017): 1633–1645. Print.