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Bayesian modeling of flexible cognitive control
Journal article   Peer reviewed

Bayesian modeling of flexible cognitive control

Jiefeng Jiang, Katherine Heller and Tobias Egner
Neuroscience and biobehavioral reviews, Vol.46 Pt 1(1), pp.30-43
10/2014
DOI: 10.1016/j.neubiorev.2014.06.001
PMCID: PMC4253563
PMID: 24929218
url
https://www.ncbi.nlm.nih.gov/pmc/articles/4253563View
Open Access

Abstract

"Cognitive control" describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. We examine recent advances stemming from the application of a Bayesian learner perspective that provides optimal prediction for control processes. In reviewing the application of Bayesian models to cognitive control, we note that an important limitation in current models is a lack of a plausible mechanism for the flexible adjustment of control over conflict levels changing at varying temporal scales. We then show that flexible cognitive control can be achieved by a Bayesian model with a volatility-driven learning mechanism that modulates dynamically the relative dependence on recent and remote experiences in its prediction of future control demand. We conclude that the emergent Bayesian perspective on computational mechanisms of cognitive control holds considerable promise, especially if future studies can identify neural substrates of the variables encoded by these models, and determine the nature (Bayesian or otherwise) of their neural implementation.
Learning - physiology Mental Processes - physiology Humans Bayes Theorem Brain Mapping Cognition - physiology Brain - physiology

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