Bayesian modeling of flexible cognitive control.


Journal Article (Review)

"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.

Full Text

Duke Authors

Cited Authors

  • Jiang, J; Heller, K; Egner, T

Published Date

  • October 2014

Published In

Volume / Issue

  • 46 Pt 1 /

Start / End Page

  • 30 - 43

PubMed ID

  • 24929218

Pubmed Central ID

  • 24929218

Electronic International Standard Serial Number (EISSN)

  • 1873-7528

International Standard Serial Number (ISSN)

  • 0149-7634

Digital Object Identifier (DOI)

  • 10.1016/j.neubiorev.2014.06.001


  • eng