title: Bayesian modeling of flexible cognitive control.
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.
authors.affiliation: Center for Cognitive Neuroscience, Duke University, United States; Department of Psychology & Neuroscience, Duke University, United States.
authors.forename: Jiefeng
authors.initials: J
authors.lastname: Jiang
authors.affiliation: Center for Cognitive Neuroscience, Duke University, United States; Department of Statistical Science, Duke University, United States.
authors.forename: Katherine
authors.initials: K
authors.lastname: Heller
authors.affiliation: Center for Cognitive Neuroscience, Duke University, United States; Department of Psychology & Neuroscience, Duke University, United States. Electronic address: tobias.egner@duke.edu.
authors.forename: Tobias
authors.initials: T
authors.lastname: Egner
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D003071:Cognition / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D008606:Mental Processes / Q000502:physiology*
keywords: Bayesian models; Cognitive control; Conflict; Conflict adaptation; Congruency sequence effect; Proportion congruency effect