A Bayesian Model of 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. The present dissertation examines recent advances stemming from the application of a statistical, Bayesian learner perspective on control processes. An important limitation in current models consists of a lack of a plausible mechanism for the flexible adjustment of control over variable environments. I propose that flexible cognitive control can be achieved by a Bayesian model with a self-adapting, volatility-driven learning scheme, which modulates dynamically the relative dependence on recent (short-term) and remote (long-term) experiences in its prediction of future control demand. Using simulation data, human behavioral data and human brain imaging data, I demonstrate that this Bayesian model does not only account for several classic behavioral phenomena observed from the cognitive control literature, but also facilitates a principled, model-guided investigation of the neural substrates underlying the flexible adjustment of cognitive control. Based on the results, I conclude that the proposed Bayesian model provides a feasible solution for modeling the flexible adjustment of cognitive control.