||<p>"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.</p>