ReviewBayesian modeling of flexible cognitive control
Section snippets
Cognitive control as statistical prediction
“Cognitive control” describes the ability to guide one's behavior in line with internal goals. A key characteristic of cognitive control is thought to be flexibility: control processes must be capable of dynamically adapting (both qualitatively and quantitatively) to ongoing changes in the environment. How this type of contextual regulation of control occurs (in the absence of an all-knowing homunculus) is a key question in current cognitive psychology and neuroscience research. In the present
Overview of Bayesian methods
Bayes’ theorem can be written as follows:where X, Y are random variables (e.g., sensory input, internal states, motor output, etc.). Unlike conventional variables, the value of a random variable can vary due to randomness. Thus, a random variable is often represented in the probability distribution of its possible values. This equation means that the conditional probability of Y given X could be calculated using the probabilities of X, Y, and the conditional probability of X
A Bayesian model of flexible conflict-control
Here, we propose a Bayesian model that can account for the flexibility of cognitive control over conflict in a non-stationary environment. The modeling done relies on the ability to perform statistical inference, taking the perspective that the regulation of cognitive control should be considered as a process of predicting the optimal amount of cognitive control required in a given context. To achieve this contextual flexibility, the model estimates future conflict from previous experience and,
How “Bayesian” is cognitive control?
One important avenue for future work in this context is to evaluate to what extent (or in what sense) cognitive control might actually be Bayesian in nature. According to Bowers and Davis (2012), there are three levels at which one can use Bayesian methods in modeling cognitive processes: as computational tools, for generating “optimal” benchmarks for cognitive processes, and for modeling the actual neural computations carried out by the brain. The Bayesian models reviewed in this paper, along
Conclusion
The flexibility of cognitive control enables the brain to adaptively adjust the degree of top-down biasing in a dynamically changing environment. This adjustment can be cast as a prediction of control demand, which can be optimally achieved via Bayesian belief propagation. Using Bayesian models, previous studies have successfully modeled various phenomena of cognitive control. Yet, those models usually depend on specific (and post hoc) selection of parameters to achieve optimal performance.
Acknowledgments
We thank Tim Behrens for sharing code and Chris Summerfield for discussion of the model. This work was funded in part by National Institute of Mental Health (NIMH) grants R01MH087610 (T.E.) and R01MH097965 (T.E.).
References (97)
Cognitive control, hierarchy, and the rostro-caudal organization of the frontal lobes
Trends Cogn. Sci.
(2008)- et al.
Goal-directed instrumental action: contingency and incentive learning and their cortical substrates
Neuropharmacology
(1998) - et al.
Conflict monitoring and anterior cingulate cortex: an update
Trends Cogn. Sci.
(2004) The variable nature of cognitive control: a dual mechanisms framework
Trends Cogn. Sci.
(2012)- et al.
A theory of cognitive control, aging cognition, and neuromodulation
Neurosci. Biobehav. Rev.
(2002) - et al.
A model of dual control mechanisms through anterior cingulate and prefrontal cortex interactions
Neurocomputing
(2006) - et al.
Neural signatures of economic preferences for risk and ambiguity
Neuron
(2006) - et al.
Common and distinct neural substrates of attentional control in an integrated Simon and spatial Stroop task as assessed by event-related fMRI
Neuroimage
(2004) - et al.
Interdimensional interference in the Stroop effect: uncovering the cognitive and neural anatomy of attention
Trends Cogn. Sci.
(2000) - et al.
Neural differentiation of expected reward and risk in human subcortical structures
Neuron
(2006)