Computational Models of Cognitive Control

Verguts, Tom

doi:10.1002/9781118920497.ch8

Cited by 12 publications

(11 citation statements)

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“…The assumption of linearity could also be tested by applying LCT to existing computational models of cognitive control (Brown, 2017;Verguts, 2017b), in particular, to classic neural network architectures that simulate task performance with separate groups of units for control and task processing . The weight matrices of these models, which parse naturally into connectivity matrices for the control and controlled systems, can be used to solve the controllability Gramian (see the main text).…”

Section: Summary and Discussionmentioning

confidence: 99%

The Controllosphere: The Neural Origin of Cognitive Effort

Holroyd¹

2023

Preprint

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Why do some mental activities feel harder than others? The answer to this question is surprisingly controversial. Current theories propose that cognitive effort affords a computational benefit, such as instigating a switch from an activity with low reward value to a different activity with higher reward value. By contrast, in this article I relate cognitive effort to the fact that brain neuroanatomy and neurophysiology render some neural states more energy-efficient than others. I introduce the concept of the “controllosphere,” an energy-inefficient region of neural state space associated with high control, which surrounds the better-known “intrinsic manifold”, an energy-efficient subspace associated with low control. Integration of control-theoretic principles with classic neurocomputational models of cognitive control suggests that dorsolateral prefrontal cortex (DLPFC) implements a controller that can drive the system state into the controllosphere, anterior cingulate cortex (ACC) implements an observer that monitors changes of state of the controlled system, and cognitive effort reflects a mismatch between DLPFC and ACC energies for control and observation. On this account, cognitive effort scales with the energetic demands of the DLPFC control signal, especially when the consequences of the control are unobservable by ACC. Further, I propose that neural transitions through the controllosphere lead to a buildup of neural waste. Cognitive effort therefore prevents against neural damage by discouraging extended periods of high control.

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Section: Summary and Discussionmentioning

confidence: 99%

The Controllosphere: The Neural Origin of Cognitive Effort

Holroyd¹

2023

Preprint

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“…Modeling ACC function faces the challenge of accounting for a multitude of empirical findings within a single coherent framework. Although progress has been made over the last two decades in explaining a wider array of empirical findings associated with different experimental techniques (Verguts, 2017b ), the debate about underlying computational principles remains lively. We see several outstanding conceptual issues that remain to be resolved, many of which lie at the crossroads between predictive mechanisms and effortful control.…”

Section: Discussionmentioning

confidence: 99%

Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort

2017

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In the last two decades the anterior cingulate cortex (ACC) has become one of the most investigated areas of the brain. Extensive neuroimaging evidence suggests countless functions for this region, ranging from conflict and error coding, to social cognition, pain and effortful control. In response to this burgeoning amount of data, a proliferation of computational models has tried to characterize the neurocognitive architecture of ACC. Early seminal models provided a computational explanation for a relatively circumscribed set of empirical findings, mainly accounting for EEG and fMRI evidence. More recent models have focused on ACC's contribution to effortful control. In parallel to these developments, several proposals attempted to explain within a single computational framework a wider variety of empirical findings that span different cognitive processes and experimental modalities. Here we critically evaluate these modeling attempts, highlighting the continued need to reconcile the array of disparate ACC observations within a coherent, unifying framework.

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“…The insight that cognitive control is closely related to motivation is formalized in a growing number of computational models of cognitive control. At present there are multiple theoretical and computational models which deal with the problem of how cognitive control is allocated (for recent reviews see: Verguts, 2017). These models specify a set of components needed for optimal decision-making about control allocation and they are not mutually exclusive.…”

Section: Computational Models Of Cognitive Controlmentioning

confidence: 99%