Rationalizing Constraints on the Capacity for Cognitive Control

Musslick, Sebastian; Cohen, Jonathan D.

doi:10.31234/osf.io/vtknh

Cited by 47 publications

(69 citation statements)

References 100 publications

(153 reference statements)

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“…2 Note that neither EVC nor MIT provide a rationale for the resource conservation principle. Some have suggested that a cost of control results from computational dilemmas in neural systems (Musslick et al, 2017;Musslick, Jang, et al, 2018;Musslick & Cohen, 2020;Zénon et al, 2019) whereas others have suggested that such costs originate from biological constraints (e.g., Holroyd, 2016). A second crucial prediction made by MIT regards situations in which task difficulty remains unknown (unspecified) or where it can be determined by participants themselves (unfixed).…”

Section: Relationship To Mitmentioning

confidence: 99%

An integrative effort : Bridging motivational intensity theory and recent neurocomputational and neuronal models of effort and control allocation

Silvestrini¹,

Musslick

Berry³

et al. 2021

Preprint

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An increasing number of cognitive, neurobiological and computational models have been proposed in the last decade, seeking to explain how humans allocate physical or cognitive effort. Most models share conceptual similarities with motivational intensity theory (MIT), an influential classic psychological theory of motivation. Yet, little effort has been made to integrate such models, which remain confined within the explanatory level for which they were developed, i.e. psychological, computational, neurobiological and neuronal. In this critical review, we derive novel analyses of three recent computational and neuronal models of effort allocation—the expected value of control (EVC) theory, the reinforcement meta-learner (RML) model, and the neuronal model of attentional effort— and establish a formal relationship between these models and MIT. Our analyses reveal striking similarities between predictions made by these models, with a shared key tenet: a non-monotonic relationship between perceived task difficulty and effort mobilization, following a saw-tooth or inverted-U shape. In addition, the models converge on the proposition that the dorsal anterior cingulate cortex (dACC) may be responsible for determining the allocation of effort and cognitive control. We conclude by discussing the distinct contributions and strengths of each theory toward understanding neurocomputational processes of effort allocation. Finally, we highlight the necessity for a unified understanding of effort allocation, by drawing novel connections between different theorizing of adaptive effort allocation as described by the presented models (cognitive, neurobiological, and neuronal levels of analysis).

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Section: Relationship To Mitmentioning

confidence: 99%

An integrative effort : Bridging motivational intensity theory and recent neurocomputational and neuronal models of effort and control allocation

Silvestrini¹,

Musslick

Berry³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The neuronal congruency effects observed in the FEF support connectionist models such as the parallel distributed processing (PDP) model 12,58,59 , which suggest that the interference from task-irrelevant information occurred automatically and was induced by prior training 29,59 .…”

Section: Discussionmentioning

confidence: 59%

Neuronal congruency effects in macaque frontal cortex

Yao

Vanduffel

2021

Preprint

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The interplay between task-relevant and task-irrelevant stimulus features induces conflicts which impair human behavioral performance in many perceptual and cognitive tasks, a.k.a. a behavioral congruency effect. The neuronal mechanisms underlying behavioral congruency effects, however, are poorly understood. We recorded single unit activity in monkey frontal cortex using a novel task-switching paradigm and discovered a neuronal congruency effect that is carried by task-relevant and -irrelevant neurons. The former neurons provide more signal, the latter less noise in congruent compared to incongruent conditions. Their relative activity levels determine the neuronal congruency effect and behavioral performance. Although these neuronal congruency signals are sensitive to selective attention, they cannot be entirely explained by selective attention as gauged by response time. We propose that such neuronal congruency effects can explain behavioral congruency effects in general, as well as previous fMRI and EEG results in various conflict paradigms.

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“…Yet, flexibility is a hallmark of cognition, with cognitive circuits shared across many different tasks (Fig. 3a) [64].…”

Section: Pre-training In Ecologically Relevant Environmentsmentioning

confidence: 99%

Next-generation of recurrent neural network models for cognition

Gr¹,

Molano-Mazón²

2021

Preprint

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Recurrent Neural Networks (RNNs) trained with machine learning techniques on cognitive tasks have become a widely accepted tool for neuroscientists. In this short opinion piece, we discuss fundamental challenges faced by early work of this approach, and recent steps to overcome such challenges and build next-generation RNN models for cognition. We propose several essential questions that practitioners of this approach should address to continue building future generations of RNN models.

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Rationalizing Constraints on the Capacity for Cognitive Control

Cited by 47 publications

References 100 publications

An integrative effort : Bridging motivational intensity theory and recent neurocomputational and neuronal models of effort and control allocation

An integrative effort : Bridging motivational intensity theory and recent neurocomputational and neuronal models of effort and control allocation

Neuronal congruency effects in macaque frontal cortex

Next-generation of recurrent neural network models for cognition

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