Literature DB >> 29750987

The opportunity cost of time modulates cognitive effort.

A Ross Otto1, Nathaniel D Daw2.   

Abstract

A spate of recent work demonstrates that humans seek to avoid the expenditure of cognitive effort, much like physical effort or economic resources. Less is clear, however, about the circumstances dictating how and when people decide to expend cognitive effort. Here we adopt a popular theory of opportunity costs and response vigor and to elucidate this question. This account, grounded in Reinforcement Learning, formalizes a trade-off between two costs: the harder work assumed necessary to emit faster actions and the opportunity cost inherent in acting more slowly (i.e., the delay that results to the next reward and subsequent rewards). Recent work reveals that the opportunity cost of time-operationalized as the average reward rate per unit time, theorized to be signaled by tonic dopamine levels, modulates the speed with which a person responds in a simple discrimination tasks. We extend this framework to cognitive effort in a diverse range of cognitive tasks, for which 1) the amount of cognitive effort demanded from the task varies from trial to trial and 2) the putative expenditure of cognitive effort holds measureable consequences in terms of accuracy and response time. In the domains of cognitive control, perceptual decision-making, and task-switching, we found that subjects tuned their level of effort exertion in accordance with the experienced average reward rate: when the opportunity cost of time was high, subjects made more errors and responded more quickly, which we interpret as a withdrawal of cognitive effort. That is, expenditure of cognitive effort appeared to be modulated by the opportunity cost of time. Further, and consistent with our account, the strength of this modulation was predicted by individual differences in efficacy of cognitive control. Taken together, our results elucidate the circumstances dictating how and when people expend cognitive effort.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cognitive control; Cognitive effort; Decision-making

Mesh:

Year:  2018        PMID: 29750987     DOI: 10.1016/j.neuropsychologia.2018.05.006

Source DB:  PubMed          Journal:  Neuropsychologia        ISSN: 0028-3932            Impact factor:   3.139


  22 in total

1.  Task-evoked pupillary responses track effort exertion: Evidence from task-switching.

Authors:  Kevin da Silva Castanheira; Sophia LoParco; A Ross Otto
Journal:  Cogn Affect Behav Neurosci       Date:  2020-10-20       Impact factor: 3.282

Review 2.  Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making.

Authors:  R Frömer; A Shenhav
Journal:  Neurosci Biobehav Rev       Date:  2021-12-10       Impact factor: 8.989

3.  Distinguishing cognitive effort and working memory load using scale-invariance and alpha suppression in EEG.

Authors:  Omid Kardan; Kirsten C S Adam; Irida Mance; Nathan W Churchill; Edward K Vogel; Marc G Berman
Journal:  Neuroimage       Date:  2020-02-14       Impact factor: 6.556

4.  Methylphenidate boosts choices of mental labor over leisure depending on striatal dopamine synthesis capacity.

Authors:  Lieke Hofmans; Danae Papadopetraki; Ruben van den Bosch; Jessica I Määttä; Monja I Froböse; Bram B Zandbelt; Andrew Westbrook; Robbert-Jan Verkes; Roshan Cools
Journal:  Neuropsychopharmacology       Date:  2020-09-12       Impact factor: 7.853

5.  Cognitive Control as a Multivariate Optimization Problem.

Authors:  Harrison Ritz; Xiamin Leng; Amitai Shenhav
Journal:  J Cogn Neurosci       Date:  2022-03-05       Impact factor: 3.225

6.  Context-sensitive valuation and learning.

Authors:  Lindsay E Hunter; Nathaniel D Daw
Journal:  Curr Opin Behav Sci       Date:  2021-06-09

Review 7.  Search for solutions, learning, simulation, and choice processes in suicidal behavior.

Authors:  Alexandre Y Dombrovski; Michael N Hallquist
Journal:  Wiley Interdiscip Rev Cogn Sci       Date:  2021-05-18

8.  Integrating Reward Information for Prospective Behavior.

Authors:  Sam Hall-McMaster; Mark G Stokes; Nicholas E Myers
Journal:  J Neurosci       Date:  2022-01-18       Impact factor: 6.709

9.  The computational cost of active information sampling before decision-making under uncertainty.

Authors:  Pierre Petitet; Bahaaeddin Attaallah; Sanjay G Manohar; Masud Husain
Journal:  Nat Hum Behav       Date:  2021-05-27

Review 10.  A mosaic of cost-benefit control over cortico-striatal circuitry.

Authors:  Andrew Westbrook; Michael J Frank; Roshan Cools
Journal:  Trends Cogn Sci       Date:  2021-06-10       Impact factor: 24.482

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