Estimating the costs of cognitive control from task performance: theoretical validation and potential pitfalls

Sebastian Musslick, Jonathan D. Cohen, Amitai Shenhav

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Cognitive control is critical for accomplishing daily tasks and yet we experience it as effortful or costly. Researchers have been increasingly interested in estimating how costly cognitive control is for a given individual, to better understand underlying mechanisms and predict motivational impairments outside the lab. Here we leverage a computational model of control allocation to (a) demonstrate a procedure for estimating individual's control costs from task performance and (b) highlight the conditions under which estimated costs will be confounded with other motivational variables. We show that costs of cognitive control can be reliably estimated under perfect assumptions about other motivational variables. However, our simulation results indicate that poorly calibrated estimates of those other variables can lead to potentially drastic misestimations of subjects' control costs, compromising the validity of empirical observations. We conclude by discussing the implications of these analyses for assessing individual differences in the costs of cognitive control.

Original languageEnglish (US)
Title of host publicationProceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
PublisherThe Cognitive Science Society
Pages798-803
Number of pages6
ISBN (Electronic)9780991196784
StatePublished - 2018
Event40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018 - Madison, United States
Duration: Jul 25 2018Jul 28 2018

Publication series

NameProceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018

Conference

Conference40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018
Country/TerritoryUnited States
CityMadison
Period7/25/187/28/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • cognitive control
  • expected value of control
  • individual differences
  • mental effort

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