The Value of Learning and Cognitive Control Allocation

Javier Masís, Sebastian Musslick, Jonathan D. Cohen

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

Current models of cognitive control address selection among tasks in terms of a cost-benefit tradeoff. Importantly, they usually assume a fixed level of competence for each candidate task when estimating its value. However, performing a task can improve competence through learning, which should be factored into estimates of future value. Here, we consider an extension of the Expected Value of Control (EVC) theory that includes such estimates. We demonstrate that control allocation is a function of task learnability, and show the use of this model by generating novel predictions in cognitive effort discounting tasks. We argue that the value of learning in control allocation may account for the seemingly paradoxical finding that sometimes participants prefer more difficult (i.e. costly) tasks, and discuss how the model can be leveraged to further our understanding of human decision making and cognitive impairments.

Original languageEnglish (US)
Pages348-354
Number of pages7
StatePublished - 2021
Event43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021 - Virtual, Online, Austria
Duration: Jul 26 2021Jul 29 2021

Conference

Conference43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021
Country/TerritoryAustria
CityVirtual, Online
Period7/26/217/29/21

All Science Journal Classification (ASJC) codes

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

Keywords

  • cognitive control
  • decision making
  • expected value of control theory
  • learning

Fingerprint

Dive into the research topics of 'The Value of Learning and Cognitive Control Allocation'. Together they form a unique fingerprint.

Cite this