Translating a Reinforcement Learning Task into a Computational Psychiatry Assay: Challenges and Strategies

Peter Hitchcock, Angela Radulescu, Yael Niv, Chris R. Sims

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

1 Scopus citations

Abstract

Computational psychiatry applies advances from computational neuroscience to psychiatric disorders. A core aim is to develop tasks and modeling approaches that can advance clinical science. Special interest has centered on reinforcement learning (RL) tasks and models. However, laboratory tasks in general often have psychometric weaknesses and RL tasks pose special challenges. These challenges must be addressed if computational psychiatry is to capitalize on its promise of developing sensitive, replicable assays of cognitive function. Few resources identify these challenges and discuss strategies to mitigate them. Here, we first overview general psychometric challenges associated with laboratory tasks, as these may be unfamiliar to cognitive scientists. Next, we illustrate how these challenges interact with issues specific to RL tasks, in the context of presenting a case example of preparing an RL task for computational psychiatry. Throughout, we highlight how considering measurement issues prior to a clinical science study can inform study design.

Original languageEnglish (US)
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages2217-2222
Number of pages6
ISBN (Electronic)9780991196760
StatePublished - 2017
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: Jul 26 2017Jul 29 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period7/26/177/29/17

All Science Journal Classification (ASJC) codes

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

Keywords

  • computational modeling
  • computational psychiatry
  • measurement
  • psychometrics
  • reinforcement learning

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