Trial-by-trial data analysis using computational models

Research output: Chapter in Book/Report/Conference proceedingChapter

165 Scopus citations

Abstract

Researchers have recently begun to integrate computational models into the analysis of neural and behavioural data, particularly in experiments on reward learning and decision making. This chapter aims to review and rationalize these methods. It exposes these tools as instances of broadly applicable statistical techniques, considers the questions they are suited to answer, provides a practical tutorial and tips for their effective use, and, finally, suggests some directions for extension or improvement. The techniques are illustrated with fits of simple models to simulated datasets. Throughout, the chapter flags interpretational and technical pitfalls of which authors, reviewers, and readers should be aware.

Original languageEnglish (US)
Title of host publicationDecision Making, Affect, and Learning
Subtitle of host publicationAttention and Performance XXIII
PublisherOxford University Press
ISBN (Electronic)9780191725623
ISBN (Print)9780199600434
DOIs
StatePublished - May 1 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Psychology(all)

Keywords

  • Computational models
  • Data analysis
  • Decision making
  • Neural data
  • Reward learning
  • Statistical methods

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  • Cite this

    Daw, N. D. (2011). Trial-by-trial data analysis using computational models. In Decision Making, Affect, and Learning: Attention and Performance XXIII Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199600434.003.0001