Trial-by-trial data analysis using computational models

Research output: Chapter in Book/Report/Conference proceedingChapter

302 Scopus citations


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
StatePublished - May 1 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Psychology


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


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