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Decision Making, Affect, and LearningAttention and Performance XXIII$
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Mauricio R. Delgado, Elizabeth A. Phelps, and Trevor W. Robbins

Print publication date: 2011

Print ISBN-13: 9780199600434

Published to Oxford Scholarship Online: May 2011

DOI: 10.1093/acprof:oso/9780199600434.001.0001

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Trial-by-trial data analysis using computational models

Trial-by-trial data analysis using computational models

(Tutorial Review)

Chapter:
(p.3) Chapter 1 Trial-by-trial data analysis using computational models
Source:
Decision Making, Affect, and Learning
Author(s):

Nathaniel D. Daw

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199600434.003.0001

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.

Keywords:   computational models, statistical methods, data analysis, reward learning, decision making, neural data

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