Chapter

Trial-by-trial data analysis using computational models

Nathaniel D. Daw

in Decision Making, Affect, and Learning

Published in print March 2011 | ISBN: 9780199600434
Published online May 2011 | e-ISBN: 9780191725623 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199600434.003.0001

Series: Attention and Performance

Trial-by-trial data analysis using computational models

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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

Chapter.  17560 words.  Illustrated.

Subjects: Cognitive Psychology

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