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