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Part III - Experimental and Biological Approaches

Published online by Cambridge University Press:  23 March 2020

Edited by
Aidan G. C. Wright  and
Michael N. Hallquist
Aidan G. C. Wright
Affiliation:
University of Pittsburgh
Michael N. Hallquist
Affiliation:
Pennsylvania State University
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The Cambridge Handbook of Research Methods in Clinical Psychology , pp. 91 - 190
Publisher: Cambridge University Press
Print publication year: 2020

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References

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Further Reading

For an in-depth treatment of reinforcement learning, we recommend Sutton and Barto’s recently updated classic book, Reinforcement Learning: An Introduction (2018). The Oxford Handbook of Computational and Mathematical Psychology introduces the reader to cognitive modeling and contains Gureckis and Love’s superb chapter on reinforcement learning (2015). Excellent computational neuroscience texts include Miller’s Introductory Course in Computational Neuroscience (2018) and Dayan and Abbott’s Theoretical Neuroscience (2005). Miller covers useful preliminary material, including mathematics, circuit physics and even computing and MATLAB (much of existing code for reinforcement learning modeling is written in MATLAB, but R and Python are becoming increasingly popular). Dayan and Abbot treat conditioning and reinforcement learning in greater detail. A more detailed treatment of model-based cognitive neuroscience can be found in An Introduction to Model-Based Cognitive Neuroscience (Forstmann & Wagenmakers, 2015).

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