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14 - Clinical Computational Neuroscience

from Part III - Experimental and Biological Approaches

Published online by Cambridge University Press:  23 March 2020

By
Michael N. Hallquist  and
Alexandre Y. Dombrovski
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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Summary

This chapter discusses mathematical models of learning in neural circuits with a focus on reinforcement learning. Formal models of learning provide insights into how we adapt to a complex, changing environment, and how this adaptation may break down in psychopathology. Computational clinical neuroscience is motivated to use mathematical models of decision processes to bridge between brain and behavior, with a particular focus on understanding individual differences in decision making. The chapter reviews the basics of model specification, model inversion (parameter estimation), and model-based approaches to understanding individual differences in health and disease. It illustrates how models can be specified based on theory and empirical observations, how they can be fitted to human behavior, and how model-predicted signals from neural recordings can be decoded. A functional MRI (fMRI) study of social cooperation is used to illustrate the application of reinforcement learning (RL) to test hypotheses about neural underpinnings of human social behavior.

Keywords

reinforcement learningcomputational psychiatrymodel-based cognitive neurosciencecognitive modeling
Type
Chapter
Information
The Cambridge Handbook of Research Methods in Clinical Psychology , pp. 168 - 190
Publisher: Cambridge University Press
Print publication year: 2020

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References

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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