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1 results

  • 14 - Clinical Computational Neuroscience

  • from Part III - Experimental and Biological Approaches
  • Edited by Aidan G. C. Wright, University of Pittsburgh, Michael N. Hallquist, Pennsylvania State University
  • Book:
    The Cambridge Handbook of Research Methods in Clinical Psychology
    Published online:
    23 March 2020
    Print publication:
    02 April 2020, pp 168-190

  • 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.