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Extending Models of Decision Making: Neural Systems and Patient Predictions

James F. Cavanagh, Ph.D. - Assistant Professor in the Department of Psychology at the University of New Mexico

Presented: November 13, 2014

ABSTRACT: The ubiquitous Drift Diffusion Model (DDM) has been used for decades to understand latent features of decision making.  This model has been applied to the fields of memory recall, perception, learning, and neuroeconomics, and a sophisticated understanding of the neural systems underlying this process has begun to emerge.  In this talk I will discuss how hierarchical Bayesian parameter estimation (HDDM) has been used to understand the neural network responsible for raising the decision threshold, a latent feature of the DDM that accounts for the speed-accuracy tradeoff.   Evidence will be presented from intracranial human recordings, scalp EEG, manipulation of deep brain stimulation, combined EEG-fMRI, and pupillometry to support the hypothesis that a mid-frontal – subthalamic circuit underlies this adaptive ability to proceed with enhanced caution.  Ongoing studies and the predictive utility of latent DDM parameters for classifying patient groups will be discussed.

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