Russell Poldrack
(University of Texas, Austin)
Model-Based fMRI Analysis: Beyond Reinforcement Learning
Date: 08/15/2012
Description:
The use of behavioral models to derive predictions for fMRI analysis has been particularly successful in the context of reinforcement learning models, but has been used relatively infrequently with other kinds of models. I will discuss three approaches that have used cognitive models to drive fMRI analyses. The first uses drift diffusion models of response times to understand the role of specific brain regions in simple decision processes. The second uses diffusion models to understand individual differences in executive function on the stop signal task. The third uses a model of dynamic decision making to better understand risk taking on the Balloon Analog Risk Task. Together, these studies illustrate new approaches to the integration of mathematical cognitive models into fMRI analysis.
Russell Poldrack
(University of Texas, Austin)
Habits and Behavioral Change: Where Learning, Decision Making, and Executive Function Meet
Date: 08/13/2012
Description:
Habits are essential to optimal behavior, but are also the source of many public health problems, such as drug addiction and obesity. I will discuss our research that has examined how habits are acquired, which has focused on the role of corticostriatal systems. I will then discuss ongoing research in our lab that is examining the nature of behavioral change in the context of value-based decision making. Together this work provides a framework for understanding how habitual behavior develops and changes over time.