Name: Matthew Barrett

Dissertation Proposal Defense Meeting

Date: Tuesday, August 14th, 2018

Time: 1:00PM

Location: J.S. Coon bldg. room 148

Advisor: 

James Roberts, Ph.D. (Georgia Tech)

Co-Chair

Eric Schumacher, Ph.D. (Georgia Tech)

Dissertation Committee Members:

Susan Embretson, Ph.D. (Georgia Tech)

Dan Spieler, Ph.D. (Georgia Tech)

Jessica Turner, Ph.D. (Georgia State University)

Title:  Integrating Neuroimaging and Behavioral Data Using The Multidimensional Generalized Graded Unfolding Model.

Summary: A study investigating the relationship between two distinct data structures resulting from the same response process is proposed. Participants will make attractiveness judgments to computer generated models in two phases. Phase 1 of the study is conducted in the laboratory (behavioral) while phase 2 is conducted in the fMRI scanner (neuroimaging). Data from the behavioral component will be composed of attractiveness ratings for computer generated models, whereas the neuroimaging component will be composed of signal change in pre-specified ROIs when responding to the identical stimulus. The dissertation hypothesizes that both of these outcomes are a function of the distance between a subject’s ideal point and the stimulus location in a latent multidimensional preference space.  The attractiveness ratings will be modeled with the multidimensional generalized graded unfolding model (MGGUM), which is an item response theory model for proximity-based data presumed to underlie the general preference ratings.  The signal change data will be simultaneously modeled as a function of the estimated distance between a subject and stimulus derived from the MGGUM.  Estimation of models for both types of data will be conducted simultaneously using a system of two simultaneous equations with parameters that are updated using a Markov chain Monte Carlo procedure.  Information about signal change and its relationship to person-stimulus distances in the multidimensional latent space will be utilized to update estimates of the individual’s location in that space and this will, in turn, lead to updated predictions of signal change in an ROI.  This project is predicated on the notion that both behavioral and neural signal data are a function of the proximity between a given individual and stimulus, and will be the first study to integrate models for neural signal into an item response theory framework.