PhD Defense by Matthew Barrett

Event Details
  • Date/Time:
    • Friday April 16, 2021
      12:00 pm - 2:00 pm
  • Location: Atlanta, GA; REMOTE
  • Phone:
  • URL: Bluejeans
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact
No contact information submitted.
Summaries

Summary Sentence: Integrating Neuroimaging and Behavioral Data Using The Multidimensional Generalized Graded Unfolding Model

Full Summary: No summary paragraph submitted.

Name: Matthew Barrett

Dissertation Defense Meeting

Date: Friday, April 16, 2021

Time: 12:00PM

Location:  https://bluejeans.com/665088626

 

 

Advisor: 

James Roberts, Ph.D. (Georgia Tech)

 

Dissertation Committee Co-Chair:

Eric Schumacher, Ph.D. (Georgia Tech)

 

Dissertation Committee Members:

Susan Embretson, Ph.D. (Georgia Tech)

Daniel 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 stimulus was examined. Participants made attractiveness judgments to computer generated models in two phases. Phase 1 of the study was conducted in the laboratory (behavioral) while phase 2 was conducted in the fMRI scanner (neuroimaging). Data from the behavioral component was composed of attractiveness ratings for computer generated models, whereas the neuroimaging component was composed of signal change in five pre-specified ROIs when responding to the identical stimulus. It was hypothesized that both of these outcomes were a function of the distance between a subject’s ideal point and the stimulus location in a latent multidimensional preference space.  The attractiveness ratings were 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 was 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 was 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 (i.e., idealness) in the multidimensional latent space was utilized to update estimates of the individual’s location in that space and this, in turn, lead to updated predictions of signal change in each ROI.  This project was predicated on the notion that both behavioral and neural signal data are a function of the proximity between a given individual and stimulus, and was the first study to integrate models for neural signal into an item response theory framework. 

Additional Information

In Campus Calendar
No
Groups

Graduate Studies

Invited Audience
Faculty/Staff, Public, Graduate students, Undergraduate students
Categories
Other/Miscellaneous
Keywords
Phd Defense
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Apr 9, 2021 - 12:49pm
  • Last Updated: Apr 9, 2021 - 12:49pm