Publications

Detailed Information

A Model-Based fMRI Analysis With Hierarchical Bayesian Parameter Estimation

Cited 0 time in Web of Science Cited 113 time in Scopus
Authors

Ahn, Woo-Young; Krawitz, Adam; Kim, Woojae; Busemeyer, Jerome R.; Brown, Joshua W.

Issue Date
2011
Publisher
Association for NeuroPsychoEconomics
Citation
Journal of Neuroscience, Psychology, and Economics, Vol.4 No.2, pp.95-110
Abstract
A recent trend in decision neuroscience is the use of model-based functional magnetic resonance imaging (fMRI) using mathematical models of cognitive processes. However, most previous model-based fMRI studies have ignored individual differences because of the challenge of obtaining reliable parameter estimates for individual participants. Meanwhile, previous cognitive science studies have demonstrated that hierarchical Bayesian analysis is useful for obtaining reliable parameter estimates in cognitive models while allowing for individual differences. Here we demonstrate the application of hierarchical Bayesian parameter estimation to model-based fMRI using the example of decision making in the Iowa Gambling Task. First, we used a simulation study to demonstrate that hierarchical Bayesian analysis outperforms conventional (individual- or group-level) maximum likelihood estimation in recovering true parameters. Then we performed model-based fMRI analyses on experimental data to examine how the fMRI results depend on the estimation method. © 2011 American Psychological Association.
ISSN
1937-321X
URI
https://hdl.handle.net/10371/203327
DOI
https://doi.org/10.1037/a0020684
Files in This Item:
There are no files associated with this item.
Appears in Collections:

Related Researcher

  • College of Social Sciences
  • Department of Psychology
Research Area Addiction, computational neuroscience, decision neuroscience, 계산 신경과학, 의사결정 신경과학, 중독

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share