English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Risk patterns and correlated brain activities. Multidimensional statistical analysis of FMRI data in economic decision making study

van Bömmel, A., Song, S., Majer, P., Mohr, P. N. C., Heekeren, H. R., & Härdle, W. K. (2014). Risk patterns and correlated brain activities. Multidimensional statistical analysis of FMRI data in economic decision making study. Psychometrika, 79(3), 489-514. doi:10.1007/s11336-013-9352-2.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0025-09B0-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0025-09B1-0
Genre: Journal Article

Files

show Files
hide Files
:
van Boemmel.pdf (Publisher version), 2MB
Name:
van Boemmel.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
© 2014 Springer International Publishing AG, Part of Springer Science+Business Media
License:
-

Locators

show

Creators

show
hide
 Creators:
van Bömmel, Alena1, Author              
Song, Song, Author
Majer, Pjotr, Author
Mohr, Peter N. C., Author
Heekeren, Hauke R., Author
Härdle, Wolfgang K., Author
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              

Content

show
hide
Free keywords: -
 Abstract: Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we analyze functional magnetic resonance imaging (fMRI) data on 17 subjects who were exposed to an investment decision task from Mohr, Biele, Krugel, Li, and Heekeren (in NeuroImage 49, 2556–2563, 2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park, Mammen, Wolfgang, and Borak (in Journal of the American Statistical Association 104(485), 284–298, 2009) and identify task-related activations in space and dynamics in time. With the panel DSFM (PDSFM) we can capture the dynamic behavior of the specific brain regions common for all subjects and represent the high-dimensional time-series data in easily interpretable low-dimensional dynamic factors without large loss of variability. Further, we classify the risk attitudes of all subjects based on the estimated low-dimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects’ decision behavior.

Details

show
hide
Language(s): eng - English
 Dates: 2013-07-132014-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1007/s11336-013-9352-2
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Psychometrika
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York : Springer US
Pages: - Volume / Issue: 79 (3) Sequence Number: - Start / End Page: 489 - 514 Identifier: ISBN: 0033-3123 (print) 1860-0980 (online)