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Risk Patterns and Correlated Brain Activities. Multidimensional Statistical Analysis of fMRI Data in Economic Decision Making Study

Published online by Cambridge University Press:  01 January 2025

Alena van Bömmel ,
Song Song ,
Piotr Majer ,
Peter N. C. Mohr ,
Hauke R. Heekeren  and
Wolfgang K. Härdle
Alena van Bömmel
Affiliation:
Max Planck Institute for Molecular Genetics
Song Song
Affiliation:
C.A.S.E. Center for Applied Statistics and Economics, Humboldt-Universität zu Berlin
Piotr Majer*
Affiliation:
C.A.S.E. Center for Applied Statistics and Economics, Humboldt-Universität zu Berlin
Peter N. C. Mohr
Affiliation:
Department of Education and Psychology, Freie Universität Berlin
Hauke R. Heekeren
Affiliation:
Department of Education and Psychology, Freie Universität Berlin
Wolfgang K. Härdle
Affiliation:
School of Business, Singapore Management University
*
Requests for reprints should be sent to Piotr Majer, C.A.S.E. Center for Applied Statistics and Economics, Humboldt-Universität zu Berlin, Berlin, Germany. E-mail: [email protected]
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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.

Keywords

riskrisk attitudefMRIdecision makingsemiparametric modelfactor structureSVM
Type
Original Paper
Information
Psychometrika , Volume 79 , Issue 3 , July 2014 , pp. 489 - 514
Copyright
Copyright © 2013 The Psychometric Society

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