, Volume 81, Issue 3, pp 881–903 | Cite as

Portfolio Decisions and Brain Reactions via the CEAD method

  • Piotr Majer
  • Peter N. C. Mohr
  • Hauke R. Heekeren
  • Wolfgang K. Härdle


Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: cluster, estimation, activation, and decision method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal-to-noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible dynamic semiparametric factor model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation, and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.


risk risk attitude fMRI decision making  neuroeconomics semiparametric model factor structure  brain imaging spatial clustering inference on clusters CEAD method 

JEL Classification

C3 C6 C9 C14 D8 



The authors greatfully acknowledge financial support from the Deutsche Forschungsgemeinschaft through SFB 649 “Economic Risk” and IRTG 1792 “High Dimensional Non Stationary Time Series”.


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Copyright information

© The Psychometric Society 2015

Authors and Affiliations

  • Piotr Majer
    • 1
  • Peter N. C. Mohr
    • 2
  • Hauke R. Heekeren
    • 2
  • Wolfgang K. Härdle
    • 1
    • 3
  1. 1.C.A.S.E. - Center for Applied Statistics and EconomicsHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Department of Education and PsychologyFreie Universität BerlinBerlinGermany
  3. 3.School of Business Singapore Management UniversitySingaporeSingapore

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