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Majer, Piotr
Mohr, Peter N. C.
Heekeren, Hauke R.
Härdle, Wolfgang Karl
Year of Publication: 
Series/Report no.: 
SFB 649 Discussion Paper No. 2014-036
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 (ID) study for ID-related effects. We propose a new technique for identifying activated brain regions: Cluster, Estimation, Activation and Decision (CEAD) 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 NCUT spectral clustering. The information within each cluster can then be extracted by the flexible 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 BOLD 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 (aINS) and DMPFC. 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 attitude
decision making
semiparametric model
factor structure
brain imaging
spatial clustering
inference on clusters
CEAD method
Document Type: 
Working Paper

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