Portfolio Decisions and Brain Reactions Via the CEAD Method
Psychometrika 2015; doi: 10.1007/s11336-015-9441-5
Posted: 7 Jun 2016
Date Written: February 11, 2015
Abstract
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 neuro-economics. 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 semi-parametric 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.
Keywords: 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
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