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Portfolio Decisions and Brain Reactions via the CEAD method

Published online by Cambridge University Press:  01 January 2025

Piotr Majer ,
Peter N. C. Mohr ,
Hauke R. Heekeren  and
Wolfgang K. Härdle
Piotr Majer*
Affiliation:
Humboldt-Universität zu Berlin
Peter N. C. Mohr
Affiliation:
Freie Universität Berlin
Hauke R. Heekeren
Affiliation:
Freie Universität Berlin
Wolfgang K. Härdle
Affiliation:
Humboldt-Universität zu Berlin Singapore Management University
*
Correspondence should bemade to Piotr Majer, C.A.S.E. – Center for Applied Statistics and Economics, Humboldt-Universität zu Berlin, Spandauer Str. 1, 10178 Berlin, Germany. Email: [email protected]
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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 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.

Keywords

riskrisk attitudefMRIdecision makingneuroeconomicssemiparametric modelfactor structurebrain imagingspatial clusteringinference on clustersCEAD method

JEL classification

C3: Multiple or Simultaneous Equation Models • Multiple Variables C6: Mathematical Methods • Programming Models • Mathematical and Simulation Modeling D8: Information, Knowledge, and Uncertainty C9: Design of Experiments C14: Semiparametric and Nonparametric Methods: General
Type
Original Paper
Information
Psychometrika , Volume 81 , Issue 3 , September 2016 , pp. 881 - 903
Copyright
Copyright © 2015 The Psychometric Society

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References

Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003). General multilevel linear modeling for group analysis in FMRI. NeuroImage, 20, 21052–1063.CrossRefGoogle Scholar
Beckmann, C. F., & Smith, S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Transactions on Medical Imaging, 23, 2137–152.CrossRefGoogle ScholarPubMed
Beckmann, C. F., & Smith, S. M. (2005). Tensorial extensions of independent component analysis for multisubject FMRI analysis. NeuroImage, 25, 1294–311.CrossRefGoogle ScholarPubMed
Bernoulli, D. (1738). Specimen Theoriae Novae de Mensura Sortis. Papers of the Imperial Academy of Sciences in Petersburg, 5, 172–192.Google Scholar
Brown, D. A., Lazar, N. A., Datta, G. S., Jang, W., & McDowell, J. E. (2014). Incorporating spatial dependence into Bayesian multiple testing of statistical parametric maps in functional neuroimaging. NeuroImage, 84, 197–112.CrossRefGoogle ScholarPubMed
Camerer, C. F. (2007). Neuroeconomics: Using neuroscience to make economic predictions. The Economic Journal, 117, 519C26–C42.CrossRefGoogle Scholar
Camerer, C. F. (2013). Goals, methods, and progress in neuroeconomics. Annual Review of Economics, 5, 1425–455.CrossRefGoogle Scholar
Caraco, T. (1981). Energy budgets, risk and foraging preferences in dark-eyed juncos (Junco hyemalis). Behavioral Ecology and Sociobiology, 8, 3213–217.CrossRefGoogle Scholar
Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33, 81914–1928.CrossRefGoogle ScholarPubMed
Desikan, R. S., Segonne, F., Fischl, B., Quinn, B. R., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, S. A., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31, 3968–980.CrossRefGoogle ScholarPubMed
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J-P, Frith, C. D., & Frackowiak, R. S. J. (1994). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2, 4189–210.CrossRefGoogle Scholar
Garrett, D. D., Samanez-Larkin, G. R., MacDonald, S. W., Lindenberger, U., McIntosh, A. R., & Grady, C. L. (2013). Moment-to-moment brain signal variability: A next frontier in human brain mapping. Neuroscience and Biobehavioral Reviews, 37, 4610–624.CrossRefGoogle ScholarPubMed
Glimcher, P. W., & Fehr, E. (2013). Neuroeconomics: Decision making and the brain, 2London: Academic Press ISBN: 9780124160088Google Scholar
Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008). The neural systems that mediate human perceptual decision making. Nature Reviews Neuroscience, 9, 6467–479.CrossRefGoogle ScholarPubMed
Heller, R., Stanley, D., Yekutieli, D., Rubin, N., & Benjamini, Y. (2006). Cluster-based analysis of FMRI data. NeuroImage, 33, 2599–608.CrossRefGoogle ScholarPubMed
Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature Reviews Neuroscience, 10, 1625–1633.CrossRefGoogle ScholarPubMed
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decisison under risk. Econometrica, 47, 2263–292.CrossRefGoogle Scholar
Kamvar, S. D., Klein, D., & Manning, C. D. (2003). Spectral Learning. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI’03 (pp. 561–566). San Francisco: Morgan Kaufmann Publishers Inc. ISBN: 9780127056616.Google Scholar
Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453, 7197869–878.CrossRefGoogle Scholar
Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17, 4395–416.CrossRefGoogle Scholar
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7, 177–91.Google Scholar
Mohr, P., Biele, G., & Heekeren, H. (2010). Neural processing of risk. The Journal of Neuroscience, 30, 196613–6619.CrossRefGoogle ScholarPubMed
Mohr, P. N. C., Biele, G., Krugel, L. K., Li, S-C, & Heekeren, H. R. (2010). Neural foundations of risk-return trade-off in investment decisions. NeuroImage, 49, 32556–2563.CrossRefGoogle ScholarPubMed
Mohr, P. N. C., & Nagel, I. E. (2010). Variability in brain activity as an individual difference measure in neuroscience?. The Journal of Neuroscience, 30, 237755–7757.CrossRefGoogle Scholar
Park, B. U., Mammen, E., Härdle, W. K., & Borak, S. (2009). Time series modelling with semiparametric factor dynamics. Journal of the American Statistical Association, 104, 485284–298.CrossRefGoogle Scholar
Ruff, C. C., & Huettel, S. A. (2013). Chapter Experimental methods in cognitive neuroscience. Neuroeconomics: Decision making and the brain (2nd ed.). London: Academic Press. ISBN: 9780124160088.Google Scholar
Shen, X., Papademetris, X., & Constable, R. T. (2010). Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data. NeuroImage, 50, 31027–1035.CrossRefGoogle ScholarPubMed
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 8888–905.Google Scholar
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., & Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106, 3113040–13045.CrossRefGoogle ScholarPubMed
Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain: 3-D proportional system: An approach to cerebral imaging (Thieme Classics), Stuttgart: ThiemeGoogle Scholar
van Bömmel, A., Song, S., Majer, P., Mohr, P. N. C., Heekeren, H. R., & Härdle, W. K. (2013). Risk patterns and correlated brain activities. Multidimensional statistical analysis of fMRI data in economic decision making study. Psychometrika. doi:10.1007/s11336-013-9352-2.CrossRefGoogle Scholar
van den Heuvel, M., & Mandl, R. (2008). Normalized cut group clustering of resting-state fMRI data. PLoS ONE, 3, 4e2001CrossRefGoogle ScholarPubMed
von Neumann, J., & Morgenstern, O. (1953). Theory of games and economic behavior, Princeton: Princeton University PressGoogle Scholar
Weber, E. U., & Milliman, R. A. (1997). Perceived risk attitudes: Relating risk perception to risky choice. Management Science, 43, 2123–144.CrossRefGoogle Scholar
Worsley, K. J., Liao, C. H., Aston, J., Petre, V., Duncan, G. H., Morales, F., & Evans, A. C. (2002). A general statistical analysis for fMRI data. NeuroImage, 15, 11–15.CrossRefGoogle Scholar
Xu, Q., desJardins, M., & Wagstaff, K. (2005). Constrained spectral clustering under a local proximity structure assumption. In: Proceedings of the 18th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (pp. 866–867). Palo Alto: AAAI Press. ISBN: 9781577352341.Google Scholar