Abstract
Rapid progress has been made during the past decade in our understanding of neural circuits and neuromodulatory systems that mediate economic behavior. This research has produced a set of experimental tools that have been successfully applied to a variety of neuropsychiatric and focal lesion cohorts. Despite these advances, however, major gaps still exist between this scientific understanding and future clinical applications. In particular, little systematic work has been done to map these behavioral and neural measures to clinically relevant characteristics, in ways that can (1) organize clinical descriptions of decision-making deficits or (2) refine and quantify these descriptions. Medical charts constitute a rich source of primary data on behavioral symptoms, and have been largely untapped in translational research. Here we discuss and provide an example of how to connect scientific insights of neural basis of decision-making to clinical data. We conclude by discussing the scientific and ethical challenges to a more full integration of these sources of experimental and clinical data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agarwal, S., Driscoll, J., et al. (2008). The age of reason: Financial decisions over the lifecycle. American Economic Association Annual Meeting.
Bakalar, N. (2013). Sharing psychiatric records helps care. New York Times.
Bechara, A., Damasio, H., et al. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275(5304), 1293–1295.
Bechara, A., Tranel, D., et al. (2000). Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain, 123(11), 2189–2202.
Bucks, B. K., Kennickell, A. B., et al. (2009). Changes in U.S. family finances from 2004 to 2007: Evidence from the survey of consumer finances. D. o. R. a. Statistics. Washington: Board of Governors of the Federal Reserve System.
Camerer, C. F., & Weber, M. (1992). Recent developments in modeling preferences—uncertainty and ambiguity. Journal of Risk and Uncertainty, 5(4), 325–370.
Chiong, W., Hsu, M., et al. Financial errors in dementia: Testing a neuroeconomic conceptual framework. NeuroCase (in press).
Cummings, J. L., Mega, M., et al. (1994). The neuropsychiatric inventory comprehensive assessment of psychopathology in dementia. Neurology, 44(12), 2308.
Denburg, N., Cole, C., et al. (2007). The orbitofrontal cortex, real-world decision making, and normal aging. Annals of the New York Academy of Sciences, 1121(1), 480–498.
Fehr, E., & Camerer, C. F. (2007). Social neuroeconomics: the neural circuitry of social preferences. Trends in Cognitive Sciences, 11(10), 419–427.
First, M. B., & Gibbon, M. (1997). User’s guide for the structured clinical interview for DSM-IV axis I disorders SCID-I: Clinician version. Amer Psychiatric Pub Incorporated.
Frank, M. J., Seeberger, L. C., et al. (2004). By carrot or by stick: Cognitive reinforcement learning in parkinsonism. Science, 306(5703), 1940–1943.
Fudenberg, D., & Levine, D. K. (1998). The theory of learning in games. Cambridge: MIT press.
Glimcher, P. (2002). Decisions, decisions, decisions: Choosing a biological science of choice. Neuron, 36(2), 323–332.
Himmelstein, D. U., Wright, A., et al. (2010). Hospital computing and the costs and quality of care: A national study. The American Journal of Medicine, 123(1), 40–46.
Hofbauer, J., & Sigmund, K. (1998). Evolutionary games and population dynamics. Cambridge: Cambridge Univ Press.
Hsu, M., Bhatt, M., et al. (2005). Neural systems responding to degrees of uncertainty in human decision-making. Science, 310(5754), 1680–1683.
Hsu, M., Krajbich, I., et al. (2009). Neural response to reward anticipation under risk is nonlinear in probabilities. The Journal of Neuroscience, 29(7), 2231–2237.
Insel, T. R., & Fernald, R. D. (2004). How the brain processes social information: Searching for the social brain. Annual Review of Neuroscience, 27, 697–722.
Jaret, P. (2013). Mining electronic records for revealing health data. New York Times: D1.
Jensen, P. B., Jensen, L. J., et al. (2012). Mining electronic health records: Towards better research applications and clinical care. Nature Reviews Genetics.
Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature Neuroscience, 10(12), 1625–1633.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 263–291.
King-Casas, B., Sharp, C., et al. (2008). The rupture and repair of cooperation in borderline personality disorder. Science, 321(5890), 806.
King-Casas, B., Tomlin, D., et al. (2005). Getting to know you: Reputation and trust in a two-person economic exchange. Science, 308(5718), 78–83.
Knutson, B., & Greer, S. M. (2008). Anticipatory affect: Neural correlates and consequences for choice. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1511), 3771–3786.
Kuhnen, C., & Knutson, B. (2005). The neural basis of financial risk taking. Neuron, 47(5), 763–770.
Levy, M. L., Miller, B. L., et al. (1996). Alzheimer disease and frontotemporal dementias: Behavioral distinctions. Archives of Neurology, 53(7), 687.
Loewenstein, G. F., Weber, E. U., et al. (2001). Risk as feelings. Psychological Bulletin, 127(2), 267.
Maia, T. V., & Frank, M. J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience, 14(2), 154.
Marson, D. C., Sawrie, S. M., et al. (2000). Assessing financial capacity in patients with Alzheimer disease: A conceptual model and prototype instrument. Archives of Neurology, 57(6), 877.
McCabe, K., Houser, D., et al. (2001). A functional imaging study of cooperation in two-person reciprocal exchange. PNAS, 98(20), 11832–11835.
Miller, L. A. (1992). Impulsivity, risk-taking, and the ability to synthesize fragmented information after frontal lobectomy. Neuropsychologia, 30(1), 69–79.
Montague, P. R. (2012). The scylla and charybdis of neuroeconomic approaches to psychopathology. Biological Psychiatry, 72(2), 80–81.
Nielsen, L., & Mather, M. (2011). Emerging perspectives in social neuroscience and neuroeconomics of aging. Social Cognitive and Affective Neuroscience, 6(2), 149–164.
Office of Behavioral and Social Sciences Research. (2010). Better living through behavioral and social sciences. National Institutes of Health.
Plassman, B. L., Langa, K. M., et al. (2008). Prevalence of cognitive impairment without dementia in the United States. Annals of Internal Medicine, 148(6), 427–434.
Preuschoff, K., Quartz, S. R., et al. (2008). Human insula activation reflects risk prediction errors as well as risk. The Journal of Neuroscience, 28(11), 2745–2752.
Rothstein, M. A. (2009). Currents in contemporary ethics. The Journal of Law, Medicine and Ethics, 37(3), 507–512.
Rothstein, M. A. (2010). Is deidentification sufficient to protect health privacy in research? The American Journal of Bioethics, 10(9), 3–11.
Schultz, W., Dayan, P., et al. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599.
Templeton, V. H. M., & Kirkman, D. N. J. (2007). Fraud, vulnerability, and aging: Case studies. Alzheime’s Care Today, 8(3).
Tinbergen, N. (1951). The study of instinct.
Tinbergen, N. (1953). Social behaviour in animals: With special reference to vertebrates. Taylor & Francis.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and uncertainty, 5(4), 297–323.
Widera, E., Steenpass, V., et al. (2011). Finances in the older patient with cognitive impairment “He Didn’t Want Me to Take Over”. JAMA, the Journal of the American Medical Association, 305(7), 698–706.
Wu, S., Chaudhry, B., et al. (2006). Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine, 144(10), 742–752.
Zhu, L., Mathewson, K. E., et al. (2012). Dissociable neural representations of reinforcement and belief prediction errors underlying strategic learning. PNAS, 109(5), 1419–1424.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this chapter
Cite this chapter
Hsu, M., Chiong, W. (2016). From Laboratory to Clinic and Back: Connecting Neuroeconomic and Clinical Measures of Decision-Making Dysfunctions. In: Diefenbach, M., Miller-Halegoua, S., Bowen, D. (eds) Handbook of Health Decision Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3486-7_4
Download citation
DOI: https://doi.org/10.1007/978-1-4939-3486-7_4
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-3484-3
Online ISBN: 978-1-4939-3486-7
eBook Packages: Behavioral Science and PsychologyBehavioral Science and Psychology (R0)