Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-04T07:23:14.709Z Has data issue: false hasContentIssue false

FROM COGNITIVE SCIENCE TO COGNITIVE NEUROSCIENCE TO NEUROECONOMICS

Published online by Cambridge University Press:  01 November 2008

Steven R. Quartz*
Affiliation:
California Institute of Technology

Abstract

As an emerging discipline, neuroeconomics faces considerable methodological and practical challenges. In this paper, I suggest that these challenges can be understood by exploring the similarities and dissimilarities between the emergence of neuroeconomics and the emergence of cognitive and computational neuroscience two decades ago. From these parallels, I suggest the major challenge facing theory formation in the neural and behavioural sciences is that of being under-constrained by data, making a detailed understanding of physical implementation necessary for theory construction in neuroeconomics. Rather than following a top-down strategy, neuroeconomists should be pragmatic in the use of available data from animal models, information regarding neural pathways and projections, computational models of neural function, functional imaging and behavioural data. By providing convergent evidence across multiple levels of organization, neuroeconomics will have its most promising prospects of success.

Type
Essay
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Baker, L. 1989. Instrumental intentionality. Philosophy of Science 56: 303–16.CrossRefGoogle Scholar
Bruni, L. and Sugden, R. 2007. The road not taken: how psychology was removed from economics, and how it might be brought back. The Economic Journal 117: 146–73.CrossRefGoogle Scholar
Chomsky, N. 1959. A review of B. F. Skinner's verbal behavior. Language 35: 2658.CrossRefGoogle Scholar
Churchland, P. S., Ramachandran, V. S. and Sejnowski, T. J. 1994. A critique of pure vision. In Large-Scale Neuronal Theories of the Brain, ed. Koch, C. and Davis, J. L., 2351. Cambridge, MA: MIT Press.Google Scholar
Churchland, P. S. and Sejnowski, T. J. 1988. Perspectives on cognitive neuroscience. Science 242: 741–5.CrossRefGoogle ScholarPubMed
Damasio, A. R. 1994. Descartes’ error: emotion, reason, and the human brain. New York: G.P. Putnam.Google Scholar
Dayan, P. and Abbott, L. F.. 2001. Theoretical neuroscience: computational and mathematical modeling of neural systems. Cambridge, MA: MIT Press.Google Scholar
Dennett, D. 1987. The intentional stance. Cambridge, MA: MIT Press.Google Scholar
Friedman, M. 1953. The methodology of positive economics. In Essays in Positive Economics, 343. Chicago: University of Chicago Press.Google Scholar
Gazzaniga, M. S. 2004. The cognitive neurosciences, 3rd Edn. Cambridge, MA: MIT Press.Google Scholar
Glimcher, P. W. 2004. Decisions, uncertainty, and the brain: the science of neuroeconomics. Cambridge, MA: MIT Press.Google Scholar
Gul, F. and Pesendorfer, W. 2008. The case for mindless economics. In The handbook of economic methodologies, ed. Caplin, A. and Schotter, A.. Oxford: Oxford University Press.Google Scholar
Hubel, D. H. and Wiesel, T. N. 1959. Receptive fields of single neurones in the cat's striate cortex. Journal of Physiology 148: 574–91.CrossRefGoogle ScholarPubMed
Jevons, W. S. 1871. The theory of political economy. New York: MacMillan and Co.Google Scholar
King-Casas, B., Tomlin, D., Anen, C., Camerer, C., Quartz, S. R. and Montague, P. R.. 2005. Getting to know you: reputation and trust in a two-person economic exchange. Science 308: 7883.CrossRefGoogle Scholar
Koob, G. F. and Swerdlow, N. R. 1988. The functional output of the mesolimbic dopamine system. Annals of the New York Academy of Sciences 537: 216–27.CrossRefGoogle ScholarPubMed
Kuffler, S. 1953. Discharge patterns and functional organization of the mammalian retina. Journal of Neurophysiology 16: 3768.CrossRefGoogle Scholar
Lagueux, M. 1994. Friedman instrumentalism and constructive empiricism in economics. Theory and Decision 37: 147–74.CrossRefGoogle Scholar
Maas, H. 2005. William Stanley Jevons and the making of modern economics. New York: Cambridge University Press.Google Scholar
Markowitz, H. 1952. Portfolio selection. Journal of Finance 7: 7791.Google Scholar
Marr, D. 1982. Vision : a computational investigation into the human representation and processing of visual information. San Francisco: W.H. Freeman.Google Scholar
Minsky, M. and Papert, S. 1969. Perceptrons: An introduction to computational geometry. Cambridge, MA: MIT Press.Google Scholar
Montague, P. R., Dayan, P., Person, C. and Sejnowski, T. J.. 1995. Bee foraging in uncertain environments using predictive hebbian learning. Nature 377: 725–8.CrossRefGoogle ScholarPubMed
Montague, P. R., Dayan, P. and Sejnowski, T. J.. 1996. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience 16: 1936–47.CrossRefGoogle ScholarPubMed
O'Doherty, J. P., Buchanan, T. W., Seymour, B. and Dolan, R. J.. 2006. Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum. Neuron 49: 157–66.CrossRefGoogle ScholarPubMed
Paulus, M. P. 2007. Decision-making dysfunctions in psychiatry – altered homeostatic processing? Science 318: 602–6.CrossRefGoogle ScholarPubMed
Pessoa, L. 2008. On the relationship between emotion and cognition. Nature Reviews Neuroscience 9: 148–58.CrossRefGoogle ScholarPubMed
Preuschoff, K., Bossaerts, P. and Quartz, S. R.. 2006. Neural differentiation of expected reward and risk in human subcortical structures. Neuron 51: 381–90.CrossRefGoogle ScholarPubMed
Putnam, H. 1975. Mind, language, and reality: Philosophical papers, Vol. 2. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Pylyshyn, Z. 1984. Computation and cognition. Cambridge, MA: MIT Press.Google Scholar
Quartz, S. R. and Sejnowski, T. J.. 1997. The neural basis of cognitive development: a constructivist manifesto. Behavioral and Brain Sciences 20: 537–56; discussion 556.CrossRefGoogle ScholarPubMed
Rogers, T. T. and McClelland, J. L.. 2004. Semantic cognition: A parallel distributed processing approach. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Rosenblatt, F. 1958. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65: 386408.CrossRefGoogle ScholarPubMed
Ross, D. 2005. Economic theory and cognitive science: microexplanation. Cambridge, MA: MIT Press.Google Scholar
Rumelhart, D. and McClelland, J. L.. 1986. Parallel distributed processing: explorations in the microstructure of cognition, 2 Vols. Computational models of cognition and perception. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L E. and Cohen, J. D. 2003. The neural basis of economic decision-making in the Ultimatum Game. Science 300: 1755–8.CrossRefGoogle ScholarPubMed
Sporns, O. and Zwi, J. D. 2004. The small world of the cerebral cortex. Neuroinformatics 2: 145–62.CrossRefGoogle ScholarPubMed
Sutton, R. S. and Barto, A. G.. 1998. Reinforcement learning: an introduction, adaptive computation and machine learning. Cambridge, MA: MIT Press.Google Scholar
Werbos, P. 1994. The roots of backpropagation. New York: Wiley.Google Scholar