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Mental processes and strategic equilibration: An fMRI study of selling strategies in second price auctions

Published online by Cambridge University Press:  14 March 2025

David M. Grether
Affiliation:
California Institute of Technology
Charles R. Plott*
Affiliation:
California Institute of Technology
Daniel B. Rowe
Affiliation:
Medical College of Wisconsin
Martin Sereno
Affiliation:
University of California San Diego
John M. Allman
Affiliation:
California Institute of Technology

Abstract

This study is the first to attempt to isolate a relationship between cognitive activity and equilibration to a Nash Equilibrium. Subjects, while undergoing fMRI scans of brain activity, participated in second price auctions against a single competitor following predetermined strategy that was unknown to the subject. For this auction there is a unique strategy that will maximize the subjects’ earnings, which is also a Nash equilibrium of the associated game theoretic model of the auction. As is the case with all games, the bidding strategies of subjects participating in second price auctions most often do not reflect the equilibrium bidding strategy at first but with experience, typically exhibit a process of equilibration, or convergence toward the equilibrium. This research is focused on the process of convergence.

In the data reported here subjects participated in sixteen auctions, after which all subjects were told the strategy that will maximize their revenues, the theoretical equilibrium. Following that announcement, sixteen more auctions were performed. The question posed by the research concerns the mental activity that might accompany equilibration as it is observed in the bidding behavior. Does brain activation differ between being equilibrated and non-equilibrated in the sense of a bidding strategy? If so, are their differences in the location of activation during and after equilibration? We found significant activation in the frontal pole especially in Brodmann's area 10, the anterior cingulate cortex, the amygdala and the basal forebrain. There was significantly more activation in the basal forebrain and the anterior cingulate cortex during the first sixteen auctions than in the second sixteen. The activity in the amygdala shifted from the right side to the left after the solution was given.

Type
Research Article
Copyright
Copyright © 2007 Economic Science Association

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References

Allman, J. M., Watson, K., & Hakeem, A. (2002). Two phylogenetic specializations in the human brain. Neuroscientist, 8, 335347.CrossRefGoogle ScholarPubMed
Bandettini, P. M., Jesmanowicz, A., Wong, E. C., & Hyde, J. S. (1993). Processing strategies for time-course data sets in functional MRI of the human brain. Magnetic Resonance in Medicine, 30, 161173.CrossRefGoogle ScholarPubMed
Becker, G. M., DcGroot, M. H., & Marshak, J. (1964). Measuring utility by a single response sequential method. Behavioral Science, 9, 226232.CrossRefGoogle ScholarPubMed
Frank, L. R., Buxton, R. B., & Wong, E. C. (1998). Probabilistic analysis of functional magnetic resonance imaging data. Magnetic Resonance in Medicine, 39, 132148.CrossRefGoogle ScholarPubMed
Baxter, M. G., & Murray, E. A. (2002). The amygdala and reward. Nature Reviews, Neuroscience, 3, 563573.CrossRefGoogle ScholarPubMed
Critchley, H. D., Mathias, C. J., & Dolan, R. J. (2001). Neural activity in the human brain relating to uncertainty and arousal during anticipation. Neuron, 29(2), 537545.CrossRefGoogle ScholarPubMed
Dishbrow, E. A. et al. (2000). Functional MRI at 1.5 tesla: A comparison of the blood oxygenation level- dependent signal and electrophysiology. Proceedings of the National Academy of Sciences, 97, 97189723.Google Scholar
Edwards, W., Lindman, H., & Savage, L. J. (1963). Bayesian statistical inference for psychological research. Psychological Review, 70, 193242.CrossRefGoogle Scholar
Elliott, R., Friston, K., & Dolan, R. (2000). Dissociable neural responses in human reward systems. Journal of Neuroscience, 20, 61596165.CrossRefGoogle ScholarPubMed
Elliott, R., Newman, J., Longe, O., & Deakin, J. (2003). Instrumental responding for rewards is associated with enhanced neuronal response in subcortical reward systems. Neuroimage, 21(3), 984990.CrossRefGoogle Scholar
Ellsberg, D. (1961). Risk, ambiguity, and the Savage axions. Quarterly Journal of Economics, 75, 643669.CrossRefGoogle Scholar
Ernst, M. et al. (2001). Decision-making in a risk-taking task: a PET study. Neuropsychopharmacology, 26, 682691.CrossRefGoogle Scholar
Fried, I. et al. (2001). Increased dopamine release in the human amygdala during performance of cognitive tasks. Nature Neuroscience, 201206.CrossRefGoogle Scholar
Friston, K. J., Jezzard, P., & Turner, R. (1994). Analysis of functional MRI time-series. Human Brain Mapping, 18, 153171.CrossRefGoogle Scholar
Friston, K. J., & Penny, W. (2003). Posterior probability maps and SPMs. NeuroImage, 19, 12401249.CrossRefGoogle ScholarPubMed
Friston, K. J., Penny, W. D., Phillips, C., Kiebel, S. J., Hinton, G., & Ashburner, J. (2002a). Classical and Bayesian inference in neuroimaging: theory. NeuroImage, 16, 465483.CrossRefGoogle ScholarPubMed
Friston, K. J., Glaser, D. E., Henson, R. N. A., Kiebel, S. J., Phillips, C., & Ashburner, J. (2002b) Classical and Bayesian inference in neuroimaging: applications. NeuroImage, 16, 484512.CrossRefGoogle ScholarPubMed
Gehring, W.J., & Willoughby, A. R. (2002). The medial frontal cortex and the rapid processing of monetary gains and losses. Science, 295, 22792282.CrossRefGoogle ScholarPubMed
Genovese, C. R. (2000). A Bayesian time course model for functional magnetic resonance imaging data. Journal of the American Statistical Association, 95, 691703.CrossRefGoogle Scholar
Glimcher, P. W. (2003). Decisions, uncertainty and the brain: the science of neuroeconomics. MIT Press.CrossRefGoogle Scholar
Gossl, C., Fahrmeir, L., & Auer, D. P. (2001). Bayesian modeling of the hemodynamic response function in BOLD fMRI. Neuroimage, 14, 140148.CrossRefGoogle ScholarPubMed
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1990). Experimental tests of the endowment effect and the coarse theorem. The Journal of Political Economy, 98, 13251348.CrossRefGoogle Scholar
Kahneman, D., Slovic, P., & Tversky, A. (eds.) (1982). Judgment under uncertainty: heuristics and biases. Cambridge University Press.CrossRefGoogle Scholar
Kershaw, J., & Ardekani, B. A. (1999). Application of Bayesian inference to fMRI data analysis. iEEE Transactions on Medical Imaging, 18, 11381153.CrossRefGoogle ScholarPubMed
Knutson, B., Fong, G. W., Bennett, S. M., Adams, C. M., & Hommer, D. (2003). A region of mesial prefrontal cortex tracks monetarily rewarding outcomes: characterization with rapid event-related fMRI. Neuroimage, 18(2), 263272.CrossRefGoogle ScholarPubMed
LeDoux, J. (1995). The emotional brain. New York: Simon & Schuster.Google Scholar
Lindley, D. V., & Smith, A. F. M. (1972). Bayes estimates for the linear model. Journal of the Royal Statistical Society, 34, 119.CrossRefGoogle Scholar
Liotti, M., Brannan, S., Egan, G., Shade, R., Madden, L., Abplanalp, B., Robillard, R., Lancaster, J., Zamarripa, F., Fox, O., & Denton, D. (2001). Brain responses associated with consciousness of breathlessness (air hunger). Proceeding of the National Academy of Sciences, 98, 20352040.CrossRefGoogle ScholarPubMed
McCabe, K., Houser, D., Ryan, L., Smith, V., & Tromard, T. (2001). A functional imaging study of cooperation in two-person reciprocal exchange. Proceedings of the National Academy of Sciences, 98(20), 1183211835.CrossRefGoogle ScholarPubMed
Plott, C. R. (1996). Rational individual behavior in markets and social choice processes: the discovered preference hypothesis. In Arrow, K. J., Colombatto, E., Perlman, M. and Schmidt, C. (eds.), The rational foundations of economic behavior.Google Scholar
Plott, C. R., & Zeiler, K. (2003). The willingness to pay/willingness to accept gap, the ‘endowment effect,’ subject misconceptions and experimental procedures for eliciting valuations. American Economic Review (submitted).Google Scholar
Raichle, M., Feiz, J. A., Videen, T. O., MacLeod, A. M., Pardo, J. V., Fox, P. T., & Petersen, S. E. (1994). Practice-related changes in human brain functional anatomy during nonmotor learning. Cerebral Cortex, 4, 826.CrossRefGoogle Scholar
Rickard, T., Romero, S., Basso, G., Wharton, C., Flitman, S., & Grafman, J. (2000). The calculating brain: an fMRI study. Neuropsychologia, 38, 325335.CrossRefGoogle Scholar
Rowe, D. B. (2001). Bayesian source separation for reference function determination in fMRI. Magnetic Resonance in Medicine, 46, 374378.CrossRefGoogle ScholarPubMed
Rowe, D. B (2003). Multivariate Bayesian statistics: models for source separation and signal unmixing. Chapman and Hall: CRC Press.Google Scholar
Russo, E. (2000). Debating the meaning of fMRI. The Scientist, 14, 1820.Google Scholar
Schultz, , et al. (2000). Reward processing in primate orbital frontal cortex and basal ganglia. Cerebral Cortex, 10, 272284.CrossRefGoogle Scholar
Schultz, W. (2002). Getting formal with dopamine and reward. Neuron, 36, 241263.CrossRefGoogle ScholarPubMed
Semendeferi, K., Armstrong, E., Schleicher, A., Zilles, K., & Van Hoesen, G. (2001). Prefrontal cortex in humans and apes: a comparative study of area 10. American Journal of Physical Anthropology, 114, 224241.3.0.CO;2-I>CrossRefGoogle ScholarPubMed
Smith, A. et al. (1999). Investigation of low frequency drift in fMRI signal. Neuroimage, 9, 526533.CrossRefGoogle ScholarPubMed
Smith, K. et al. (2002). Neuronal substrates for choice under ambiguity, risk, certainty, gains and losses. Management Science, 48, 711718.CrossRefGoogle Scholar