Heuristics Used By Humans With Prefrontal Cortex Damage: Toward An Empirical Model Of Phineas Gage
AbstractIn many research contexts it is necessary to group experimental subjects into behavioral “types.” Usually, this is done by pre-specifying a set of candidate decision-making heuristics and then assigning each subject to the heuristic that best describes his/her behavior. Such approaches might not perform well when used to explain the behavior of subjects with prefrontal cortex damage. The reason is that introspection is typically used to generate the candidate heuristic set, but this procedure is likely to fail when applied to the decision-making strategies of subjects with brain damage. This research uses the type classification approach introduced by Houser, Keane and McCabe (2002) to investigate the heuristics used by subjects in the gambling experiment (Bechara, Damasio, Damasio and Anderson, 1994). An advantage of our classification approach is that it does not require us to specify the nature of subjects’ heuristics in advance. Rather, both the number and nature of the heuristics used are discerned directly from the experimental data. Our sample includes normal subjects, as well as subjects with damage to the ventromedial (VM) area of the prefrontal cortex. Subjects are “clustered” according to similarities in their heuristic, and this clustering does not preclude some normal and VM subjects from using the same decision rule. Our results are consistent with what others have found in subsequent experimentation with VM patients.
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Bibliographic InfoPaper provided by EconWPA in its series Experimental with number 0308002.
Length: 18 pages
Date of creation: 11 Aug 2003
Date of revision:
Note: Type of Document - pdf; prepared on IBM PC ; to print on PostScript; pages: 18; figures: included
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experiments; heuristics; neuroeconomics; behavioral economics;
Find related papers by JEL classification:
- C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
- D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-08-17 (All new papers)
- NEP-CBE-2003-08-17 (Cognitive & Behavioural Economics)
- NEP-EXP-2003-08-17 (Experimental Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- John F. Geweke & Michael P. Keane, 1996. "Bayesian inference for dynamic choice models without the need for dynamic programming," Working Papers 564, Federal Reserve Bank of Minneapolis.
- Daniel Houser & Joachim Winter, 2002.
"How Do Behavioral Assumptions Affect Structural Inference? Evidence From A Laboratory Experiment,"
MEA discussion paper series
02005, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
- Houser, Daniel & Winter, Joachim, 2004. "How Do Behavioral Assumptions Affect Structural Inference? Evidence from a Laboratory Experiment," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 64-79, January.
- Houser, Daniel & Winter, Joachim, 2004. "How Do Behavioral Assumptions Affect Structural Inference? Evidence from a Laboratory Experiment," Munich Reprints in Economics 19372, University of Munich, Department of Economics.
- El-Gamal, Mahmoud A. & Grether, David M., 1995. "Are People Bayesian? Uncovering Behavioral Strategies," Working Papers 919, California Institute of Technology, Division of the Humanities and Social Sciences.
- Daniel Houser & Michael Keane & Kevin McCabe, 2002.
"Behavior in a dynamic decision problem: An analysis of experimental evidence using a bayesian type classification algorithm,"
- Daniel Houser & Michael Keane & Kevin McCabe, 2004. "Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm," Econometrica, Econometric Society, vol. 72(3), pages 781-822, 05.
- Geweke, John & Houser, Dan & Keane, Michael, 1999. "Simulation Based Inference for Dynamic Multinomial Choice Models," MPRA Paper 54279, University Library of Munich, Germany.
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