Heuristics Used By Humans With Prefrontal Cortex Damage: Toward An Empirical Model Of Phineas Gage
In 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.
|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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19372, University of Munich, Department of Economics.
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- 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.
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