piKeywords: Expectancy Valence model Bayesian hierarchical modeling Cognitive modeling

assumed to jointly determine choice behavior in the Iowa gambling task: weighing of wins versus losses, memory for past payoffs, and response consistency. In this article we explore the statistical properties of the Expectancy Valence model. We first demonstrate the difficulty of applying the model on the level of a single participant, we then propose and implement a Bayesian hierarchical estimation procedure to coherently combine information from different participants, andwe finally apply the Bayesian estimation procedure to data from an experiment designed to provide a test of specific influence. © 2008 Elsevier Inc. All rights reserved. Every neuroscientist knows the tale of Phineas Gage, the railroad worker who suffered an unfortunate accident: in 1848, an explosion drove an iron rod straight through Gage’s frontal cortex. Although Gage miraculously survived the accident, the resultant brain trauma did cause a distinct change in his personality. Prior to the accident, Gage was capable and reliable, but after the accident he was described as impatient, stubborn, and impulsive. Gage was no longer able to plan ahead in order to achieve long–term goals.1 The symptoms of Phineas Gage are characteristic for patients with damage to the ventromedial prefrontal cortex (vmPFC). These patients often take irresponsible decisions and do not seem to learn from their mistakes. The observed real–life decision making deficits are not caused by low intelligence, as vmPFC patients generally perform adequately on standard IQ tests. In order to study the decision making behavior of clinical populations such as vmPFC patients under controlled conditions, Bechara and Damasio developed the now–famous ‘‘Iowa gamblin