Using ε{lunate}-greedy reinforcement learning methods to further understand ventromedial prefrontal patients' deficits on the Iowa Gambling Task

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Abstract

An important component of decision making is evaluating the expected result of a choice, using past experience. The way past experience is used to predict future rewards and punishments can have profound effects on decision making. The aim of this study is to further understand the possible role played by the ventromedial prefrontal cortex in decision making, using results from the Iowa Gambling Task (IGT). A number of theories in the literature offer potential explanations for the underlying cause of the deficit(s) found in bilateral ventromedial prefrontal lesion (VMF) patients on the IGT. An error-drivenε{lunate} -greedy reinforcement learning method was found to produce a good match to both human normative and VMF patient groups from a number of studies. The model supports the theory that the VMF patients are less strategic (more explorative), which could be due to a working memory deficit, and are more reactive than healthy controls. This last aspect seems consistent with a 'myopia' for future consequences. © 2007 Elsevier Ltd. All rights reserved.

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Kalidindi, K., & Bowman, H. (2007). Using ε{lunate}-greedy reinforcement learning methods to further understand ventromedial prefrontal patients’ deficits on the Iowa Gambling Task. Neural Networks, 20(6), 676–689. https://doi.org/10.1016/j.neunet.2007.04.026

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