Using e-greedy reinforcement learning methods to further understand ventromedial prefrontal patients' deficits on the Iowa Gambling Task

Kalidindi, Kiran and Bowman, Howard and Wyble, Brad (2005) Using e-greedy reinforcement learning methods to further understand ventromedial prefrontal patients' deficits on the Iowa Gambling Task. Technical report. , Canterbury, Kent, CT2 7NF, UK (Full text available)

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Abstract

An important component of decision making is the process of choosing from a set of options. This choice is often based on an evaluation of the potential costs or benefits of selecting an option, its estimated reward value. The foundation for these estimations of costs and benefits is frequently past experience. The way past experience is used to predict future rewards and punishments can have profound effects on choice. Current literature suggests an important role for the orbitofrontal cortex (OFC), in both humans and non-human primates, in representing the reward value of stimuli. The role played by the ventromedial prefrontal cortex (a region which includes the medial OFC) in decision making has previously been tested with the Iowa Gambling Task (IGT), by comparing the performance of patients with bilateral ventromedial prefrontal lesions (VMF) and normal healthy controls (NHCs). A number of theories in the literature offer potential explanations for the underlying cause of the deficit(s) found in VMF patients on the IGT. An empherror-driven ε-greedy reinforcement learning method has been used to model both normative and VMF patient groups from a number of studies, offering support and, more importantly, a deeper understanding of the causes for the `myopia' for future consequences explanation of VMF patients deficits on the IGT.

Item Type: Monograph (Technical report)
Uncontrolled keywords: reinforcement learning, ventromedial prefrontal cortex, orbitofrontal cortex, decison making, Iowa gambling task
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Computational Intelligence Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:02
Last Modified: 12 May 2014 13:57
Resource URI: http://kar.kent.ac.uk/id/eprint/14252 (The current URI for this page, for reference purposes)
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