Skip to main content

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

Kalidindi, Kiran, Bowman, Howard (2007) Using e-greedy reinforcement learning methods to further understand ventromedial prefrontal patients' deficits on the Iowa Gambling Task. Neural Networks, 20 (6). pp. 676-689. ISSN 0893-6080. (doi:10.1016/j.neunet.2007.04.026) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:14601)

PDF (Using e-greedy reinforcement learning methods)
Language: English

Restricted to Repository staff only
[thumbnail of Using e-greedy reinforcement learning methods]
Official URL
http://dx.doi.org/10.1016/j.neunet.2007.04.026

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 errordriven e-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.

Item Type: Article
DOI/Identification number: 10.1016/j.neunet.2007.04.026
Additional information: In Press
Uncontrolled keywords: Iowa gambling task, ventromedial prefrontal cortex, orbitofrontal cortex, reinforcement learning, decision making
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:05 UTC
Last Modified: 16 Nov 2021 09:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14601 (The current URI for this page, for reference purposes)
Bowman, Howard: https://orcid.org/0000-0003-4736-1869
  • Depositors only (login required):