Duffin, E, Bland, AR, Schaefer, A et al. (1 more author) (2014) Differential effects of reward and punishment in decision making under uncertainty: A computational study. Frontiers in Neuroscience, 8 (8 FEB). 30 - ?. ISSN 1662-4548
Abstract
Computational models of learning have proved largely successful in characterizing potential mechanisms which allow humans to make decisions in uncertain and volatile contexts. We report here findings that extend existing knowledge and show that a modified reinforcement learning model, which has separate parameters according to whether the previous trial gave a reward or a punishment, can provide the best fit to human behavior in decision making under uncertainty. More specifically, we examined the fit of our modified reinforcement learning model to human behavioral data in a probabilistic two-alternative decision making task with rule reversals. Our results demonstrate that this model predicted human behavior better than a series of other models based on reinforcement learning or Bayesian reasoning. Unlike the Bayesian models, our modified reinforcement learning model does not include any representation of rule switches. When our task is considered purely as a machine learning task, to gain as many rewards as possible without trying to describe human behavior, the performance of modified reinforcement learning and Bayesian methods is similar. Others have used various computational models to describe human behavior in similar tasks, however, we are not aware of any who have compared Bayesian reasoning with reinforcement learning modified to differentiate rewards and punishments.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 Duffin, Bland, Schaefer and de Kamps. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CCBY).The use, distribution or reproduction in other forums is permitted, provided the original author(s)or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission. |
Keywords: | Bayesian learning; Decision making; Reinforcement learning; Reward and punishment; Uncertainty; Volatility |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Nov 2014 13:45 |
Last Modified: | 23 Jun 2023 21:42 |
Published Version: | http://dx.doi.org/10.3389/fnins.2014.00030 |
Status: | Published |
Publisher: | Frontiers Media SA |
Identification Number: | 10.3389/fnins.2014.00030 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:80981 |