A reinforcement learning model of bounded optimal strategy learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Colleges, School and Institutes

Abstract

In this paper we report a reinforcement learning model of how individuals learn the value of strategies for remembering. The model learns from experience about the changing speed and accuracy of memory strategies. The reward function was sensitive to the internal information processing constraints (limited working memory capacity) of the participants. In addition, because the value of strategies for remembering changed with practice, experience was discounted according to a recency-weighted function. The model was used to generate predictions of the behavioural data of 40 participants who were asked to copy appointment information from an email message to a calendar. The experience discounting parameter for a model of each individual participant was set so as to maximize the expected rewards for that participant. The predictions of this bounded optimal control model were compared with the observed data. The result suggests that people may be able to choose remembering strategies on the basis of optimally discounted past experience.

Bibliographic note

Copyright: Copyright 2013 Elsevier B.V., All rights reserved.

Details

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Cognitive Modeling, ICCM 2012
Publication statusPublished - 2012
Event11th International Conference on Cognitive Modeling, ICCM 2012 - Berlin, Germany
Duration: 13 Apr 201215 Apr 2012

Publication series

NameProceedings of the 11th International Conference on Cognitive Modeling, ICCM 2012

Conference

Conference11th International Conference on Cognitive Modeling, ICCM 2012
CountryGermany
CityBerlin
Period13/04/1215/04/12

Keywords

  • Bounded optimal, Information processing bounds, Memory constraints, Reinforcement learning