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Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model
Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4936697/ https://www.ncbi.nlm.nih.gov/pubmed/27387139 http://dx.doi.org/10.1371/journal.pone.0158832 |
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author | Said, Nadia Engelhart, Michael Kirches, Christian Körkel, Stefan Holt, Daniel V. |
author_facet | Said, Nadia Engelhart, Michael Kirches, Christian Körkel, Stefan Holt, Daniel V. |
author_sort | Said, Nadia |
collection | PubMed |
description | Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods. |
format | Online Article Text |
id | pubmed-4936697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-49366972016-07-22 Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model Said, Nadia Engelhart, Michael Kirches, Christian Körkel, Stefan Holt, Daniel V. PLoS One Research Article Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods. Public Library of Science 2016-07-07 /pmc/articles/PMC4936697/ /pubmed/27387139 http://dx.doi.org/10.1371/journal.pone.0158832 Text en © 2016 Said et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Said, Nadia Engelhart, Michael Kirches, Christian Körkel, Stefan Holt, Daniel V. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model |
title | Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model |
title_full | Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model |
title_fullStr | Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model |
title_full_unstemmed | Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model |
title_short | Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model |
title_sort | applying mathematical optimization methods to an act-r instance-based learning model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4936697/ https://www.ncbi.nlm.nih.gov/pubmed/27387139 http://dx.doi.org/10.1371/journal.pone.0158832 |
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