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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 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.

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Metadaten
Author:Nadia Said, Michael Engelhart, Christian Kirches, Stefan KörkelGND, Daniel V. Holt
DOI:https://doi.org/10.1371/journal.pone.0158832
Parent Title (English):PLoS ONE
Publisher:PLOS
Document Type:Article
Language:English
Year of first Publication:2016
Release Date:2022/11/26
Volume:11
Issue:7
Article Number:e0158832
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
OpenAccess Publikationsweg:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG