A high-performance approach to model calibration and validation

Sue E. Kase, Frank E. Ritter

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

3 Scopus citations

Abstract

A new model validation approach is presented that integrates parallel processing on high-performance computing clusters with random search algorithms to fit cognitive models to human performance data. The efficiency, accuracy, and non-biasness of this approach surpasses the prevalent manual optimization techniques; results in exceptional model to human data fits; and is available and extendable to other parameterized models, search algorithms, cognitive architectures, and cluster computing resources. Results from testing the validation approach using a prototype cognitive model of a serial subtraction task, the ACT-R cognitive architecture, and 15 individual fits are described.

Original languageEnglish (US)
Title of host publication18th Annual Conference on Behavior Representation in Modeling and Simulation 2009, BRiMS 2009
Pages39-46
Number of pages8
StatePublished - 2009
Event18th Annual Conference on Behavior Representation in Modeling and Simulation 2009, BRiMS 2009 - Sundance, UT, United States
Duration: Mar 30 2009Apr 2 2009

Publication series

Name18th Annual Conference on Behavior Representation in Modeling and Simulation 2009, BRiMS 2009

Other

Other18th Annual Conference on Behavior Representation in Modeling and Simulation 2009, BRiMS 2009
Country/TerritoryUnited States
CitySundance, UT
Period3/30/094/2/09

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation

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