Predicting Learning and Retention of a Complex Task

Jacob D. Oury, Farnaz Tehranchi, Frank E. Ritter

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

Abstract

We use an ACT-R model of a complex task to explore the implications of ACT-R's learning and forgetting mechanisms to better understand learning and retention. The model performs a task that has 14 non-iterated subtasks that takes approximately 25 min. to perform the first time. The results show that a typical learning curve is generated by the model that is well fit to human data. When decay is examined we find that the retention curves basically match the shapes predicted by the KRK theory, and that training and testing have been confounded in many studies. From these results we see that the previously hypothesized mixed declarative procedural stage of learning actually starts on the first trial and is never completely exited, so we will need to propose other thresholds to mark transitions between declarative, mixed, and proceduralized knowledge. We predict based on this model that learning and retention will vary greatly by task components, practice schedule, and learner's strategy.

Original languageEnglish (US)
Title of host publicationProceedings of ICCM 2018 - 16th International Conference on Cognitive Modeling
EditorsIon Juvina, Joseph Houpt, Christopher Myers
PublisherUniversity of Wisconsin
Pages90-95
Number of pages6
ISBN (Electronic)9780998508221
StatePublished - 2018
Event16th International Conference on Cognitive Modeling, ICCM 2018 - Wisconsin, United States
Duration: Jul 21 2018Jul 24 2018

Publication series

NameProceedings of ICCM 2018 - 16th International Conference on Cognitive Modeling

Conference

Conference16th International Conference on Cognitive Modeling, ICCM 2018
CountryUnited States
CityWisconsin
Period7/21/187/24/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Modeling and Simulation

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  • Cite this

    Oury, J. D., Tehranchi, F., & Ritter, F. E. (2018). Predicting Learning and Retention of a Complex Task. In I. Juvina, J. Houpt, & C. Myers (Eds.), Proceedings of ICCM 2018 - 16th International Conference on Cognitive Modeling (pp. 90-95). (Proceedings of ICCM 2018 - 16th International Conference on Cognitive Modeling). University of Wisconsin.