Modeling chunking effects on learning and performance using the Computational-Unified Learning Model (C-ULM): A multiagent cognitive process model

Duane F. Shell, Leen Kiat Soh, Vlad Chiriacescu

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

1 Scopus citations

Abstract

Chunking has emerged as a basic property of human cognition. Computationally, chunking has been proposed as a process for compressing information also has been identified in neural processes in the brain and used in models of these processes. Our purpose in this paper is to expand understanding of how chunking impacts both learning and performance using the Computational-Unified Learning Model (C-ULM) a multi-agent computational model. Chunks in C-ULM long-term memory result from the updating of concept connection weights via statistical learning. Concept connection weight values move toward the accurate weight value needed for a task and a confusion interval reflecting certainty in the weight value is shortened each time a concept is attended in working memory and each time a task is solved, and the confusion interval is lengthened when a chunk is not retrieved over a number of cycles and each time a task solution attempt fails. The dynamic tension between these updating mechanisms allows chunks to come to represent the history of relative frequency of co-occurrence for the concept connections present in the environment; thereby encoding the statistical regularities in the environment in the long-term memory chunk network. In this paper, the computational formulation of chunking in the C-ULM is described, followed by results of simulation studies examining impacts of chunking versus no chunking on agent learning and agent effectiveness. Then, conclusions and implications of the work both for understanding human learning and for applications within cognitive informatics, artificial intelligence, and cognitive computing are discussed.

Original languageEnglish (US)
Title of host publicationProceedings of 2016 IEEE 15th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016
EditorsKostas Plataniotis, Bernard Widrow, Yingxu Wang, Newton Howard, Lotfi A. Zadeh
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages77-85
Number of pages9
ISBN (Electronic)9781509038466
DOIs
StatePublished - Feb 21 2017
Event15th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016 - Stanford, United States
Duration: Aug 22 2016Aug 23 2016

Publication series

NameProceedings of 2016 IEEE 15th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016

Other

Other15th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016
CountryUnited States
CityStanford
Period8/22/168/23/16

Keywords

  • Chunking
  • Cognitive Computing
  • Cognitive machine learning
  • Cognitive process models
  • Cognitive processes of the brain
  • Multiagent Systems
  • Statistical Learning
  • Unified Learning Model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Cognitive Neuroscience
  • Software
  • Computer Vision and Pattern Recognition
  • Information Systems

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  • Cite this

    Shell, D. F., Soh, L. K., & Chiriacescu, V. (2017). Modeling chunking effects on learning and performance using the Computational-Unified Learning Model (C-ULM): A multiagent cognitive process model. In K. Plataniotis, B. Widrow, Y. Wang, N. Howard, & L. A. Zadeh (Eds.), Proceedings of 2016 IEEE 15th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016 (pp. 77-85). [7862098] (Proceedings of 2016 IEEE 15th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCI-CC.2016.7862098