TY - GEN
T1 - Modeling chunking effects on learning and performance using the Computational-Unified Learning Model (C-ULM)
T2 - 15th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016
AU - Shell, Duane F.
AU - Soh, Leen Kiat
AU - Chiriacescu, Vlad
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under grants no. DUE-1122956 and DUE-1431874 and by a UNL Pathways to Interdisciplinary Research Centers grant.
Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/21
Y1 - 2017/2/21
N2 - 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.
AB - 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.
KW - Chunking
KW - Cognitive Computing
KW - Cognitive machine learning
KW - Cognitive process models
KW - Cognitive processes of the brain
KW - Multiagent Systems
KW - Statistical Learning
KW - Unified Learning Model
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U2 - 10.1109/ICCI-CC.2016.7862098
DO - 10.1109/ICCI-CC.2016.7862098
M3 - Conference contribution
AN - SCOPUS:85016169307
T3 - Proceedings of 2016 IEEE 15th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016
SP - 77
EP - 85
BT - Proceedings of 2016 IEEE 15th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016
A2 - Plataniotis, Kostas
A2 - Widrow, Bernard
A2 - Wang, Yingxu
A2 - Howard, Newton
A2 - Zadeh, Lotfi A.
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 August 2016 through 23 August 2016
ER -