Modeling self-efficacy using the computational-Unified Learning Model (C-ULM): Implications for computational psychology and cognitive computing

Duane F. Shell, Leen Kiat Soh, Vlad Chiriacescu

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

3 Scopus citations

Abstract

Self-efficacy is defined as a person's subjective confidence in their capability of executing an action and has been shown to be one of the most powerful motivators of human action predicting performance across a variety of domains. Self-efficacy has been associated with brain level neural processes and efficacy-like confidence mechanisms are incorporated into decision making in many cognitive informatics and cognitive computing models. Current computational implementations, however, do not directly model self-efficacy at either the theoretical or neural level. This paper reports on the computational modeling of self-efficacy based on principles derived from the Unified Learning Model (ULM) as instantiated in the multi-agent Computational ULM (C-ULM). Description of the modeling of self-efficacy within the C-ULM is provided. Results from simulations of self-efficacy evolution due to teaching and learning, task feedback, and knowledge decay are presented. The C-ULM simulation is unique in tying self-efficacy directly to the evolution of knowledge itself, consistent with recent neurological findings, and in dynamically updating self-efficacy at each step during learning and task attempts. Implications for research into human motivation and learning and for cognitive computing are discussed.

Original languageEnglish (US)
Title of host publicationProceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014
EditorsGabriele Fariello, Witold Kinsner, Yingxu Wang, Shushma Patel, Lotfi A. Zadeh, Dilip Patel
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages117-125
Number of pages9
ISBN (Electronic)9781479960811
DOIs
StatePublished - Oct 10 2014
Event13th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014 - London, United Kingdom
Duration: Aug 18 2014Aug 20 2014

Publication series

NameProceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014

Conference

Conference13th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014
CountryUnited Kingdom
CityLondon
Period8/18/148/20/14

Keywords

  • Cognitive computing
  • Cognitive modeling
  • Computational psychology
  • Motivation
  • Multi-agent systems
  • Self-Efficacy
  • Unified Learning Model

ASJC Scopus subject areas

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

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    Shell, D. F., Soh, L. K., & Chiriacescu, V. (2014). Modeling self-efficacy using the computational-Unified Learning Model (C-ULM): Implications for computational psychology and cognitive computing. In G. Fariello, W. Kinsner, Y. Wang, S. Patel, L. A. Zadeh, & D. Patel (Eds.), Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014 (pp. 117-125). [6921450] (Proceedings of 2014 IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCI-CC.2014.6921450