Metacognition for a Common Model of Cognition Jerald D
Available online at www.sciencedirect.com Procedia Computer Science 00 (2019) 000–000 www.elsevier.com/locate/procedia Postproceedings of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2018 (Ninth Annual Meeting of the BICA Society) Metacognition for a Common Model of Cognition Jerald D. Kralika,∗, Jee Hang Leeb, Paul S. Rosenbloomc, Philip C. Jackson, Jr.d, Susan L. Epsteine, Oscar J. Romerof, Ricardo Sanzg, Othalia Larueh, Hedda R. Schmidtkei, Sang Wan Leea,b, Keith McGreggorj aDepartment of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea bKI for Health Science and Technology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea cInstitute for Creative Technologies & Department of Computer Science, University of Southern California, Los Angeles, CA, USA dTalaMind LLC, PMB #363, 55 E. Long Lake Rd., Troy, MI, USA eDepartment of Computer Science, Hunter College and The Graduate Center of the City University of New York, New York, NY, USA fDepartment of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA gAutonomous Systems Laboratory, Universidad Polit´ecnicade Madrid, Spain hDepartment of Psychology, Wright State University, Dayton, Ohio, USA iDepartment of Geography, University of Oregon, Eugene, OR, USA jCollege of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA Abstract This paper provides a starting point for the development of metacognition in a common model of cognition. It identifies significant theoretical work on metacognition from multiple disciplines that the authors believe worthy of consideration. After first defining cognition and metacognition, we outline three general categories of metacognition, provide an initial list of its main components, consider the more difficult problem of consciousness, and present examples of prominent artificial systems that have implemented metacognitive components.
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