Clarion Publications

List of publications on Clarion

Quick sketches of the Clarion cognitive architecture

• • R. Sun, The CLARION cognitive architecture: Towards a comprehensive theory of the mind. In: S. Chipman (ed.), The Oxford Handbook of Cognitive Science. Oxford University Press, New York. 2017.

  • R. Sun, The CLARION cognitive architecture: Extending cognitive modeling to social simulation. In: R. Sun (ed.), Cognition and Multi-Agent Interaction. Cambridge University Press, New York. 2006.

  • R. Sun, The motivational and metacognitive control in CLARION. In: W. Gray (ed.), Modeling Integrated Cognitive Systems. Oxford University Press, New York. 2007.

  • R. Sun, T. Peterson, and C. Sessions, Beyond simple rule extraction: acquiring planning knowledge from neural networks. Proceedings of WIRN'01, Salermo, Italy. Springer-Verlag, 2001.

Detailed information on the Clarion cognitive architecture

• • R. Sun, Anatomy of the Mind. Oxford University Press, New York. 2016. (It contains accessible descriptions of technical details of CLARION 6.0.)

  • R. Sun, Duality of the Mind. Lawrence Erlbaum Associates, 2002.

• • R. Sun, A Detailed Specification of CLARION 5.0 . Technical report, Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, NY. 2003. (It contains detailed technical specifications of CLARION 5.0.)

    • Addendum 1: The enhanced description of the motivational subsystem.

    • Addendum 2: The enhanced description of similarity-based reasoning.

    • Addendum 3: The properties of the CLARION-H implementation.

    • Addendum 4: Q and A.

• • A much simplified (non-technical) description of CLARION 5.0, written by a student, providing some general ideas concerning CLARION: A Simplified Introduction to CLARION 5.0, or A Simplified Introduction to CLARION 5.0 (Technical report, Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, NY. 2004.)

  • A summary of (and a commentary on) motivational aspects of Clarion, written by a student: The Importance of Motivation (Bachelor's Thesis, Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, NY. 2022.)

Comparison of the Clarion cognitive architecture

• • S. Helie and R. Sun, Autonomous learning in psychologically oriented cognitive architectures: A survey. New Ideas in Psychology. Vol.34, pp.37–55. 2014.

For details of cognitive modeling, see the following papers:

Cognitive modeling using the Clarion cognitive architecture

• • R. Sun, J. Allen, and E. Werbin, Modeling emotion contagion within a computational cognitive architecture. Journal of Cognition and Culture, Vol.22, No.1-2, pp.60-89. 2022. https://doi.org/10.1163/15685373-12340125

  • R. Sun, S. Bugrov, and D. Dai, A unified framework for interpreting a range of motivation-performance phenomena. Cognitive Systems Research, Vol.71, pp.24–40. 2022. https://authors.elsevier.com/a/1dy9L4xrDwMyCV https://doi.org/10.1016/j.cogsys.2021.09.003

  • N. Wilson and R. Sun, A mechanistic account of stress-induced performance degradation. Cognitive Computation, Vol.13, No.1, pp.207-227. 2021. https://rdcu.be/b34GG https://dx.doi.org/10.1007/s12559-020-09725-5

  • R. Sun, Exploring culture from the standpoint of a cognitive architecture. Philosophical Psychology, Vol.33, No.2, pp.155-180. 2020. https://doi.org/10.1080/09515089.2020.1719054

  • R. Sun, Full human-machine symbiosis and truly intelligent cognitive systems. AI and Society: Journal of Knowledge, Culture and Communication, Vol.35, No.1, pp 17–28. 2020. https://doi.org/10.1007/s00146-017-0775-7

  • S. Bretz and R. Sun, Two models of moral judgment. Cognitive Science, Vol.42, No.S1, pp.4-37. 2018. https://onlinelibrary.wiley.com/doi/pdf/10.1111/cogs.12517

  • R. Sun, Why is a computational framework for motivational and metacognitive control needed? Journal of Experimental and Theoretical Artificial Intelligence, Vol.30, No.1, pp.13-37. 2018. http://www.tandfonline.com/eprint/4GPCrukk7qzn46kWjGZF/full

  • R. Sun, N. Wilson, and M. Lynch, Emotion: A unified mechanistic interpretation from a cognitive architecture. Cognitive Computation, Vol.8, No.1, pp.1-14. 2016.

  • R. Sun, Interpreting psychological notions: A dual-process computational theory. Journal of Applied Research in Memory and Cognition, Vol.4, No.3, pp.191–196. 2015. https://www.sciencedirect.com/science/article/pii/S2211368114000783

  • S. Helie and R. Sun, An integrative account of memory and reasoning phenomena. New Ideas in Psychology. Vol.35, pp.36-52. 2014. [supplemental materials].

  • R. Sun and N. Wilson. Roles of implicit processes: instinct, intuition, and personality. Mind and Society, Vol.13, pp.109–134. 2014

  • R. Sun and N. Wilson, A model of personality should be a cognitive architecture itself. Cognitive Systems Research, Vol.29–30, pp.1–30. 2014.

  • R. Sun, Moral judgment, human motivation, and neural networks. Cognitive Computation, Vol.5, No.4, pp.566-579. 2013.

  • R. Sun, Autonomous generation of symbolic representations through subsymbolic activities. Philosophical Psychology, Vol.26, No.6, pp.888-912. 2013.

  • R. Sun and S. Helie, Psychologically realistic cognitive agents: Taking human cognition seriously. Journal of Experimental and Theoretical Artificial Intelligence, Vol.25, pp.65-92. 2013.

  • R. Sun and P. Fleischer, A cognitive social simulation of tribal survival strategies: The importance of cognitive and motivational factors. Journal of Cognition and Culture, Vol.12, No.3-4, pp.287-321, 2012.

  • R. Sun and R. Mathews, Implicit cognition, emotion, and meta-cognitive control. Mind and Society, the special issue on Dual Processes Theories of Language and Thinking, Vol.11, No.1, pp.107-119. 2012.

  • R. Sun, Memory Systems within a Cognitive Architecture. New Ideas in Psychology, Vol.30, pp.227-240. 2012.

  • R. Sun, N. Wilson, and R. Mathews, Accounting for certain mental disorders within a comprehensive cognitive architecture. Cognitive Computation, Vol.3, No.2, pp.341-359. 2011.

  • S. Helie and R. Sun, Incubation, insight, and creative problem solving: A unified theory and a connectionist model. Psychological Review, Vol.117, No.3, pp.994-1024. 2010.

  • N. Wilson, R. Sun, and R. Mathews, A motivationally-based simulation of performance degradation under pressure . Neural Networks, Vol.22, pp.502-508. 2009.

  • R. Sun, Motivational representations within a computational cognitive architecture . Cognitive Computation, Vol.1, No.1, pp.91-103. 2009.

  • R. Sun, X. Zhang, and R. Mathews, Capturing human data in a letter counting task: Accessibility and action-centeredness in representing cognitive skills . Neural Networks, Vol.22, pp.15-29. 2009.

  • R. Sun and I. Naveh, Social institution, cognition, and survival: A cognitive-social simulation . Mind and Society, Vol.6, No.2, pp.115-142. 2007.

  • R. Sun, R. Mathews, and S. Lane, Implicit and explicit processes in the development of cognitive skills: A theoretical interpretation with some practical implications for science education. In: E. Vargios (ed.), Educational Psychology Research Focus, pp.1-26. Nova Science Publishers, Hauppauge, NY. 2007.

  • R. Sun, X. Zhang, P. Slusarz, and R. Mathews, The interaction of implicit learning, explicit hypothesis testing learning, and implicit-to-explicit knowledge extraction . Neural Networks, Vol.20, No.1, pp.34-47. 2007. [Elsevier formatted PDF]

  • R. Sun, The importance of cognitive architectures: An analysis based on CLARION. Journal of Experimental and Theoretical Artificial Intelligence, Vol.19, No.2, p p.159-193. 2007

  • R. Sun and X. Zhang, Accounting for a variety of reasoning data within a cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence, Vol.18, No.2, pp.169-191. 2006.

  • R. Sun, X. Zhang, and R. Mathews, Modeling meta-cognition in a cognitive architecture. Cognitive Systems Research, Vol.7, No.4, pp.327-338. 2006. [Elsevier formatted PDF]

  • I. Naveh and R. Sun, A cognitively based simulation of academic science . Computational and Mathematical Organization Theory, Vol.12, pp.313-337. 2006.

  • R. Sun, P. Slusarz, and C. Terry, The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychological Review, Vol.112, No.1, pp.159-192. 2005.

  • R. Sun and X. Zhang, Top-down versus bottom-up learning in cognitive skill acquisition. Cognitive Systems Research, Vol.5, No.1, pp.63-89, March 2004. [Elsevier-formatted PDF]

  • R. Sun and I. Naveh, Simulating organizational decision-making using a cognitively realistic agent model. Journal of Artificial Societies and Social Simulation, Vol.7, No.3. 2004. [ http://jasss.soc.surrey.ac.uk/7/3/5.html ]

  • L. A. Coward and R. Sun, Criteria for an effective theory of consciousness and some preliminary attempts. Consciousness and Cognition, Vol.13, pp. 268-301. 2004.

  • R. Sun, E. Merrill, and T. Peterson, From implicit skills to explicit knowledge: a bottom-up model of skill learning Cognitive Science, Vol.25, No.2, pp.203-244. 2001. [Elsevier-formatted PDF]

  • R. Sun, E. Merrill, and T. Peterson, Knowledge acquisition via bottom-up skill learning. Knowledge Engineering: Systems, Techniques and Applications, ed. C. Leondes, Academic Press. 2000.

  • R. Sun, Accounting for the computational basis of consciousness: A connectionist approach. Consciousness and Cognition, Vol.8, pp.529-565. December, 1999.

  • R. Sun, Learning, action, and consciousness: A hybrid approach towards modeling consciousness. Neural Networks, special issue on consciousness. 10 (7), pp.1317-1331. 1997. (an overview)

Conference papers:

• • J. Allen and R. Sun, Emotion contagion in a cognitive architecture. In: Y. Jin and S. Kollias (eds.), Proceedings of IEEE Symposium Series in Computational Intelligence (SSCI 2016). Athens, Greece. December 6-9, 2016. Published by IEEE Press, Piscataway, NJ. 2016.

  • J. Licato, R. Sun, and S. Bringsjord, Structural representation and reasoning in a hybrid cognitive architecture. Proceedings of the 2014 International Joint Conference on Neural Networks. IEEE Press, Piscataway, NJ. 2014.

  • J. Licato, R. Sun, and S. Bringsjord, Using a hybrid cognitive architecture to model children’s errors in an analogy task. Proceedings of the Annual Conference of Cognitive Science Society, Quebec City, Quebec, Canada. July, 2014. pp.857-862. Published by Cognitive Science Society, Austin, Texas. 2014.

  • N. Wilson, R. Sun, and R. Mathews, A motivationally based computational interpretation of social anxiety induced stereotype bias . Proceedings of the Annual Conference of the Cognitive Science Society, pp.1750-1755. Cognitive Science Society, Austin, Texas. 2010.

  • S. Helie and R. Sun, Creative problem solving: A CLARION theory . Proceedings of the 2010 International Joint Conference on Neural Networks, Barcelona, Spain. pp.1460-1466. IEEE Press, Piscataway, NJ. 2010.

  • S. Helie and R. Sun, Simulating incubation effects using the explicit-implicit interaction with Bayes factor (EII-BF) Model . Proceedings of the International Joint Conference on Neural Networks, Atlanta, Georgia, USA. pp.1199-1205. IEEE Press, Piscataway, NJ. 2009.

  • S. Helie and R. Sun, Knowledge integration in creative problem solving. Proceedings of the 2008 Annual Conference of the Cognitive Science Society, Washington, DC. pp.1681-1686. Published by the Cognitive Science Society. July, 2008.

  • S. Helie, R. Sun, and L. Xiong, Mixed effects of distractor tasks on incubation. Proceedings of the 2008 Annual Conference of the Cognitive Science Society, Washington, DC. pp.1251-1256. Published by the Cognitive Science Society. July, 2008.

  • R. Sun and I. Naveh, A cognitively based simulation of simple organizations. Proceedings of the 27th Annual Conference of the Cognitive Science Society, Stresa, Italy. Lawrence Erlbaum Associates, Mahwah, NJ. 2005.

  • R. Sun, X. Zhang, and R. Mathews, Modeling meta-cognition in a cognitive architecture. Proceedings of the 27th Annual Conference of the Cognitive Science Society, Stresa, Italy. Lawrence Erlbaum Associates, Mahwah, NJ. 2005.

  • R. Sun and X. Zhang, Accounting for similarity-based reasoning within a cognitive architecture. Proceedings of the 26th Annual Conference of the Cognitive Science Society, Chicago. Lawrence Erlbaum Associates, Mahwah, NJ. 2004.

  • R. Sun and X. Zhang, Accounting for discovery in a cognitive architecture. Proceedings of the 25th Annual Conference of the Cognitive Science Society, Boston, MA. Lawrence Erlbaum Associates, Mahwah, NJ. 2003.

  • R. Sun and X. Zhang, Accessibility versus action-centeredness in the representation of cognitive skills. Proceedings of the Fifth International Conference on Cognitive Modeling. Bamberg, Germany, April 10-12, 2003 [ PDF]

  • R. Sun and C. Terry, Implicit learning of serial reaction time tasks: Connectionist vs. symbolic models. Proceedings of the 24th Annual Conference of the Cognitive Science Society, Fairfax, VA. Lawrence Erlbaum Associates, Mahwah, NJ. 2002. [ PDF]

  • R. Sun and X. Zhang, Top-down versus bottom-up learning in skill acquisition. Proceedings of the 24th Annual Conference of the Cognitive Science Society, Fairfax, VA. Lawrence Erlbaum Associates, Mahwah, NJ. 2002. [ PDF]

  • P. Slusarz and R. Sun, The interaction of explicit and implicit learning: An integrated model. Proceedings of Cognitive Science Society Conference, Edinburgh, pp.952-957. 2001. [ PDF]

  • R. Sun, E. Merrill, and T. Peterson, A bottom-up model of skill learning. Proc. of the 20th Conference of Cognitive Science Society, August, 1998. pp.1037-1042, Lawrence Erlbaum Associates.

  • R. Sun, E. Merrill, and T. Peterson, Skill learning using a bottom-up hybrid model. Proc. of The Second European Conference on Cognitive Modeling, Nottingham, UK. April, 1998.

For details of the earlier versions, see the following papers:

CLARION 3.0

• • R. Sun and T. Peterson, Autonomous learning of sequential tasks: experiments and analyses. IEEE Transaction on Neural Networks, November, 1998.

  • R. Sun and T. Peterson, A subsymbolic+symbolic model for learning sequential navigation. Proc. of the Fifth International Conference of the Society for Adaptive Behavior (SAB'98). Zurich, Switzerland. 1998. MIT Press.

CLARION 2.0 and 1.0

• • R. Sun, T. Peterson, and E. Merrill, A hybrid architecture for situated learning of reactive sequential decision making. Applied Intelligence, Vol.11, pp.109-127. 1999.

  • R. Sun and T. Peterson, Some experiments with a hybrid model for learning sequential decision making. Information Sciences. Vol.111, pp.83-107.

  • R. Sun and T. Peterson, A hybrid model for learning sequential navigation. Proc. of IEEE International Symposium on Computational Intelligence in Robotics and Automation. Monterey, CA. IEEE Press. 1997.

  • R. Sun, T. Peterson, and E. Merrill, Bottom-up skill learning in reactive sequential decision tasks. Proc.of 18th Cognitive Science Society Conference, Lawrence Erlbaum Associates, Hillsdale, NJ. pp.684-690. 1996.

CLARION-RBF

• • T. Peterson and R. Sun, An RBF network alternative to a hybrid architecture. Proceedings of IEEE International Conference on Neural Networks, Anchorage, Alaska. May 4-9, 1998. IEEE Press, Piscataway, NJ.

Plan extraction

• • R. Sun and C. Sessions, Learning plans without a priori knowledge. Adaptive Behavior, Vol.8, Issue 3/4, 2000. [formatted PDF]

  • R. Sun and C. Sessions, Extracting plans from reinforcement learners. Proceedings of the 1998 International Symposium on Intelligent Data Engineering and Learning, October, 1998. Springer-Verlag.

  • R. Sun and C. Sessions, Learning to plan probabilistically from neural networks. Proceedings of IEEE International Conference on Neural Networks, Anchorage, Alaska. May 4-9, 1998. IEEE Press, Piscataway, NJ.

Partitioning (modular reinforcement learning)

• • R. Sun and T. Peterson, Multi-agent reinforcement learning: weighting and partitioning. Neural Networks, 1999. [Elsevier-formatted PDF]

  • R. Sun and T. Peterson, Automatic partitioning for multi-agent reinforcement learning. From Animals to Animats: Proceedings of the International Conference of Simulation of Adaptive Behavior (SAB'2000). Paris, France. MIT Press, Cambridge, MA. 2000.

Segmentation

• • R. Sun and C. Sessions, Self-segmentation of sequences: automatic formation of hierarchies of sequential behaviors. IEEE Transactions on Systems, Man, and Cybernetics: Part B Cybernetics, in press.

  • R. Sun and C. Sessions, Self segmentation of sequences. Proceedings of International Joint Conference on Neural Networks, Washington, DC. July 10-15, 1999. IEEE Press, Piscataway, NJ.

Additional Publications

• • For additional information on Clarion, see the Clarion project page.

  • For other publications, see Prof. Ron Sun's home page.

  • See also the consciousness page.

  • See also the theoretical work on computational cognitive modeling page.