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.
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.)
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:
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:
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.
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.
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.
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.
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.
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.
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.