Motivational Processes Within the Perception–Action Cycle

  • Ron Sun
  • Nick Wilson
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS)


The present chapter discusses psychologically well-justified models of motivational processes within the human perception–action cycle (in particular, the CLARION cognitive architecture). First, some background relevant to studying and modeling motivational processes, structures, and representations is discussed. Then, the CLARION cognitive architecture is described. Some simulation results of human motivation and personality from CLARION are then briefly reviewed, and their implications and the future directions outlined.


Human Motivation Personality Type Bottom Level Implicit Knowledge Cognitive Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been supported in part by ONR grant N00014-08-1-0068 (to Ron Sun), as well as by ARI contract W74V8H-05-K-0002 (to Ron Sun and Robert Mathews). Thanks to all the reviewers who provided comments on an earlier version.


  1. Anderson, J. R. (1993). Rules of the Mind. Lawrence Erlbaum Associates, Hillsdale, NJ.Google Scholar
  2. Anderson, J. and C. Lebiere (1998). The Atomic Components of Thought. Lawrence Erlbaum Associates, Mahwah, NJ.Google Scholar
  3. Bach, J. (2009). Principles of Synthetic Intelligence. Oxford University Press, New York.Google Scholar
  4. Beilock, S., C. Kulp, L. Holt, and T. Carr (2004). More on the fragility of performance: Choking under pressure in mathematical problem solving. Journal of Experimental Psychology, 133, 584–600.PubMedGoogle Scholar
  5. Beilock, S. and T. Carr (2001). On the fragility of skilled performance: What governs choking under pressure? Journal of Experimental Psychology, 130, 701–725.PubMedGoogle Scholar
  6. Caprara, G. V. and D. Cervone (2000). Personality: Determinants, Dynamics, and Potentials. Cambridge University Press, New York.Google Scholar
  7. Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology, 41, 417–440.Google Scholar
  8. Doerner, D. (2003). The mathematics of emotions. In: Frank Detje, D. D. and Schaub, H. (eds.), Proceedings of the Fifth International Conference on Cognitive Modeling, Bamberg, Germany, pp. 75–79.Google Scholar
  9. Fuster, J. M. (2002). Physiology of the executive functions: The perception-action cycle. In: Stuss, D. T. and Knight, R. T. (eds.), Principles of Frontal Lobe Function. Oxford University Press, Oxford, pp. 96–108.CrossRefGoogle Scholar
  10. Hull, C. (1943). Principles of Behavior: An Introduction to Behavior Theory. D. Appleton-Century Company, New York.Google Scholar
  11. James, W. (1890). The Principles of Psychology. Dover, New York.Google Scholar
  12. Lambert, A., B. Payne, L. Jacoby, L. Shaffer, A. Chasteen, and S. Khan (2003). Stereotypes and dominant responses: On the “social facilitation” of prejudice in anticipated public contexts. Journal of Personality and Social Psychology, 84, 277–295.CrossRefPubMedGoogle Scholar
  13. Maner, J. K., D. T. Kenrick, S. L. Neuberg, D. V. Becker, T. Robertson, B. Hofer, A. Delton, J. Butner, and M. Schaller (2005). Functional projection: How fundamental social motives can bias interpersonal perception. Journal of Personality and Social Psychology, 88, 63–78.CrossRefPubMedGoogle Scholar
  14. Maslow, A. (1943). A theory of human motivation. Psychological Review, 50, 370–396.CrossRefGoogle Scholar
  15. Mazzoni, G. and Nelson, T. (eds.), (1998). Metacognition and Cognitive Neuropsychology. Erlbaum, Mahwah, NJ.Google Scholar
  16. McClelland, D. (1951). Personality. Dryden, New York.CrossRefGoogle Scholar
  17. McDougall, W. (1936). An Introduction to Social Psychology. Methuen & Co., London.Google Scholar
  18. McFarland, D. (1989). Problems of Animal Behaviour. Longman Publishing, Singapore.Google Scholar
  19. Moskowitz, D. S., E. J. Suh, and J. Desaulniers (1994). Situational influences on gender differences in agency and communion. Journal of Personality and Social Psychology, 66, 753–761.CrossRefPubMedGoogle Scholar
  20. Murray, H. (1938). Explorations in Personality. Oxford University Press, New York.Google Scholar
  21. Newell, A. (1990). Unified Theories of Cognition. Harvard University Press, Cambridge, MA.Google Scholar
  22. Poznanski, M. and P. Thagard (2005). Changing personalities: towards realistic virtual characters. Journal of Experimental and Theoretical Artificial Intelligence, 17, 221–241.CrossRefGoogle Scholar
  23. Quek, M. and D. S. Moskowitz (2007). Testing neural network models of personality. Journal of Research in Personality, 41, 700–706.CrossRefGoogle Scholar
  24. Read, S. J. and L. C. Miller (2002). Virtual Personalities: A Neural Network Model of Personality. Personality and Social Psychology Review, 6, 357–369.CrossRefGoogle Scholar
  25. Reiss, S. (2004). Multifaceted nature of intrinsic motivation: The theory of 16 basic desires. Review of General Psychology, 8(3), 179–193.CrossRefGoogle Scholar
  26. Rosenbloom, P., J. Laird, and A. Newell (1993). The SOAR Papers: Research on Integrated Intelligence. MIT, Cambridge, MA.Google Scholar
  27. Schwartz, S. (1994). Are there universal aspects of human values? Journal of Social Issues, 50, 19–45.CrossRefGoogle Scholar
  28. Shoda, Y. and W. Mischel (1998). Personality as a stable cognitive–affective activation network: Characteristic patterns of behavior variation emerge from a stable personality structure. In Read, S. J. and Miller, L. C. (eds.), Connectionist models of social reasoning and social behavior. Lawrence Erlbaum Associates, Inc., Mahwah, NJ, pp. 175–208.Google Scholar
  29. Simon, H. A. (1967). Motivational and emotional controls of cognition. Psychological Review, 74, 29–39.CrossRefPubMedGoogle Scholar
  30. Sloman, A. (1987). Motives, mechanisms and emotions. Emotion and Cognition, 1, 217–234.CrossRefGoogle Scholar
  31. Sun, R. (1994). Integrating Rules and Connectionism for Robust Commonsense Reasoning. New York. John Wiley and Sons.Google Scholar
  32. Sun, R. (2002). Duality of the Mind: A Bottom-up Approach Toward Cognition. Lawrence Erlbaum Associates, Mahwah, NJ.Google Scholar
  33. Sun, R. (2003). A Tutorial on CLARION 5.0. Technical Report, Cognitive Science Department, Rensselaer Polytechnic Institute.
  34. Sun, R. (2009). Motivational representations within a computational cognitive architecture. Cognitive Computation, 1(1), 91–103.CrossRefGoogle Scholar
  35. Sun, R., E. Merrill, and T. Peterson (2001). From implicit skills to explicit knowledge: A bottom-up model of skill learning. Cognitive Science, 25, 203–244.CrossRefGoogle Scholar
  36. Sun, R., Slusarz, P. and C. Terry (2005). The interaction of the explicit and the implicit in skill learning: A dual-process approach. Psychological Review, 112, 159–192.CrossRefPubMedGoogle Scholar
  37. Sun, R. and N. Wilson (2009). A computational personality model within a comprehensive cognitive architecture. Technical Report, Cognitive Science Department, Rensselaer Polytechnic Institute.Google Scholar
  38. Sun, R. and X. Zhang (2004). Top-down versus bottom-up learning in cognitive skill acquisition. Cognitive Systems Research, 5(1), 63–89.CrossRefGoogle Scholar
  39. Toates, F. (1986). Motivational Systems. Cambridge University Press, Cambridge, UK.Google Scholar
  40. Tolman, E.C. (1932). Purposive Behavior in Animals and Men. Century, New York.Google Scholar
  41. Tyrell, T. (1993). Computational Mechanisms for Action Selection. Ph.D Thesis, Oxford University, Oxford, UK.Google Scholar
  42. Watkins, C. (1989). Learning with Delayed Rewards. Ph.D Thesis, Cambridge University, Cambridge, UK.Google Scholar
  43. Weiner, B. (1992). Human Motivation: Metaphors, Theories, and Research. Sage, Newbury Park, CA.Google Scholar
  44. Wilson, N., R. Sun, and R. Mathews (2009). A motivationally-based simulation of performance degradation under pressure. Neural Network, 22, 502–508.CrossRefGoogle Scholar
  45. Wright, I. P., and A. Sloman (1997). MINDER1: An Implementation of a Proto-emotional Agent Architecture. Technical Report CSRP-97-1, University of Birmingham, School of Computer Science. (Available from

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Cognitive Science DepartmentRensselaer Polytechnic InstituteTroyUSA

Personalised recommendations