pp 1–12 | Cite as

Potential of full human–machine symbiosis through truly intelligent cognitive systems

Original Article


It is highly likely that, to achieve full human–machine symbiosis, truly intelligent cognitive systems—human-like (or even beyond)—may have to be developed first. Such systems should not only be capable of performing human-like thinking, reasoning, and problem solving, but also be capable of displaying human-like motivation, emotion, and personality. In this opinion article, I will argue that such systems are indeed possible and needed to achieve true and full symbiosis with humans. A computational cognitive architecture (named Clarion) is used in this article to illustrate, in a preliminary way, what can be achieved in this regard. It is shown that Clarion involves complex structures, representations, and mechanisms, and is capable of capturing human cognitive performance (including skills, reasoning, memory, and so on) as well as human motivation, emotion, personality, and other relevant aspects. It is further argued that the cognitive architecture can enable and facilitate true human–machine symbiosis.


Cognitive architecture Emotion Motivation Personality Symbiosis 



This work was supported in part by the ARI Grant W911NF-17-1-0236. Thanks are due to the reviewers, who provided useful suggestions.


  1. Abbass H, Scholz J, Reid D (eds) (2017) Foundations of trusted autonomy. Springer, BerlinGoogle Scholar
  2. Baldassarre G, Mirolli M (2013) Intrinsically motivated learning in natural and artificial systems. Springer, BerlinCrossRefGoogle Scholar
  3. Beilock S, Kulp C, Holt L, Carr T (2004) More on the fragility of performance: choking under pressure in mathematical problem solving. J Exp Psychol Gen 133(4):584–600CrossRefGoogle Scholar
  4. Bretz S, Sun R (2017) Two models of moral judgment. Cogn Sci. Google Scholar
  5. Carver C, Scheier M (1998) On the self-regulation of behavior. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  6. Clark LA, Watson D (1999) Temperament: a new paradigm for trait psychology. In: Pervin LA, John OP (eds) Handbook of personality: theory and research, 2nd edn. Guilford Press, New York, pp 399–423Google Scholar
  7. Damasio A (1994) Descartes’ error: emotion, reason and the human brain. Grosset/Putnam, New YorkGoogle Scholar
  8. Frijda N (1986) The emotions. Cambridge University Press, New YorkGoogle Scholar
  9. Gray JA, McNaughton N (2000) The neuropsychology of anxiety: an enquiry into the functions of the septo-hippocampal system, 2nd edn. Oxford University Press, New YorkGoogle Scholar
  10. Heidegger M (1927/1962) Being and time. English translation published by Harper and Row, New YorkGoogle Scholar
  11. Helie S, Sun R (2010) Incubation, insight, and creative problem solving: a unified theory and a connectionist model. Psychol Rev 117(3):994–1024CrossRefGoogle Scholar
  12. Helie S, Sun R (2014) An integrative account of memory and reasoning phenomena. New Ideas Psychol 35:36–52CrossRefGoogle Scholar
  13. Hume D (1765/1993). An enquiry concerning human understanding. Hacket Publishing Co., IndianapolisGoogle Scholar
  14. Lambert A, Payne B, Jacoby L, Shaffer L, Chasteen A, Khan S (2003) Stereotypes as dominant responses: on the “social facilitation” of prejudice in anticipated public contexts. J Pers Soc Psychol 84(2):277–295CrossRefGoogle Scholar
  15. Licklider JCR (1960). Man-computer symbiosis. IRE Trans Hum Factors Electron HFE-1:4–11CrossRefGoogle Scholar
  16. Mekik CS, Sun R, Dai DY. (2017). Deep learning of Raven’s matrices. In: P. Bello (ed.), Proceedings of the fifth annual conference on advances in cognitive systems (ACS 2017), Troy, New YorkGoogle Scholar
  17. Merrick E, Maher ML (2009) Motivated reinforcement learning. Springer, BerlinCrossRefGoogle Scholar
  18. Moskowitz DS, Suh EJ, Desaulniers J (1994) Situational influences on gender differences in agency and communion. J Pers Soc Psychol 66:753–761CrossRefGoogle Scholar
  19. Murray H (1938) Explorations in personality. Oxford University Press, New YorkGoogle Scholar
  20. Nagel T (1974) What is it like to be a bat? Philos Rev 83(4):435–450CrossRefGoogle Scholar
  21. Newell A (1990) Unified theories of cognition. Harvard University Press, CambridgeGoogle Scholar
  22. Ortony A, Clore G, Collins A (1988) The cognitive structure of emotions. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  23. Read SJ, Monroe BM, Brownstein AL, Yang Y, Chopra G, Miller LC (2010) Virtual personalities II: a neural network model of the structure and dynamics of human personality. Psychol Rev 117:61–92CrossRefGoogle Scholar
  24. Reber AS (1989) Implicit learning and tacit knowledge. J Exp Psychol 118(3):219–235CrossRefGoogle Scholar
  25. Reder LM (1996) Implicit memory and metacognition. Erlbaum, MahwahGoogle Scholar
  26. Reiss S (2004) Multifaceted nature of intrinsic motivation: the theory of 16 basic desires. Rev Gen Psychol 8(3):179–193CrossRefGoogle Scholar
  27. Rumelhart DE, McClelland JL, PDP Research Group (1986) Parallel distributed processing. MIT Press, CambridgeGoogle Scholar
  28. Smillie LD, Pickering AD, Jackson CJ (2006) The new reinforcement sensitivity theory: implications for personality measurement. Personal Soc Psychol Rev 10:320–335CrossRefGoogle Scholar
  29. Sun R (2002) Duality of the mind. Lawrence Erlbaum Associates, MahwahGoogle Scholar
  30. Sun R (ed) (2006) Cognition and multi-agent interaction: from cognitive modeling to social simulation. Cambridge University Press, New YorkGoogle Scholar
  31. Sun R (2007) The importance of cognitive architectures: an analysis based on CLARION. J Exp Theor Artif Intell 19(2):159–193CrossRefGoogle Scholar
  32. Sun R (ed) (2008) The Cambridge handbook of computational psychology. Cambridge University Press, New YorkGoogle Scholar
  33. Sun R (2009) Motivational representations within a computational cognitive architecture. Cogn Comput 1(1):91–103CrossRefGoogle Scholar
  34. Sun R (2016) Anatomy of the mind: exploring psychological mechanisms and processes with the Clarion cognitive architecture. Oxford University Press, New YorkCrossRefGoogle Scholar
  35. Sun R (2018) Intrinsic motivation for truly autonomous agents. In: Abbass H, Scholz J, Reid D (eds) Foundations of trusted autonomy. Springer, BerlinGoogle Scholar
  36. Sun R, Fleischer P (2012) A cognitive social simulation of tribal survival strategies: The importance of cognitive and motivational factors. J Cogn C 12(3–4):287–321CrossRefGoogle Scholar
  37. Sun R, Helie S (2013) Psychologically realistic cognitive agents: taking human cognition seriously. J Exp Theor Artif Intell 25:65–92CrossRefGoogle Scholar
  38. Sun R, Wilson N, (2014). A model of personality should be a cognitive architecture itself. Cogn Syst Res 29–30:1–30CrossRefGoogle Scholar
  39. Sun R, Zhang X (2006) Accounting for a variety of reasoning data within a cognitive architecture. J Exp Theor Artif Intell 18(2):169–191CrossRefGoogle Scholar
  40. Sun R, Merrill E, Peterson T (2001) From implicit skills to explicit knowledge: a bottom–up model of skill learning. Cogn Sci 25(2):203–244CrossRefGoogle Scholar
  41. Sun R, Slusarz P, Terry C (2005) The interaction of the explicit and the implicit in skill learning: a dual-process approach. Psychol Rev 112(1):159–192CrossRefGoogle Scholar
  42. Sun R, Zhang X, Mathews R (2006) Modeling meta-cognition in a cognitive architecture. Cogn Syst Res 7(4):327–338CrossRefGoogle Scholar
  43. Sun R, Wilson N, Lynch M (2016) Emotion: a unified mechanistic interpretation from a cognitive architecture. Cogn Comput 8(1):1–14CrossRefGoogle Scholar
  44. Tolman EC (1932) Purposive behavior in animals and men. Century, New YorkGoogle Scholar
  45. Tyrell T (1993) Computational mechanisms for action selection. Ph.D. Thesis, Oxford University, Oxford, UKGoogle Scholar
  46. Watkins C (1989) Learning with delayed rewards. Ph.D. Thesis, Cambridge University, Cambridge, UKGoogle Scholar
  47. Wilson N, Sun R, Mathews R (2009) A motivationally-based simulation of performance degradation under pressure. Neural Netw 22:502–508CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Cognitive ScienceRensselaer Polytechnic InstituteTroyUSA

Personalised recommendations