Abstract
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.
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References
Abbass H, Scholz J, Reid D (eds) (2017) Foundations of trusted autonomy. Springer, Berlin
Baldassarre G, Mirolli M (2013) Intrinsically motivated learning in natural and artificial systems. Springer, Berlin
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):584–600
Bretz S, Sun R (2017) Two models of moral judgment. Cogn Sci. https://doi.org/10.1111/cogs.12517
Carver C, Scheier M (1998) On the self-regulation of behavior. Cambridge University Press, Cambridge
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 399–423
Damasio A (1994) Descartes’ error: emotion, reason and the human brain. Grosset/Putnam, New York
Frijda N (1986) The emotions. Cambridge University Press, New York
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 York
Heidegger M (1927/1962) Being and time. English translation published by Harper and Row, New York
Helie S, Sun R (2010) Incubation, insight, and creative problem solving: a unified theory and a connectionist model. Psychol Rev 117(3):994–1024
Helie S, Sun R (2014) An integrative account of memory and reasoning phenomena. New Ideas Psychol 35:36–52
Hume D (1765/1993). An enquiry concerning human understanding. Hacket Publishing Co., Indianapolis
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):277–295
Licklider JCR (1960). Man-computer symbiosis. IRE Trans Hum Factors Electron HFE-1:4–11
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 York
Merrick E, Maher ML (2009) Motivated reinforcement learning. Springer, Berlin
Moskowitz DS, Suh EJ, Desaulniers J (1994) Situational influences on gender differences in agency and communion. J Pers Soc Psychol 66:753–761
Murray H (1938) Explorations in personality. Oxford University Press, New York
Nagel T (1974) What is it like to be a bat? Philos Rev 83(4):435–450
Newell A (1990) Unified theories of cognition. Harvard University Press, Cambridge
Ortony A, Clore G, Collins A (1988) The cognitive structure of emotions. Cambridge University Press, Cambridge
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 117:61–92
Reber AS (1989) Implicit learning and tacit knowledge. J Exp Psychol 118(3):219–235
Reder LM (1996) Implicit memory and metacognition. Erlbaum, Mahwah
Reiss S (2004) Multifaceted nature of intrinsic motivation: the theory of 16 basic desires. Rev Gen Psychol 8(3):179–193
Rumelhart DE, McClelland JL, PDP Research Group (1986) Parallel distributed processing. MIT Press, Cambridge
Smillie LD, Pickering AD, Jackson CJ (2006) The new reinforcement sensitivity theory: implications for personality measurement. Personal Soc Psychol Rev 10:320–335
Sun R (2002) Duality of the mind. Lawrence Erlbaum Associates, Mahwah
Sun R (ed) (2006) Cognition and multi-agent interaction: from cognitive modeling to social simulation. Cambridge University Press, New York
Sun R (2007) The importance of cognitive architectures: an analysis based on CLARION. J Exp Theor Artif Intell 19(2):159–193
Sun R (ed) (2008) The Cambridge handbook of computational psychology. Cambridge University Press, New York
Sun R (2009) Motivational representations within a computational cognitive architecture. Cogn Comput 1(1):91–103
Sun R (2016) Anatomy of the mind: exploring psychological mechanisms and processes with the Clarion cognitive architecture. Oxford University Press, New York
Sun R (2018) Intrinsic motivation for truly autonomous agents. In: Abbass H, Scholz J, Reid D (eds) Foundations of trusted autonomy. Springer, Berlin
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):287–321
Sun R, Helie S (2013) Psychologically realistic cognitive agents: taking human cognition seriously. J Exp Theor Artif Intell 25:65–92
Sun R, Wilson N, (2014). A model of personality should be a cognitive architecture itself. Cogn Syst Res 29–30:1–30
Sun R, Zhang X (2006) Accounting for a variety of reasoning data within a cognitive architecture. J Exp Theor Artif Intell 18(2):169–191
Sun R, Merrill E, Peterson T (2001) From implicit skills to explicit knowledge: a bottom–up model of skill learning. Cogn Sci 25(2):203–244
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):159–192
Sun R, Zhang X, Mathews R (2006) Modeling meta-cognition in a cognitive architecture. Cogn Syst Res 7(4):327–338
Sun R, Wilson N, Lynch M (2016) Emotion: a unified mechanistic interpretation from a cognitive architecture. Cogn Comput 8(1):1–14
Tolman EC (1932) Purposive behavior in animals and men. Century, New York
Tyrell T (1993) Computational mechanisms for action selection. Ph.D. Thesis, Oxford University, Oxford, UK
Watkins C (1989) Learning with delayed rewards. Ph.D. Thesis, Cambridge University, Cambridge, UK
Wilson N, Sun R, Mathews R (2009) A motivationally-based simulation of performance degradation under pressure. Neural Netw 22:502–508
Acknowledgements
This work was supported in part by the ARI Grant W911NF-17-1-0236. Thanks are due to the reviewers, who provided useful suggestions.
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Sun, R. Potential of full human–machine symbiosis through truly intelligent cognitive systems. AI & Soc 35, 17–28 (2020). https://doi.org/10.1007/s00146-017-0775-7
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DOI: https://doi.org/10.1007/s00146-017-0775-7