A Closed-Loop Perspective on Symbiotic Human-Computer Interaction

  • Stephen FaircloughEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9359)


This paper is concerned with how people interact with an emergent form of technology that is capable of both monitoring and affecting the psychology and behaviour of the user. The current relationship between people and computer is characterised as asymmetrical and static. The closed-loop dynamic of physiological computing systems is used as an example of a symmetrical and symbiotic HCI, where the central nervous system of the user and an adaptive software controller are engaged in constant dialogue. This emergent technology offers several benefits such as: intelligent adaptation, a capacity to learn and an ability to personalise software to the individual. This paper argues that such benefits can only be obtained at the cost of a strategic reconfiguration of the relationship between people and technology - specifically users must cede a degree of control over their interaction with technology in order to create an interaction that is active, dynamic and capable of responding in a stochastic fashion. The capacity of the system to successfully translate human goals and values into adaptive responses that are appropriate and effective at the interface represents a particular challenge. It is concluded that technology can develop lifelike qualities (e.g. complexity, sentience, freedom) through sustained and symbiotic interaction with human beings. However, there are a number of risks associated with this strategy as interaction with this category of technology can subvert skills, self-knowledge and the autonomy of human user.


Symbiosis Physiological computing Intelligent adaptation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Norman, D.A.: The Design of Future Things. Basic Books, New York (2007)Google Scholar
  2. 2.
    Hettinger, L.J., et al.: Neuroadaptive technologies: applying neuroergonomics to the design of advanced interfaces. Theoretical Issues in Ergonomic Science 4(1–2), 220–237 (2003)CrossRefGoogle Scholar
  3. 3.
    Martin, B.D., Schwab, E.: Current usage of symbiosis and associated terminology. International Journal of Biology 5(1), 32–42 (2013)Google Scholar
  4. 4.
    Arthur, W.B.: The Nature of Technology: What It Is and How It Evolves. Penguin (2009)Google Scholar
  5. 5.
    Kelly, K.: What Technology Wants. Penguin (2010)Google Scholar
  6. 6.
    Hancock, P.A.: Mind, Machine and Morality: Towards a Philosophy of Human-Technology Symbiosis. Ashgate (2009)Google Scholar
  7. 7.
    Clark, A., Chalmers, D.J.: The extended mind. Analysis 58, 10–28 (1998)CrossRefGoogle Scholar
  8. 8.
    Clark, A.: Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence. Oxford University Press (2003)Google Scholar
  9. 9.
    Douglas, A.E.: The Symbiotic Habit. Princeton University Press, New Jersey (2010)Google Scholar
  10. 10.
    Licklider, J.C.R.: Man-Computer Symbiosis. IRE Transactions on Human Factors in Electronics HFE-1, 4–11 (1960)CrossRefGoogle Scholar
  11. 11.
    Jacucci, G., Spagnolli, A., Freeman, J., Gamberini, L.: Symbiotic interaction: a critical definition and comparison to other human-computer paradigms. In: Jacucci, G., Gamberini, L., Freeman, J., Spagnolli, A. (eds.) Symbiotic 2014. LNCS, vol. 8820, pp. 3–20. Springer, Heidelberg (2014)Google Scholar
  12. 12.
    Allanson, J., Fairclough, S.H.: A research agenda for physiological computing. Interacting with Computers 16, 857–878 (2004)CrossRefGoogle Scholar
  13. 13.
    Fairclough, S.H.: Fundamentals of Physiological Computing. Interacting with Computers 21, 133–145 (2009)CrossRefGoogle Scholar
  14. 14.
    Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology 40, 187–195 (1995)CrossRefGoogle Scholar
  15. 15.
    Byrne, E., Parasuraman, R.: Psychophysiology and adaptive automation. Biological Psychology 42, 249–268 (1996)CrossRefGoogle Scholar
  16. 16.
    Fairclough, S.H., Gilleade, K.: Construction of the biocybernetic loop: a case study. In: 14th ACM International Conference on Multimodal Interaction, pp. 571–578. ACM, Santa Monica (2012)Google Scholar
  17. 17.
    Haynes, N.K.: How We Became Post Human: Virtual Bodies in Cybernetics, Literature and Informatics. University of Chicago Press (1999)Google Scholar
  18. 18.
    Wiener, N.: The Human Use Of Human Beings: Cybernetics & Society. Da Capo Press, Boston (1954)Google Scholar
  19. 19.
    Wilson, E.O.: Biophilia. Harvard University Press, Cambridge (1984)Google Scholar
  20. 20.
    Wilson, G.F.: Pilot workload, operator functional state and adaptive aiding. In: Hockey, G.R.J., Gaillard, A.W.K., Burov, O. (eds.) Operator Functional State: The Assessment and Prediction of Human Performance Degradation in Complex Tasks, pp. 194–203. ISO Press, Amsterdam (2003)Google Scholar
  21. 21.
    Kapoor, A., Burleson, W., Picard, R.W.: Automatic prediction of frustration. International Journal of Human-Computer Studies 65, 724–736 (2007)CrossRefGoogle Scholar
  22. 22.
    Dekker, A., Champion, E.: Please biofeed the zombies: enhancing the gameplay and display of a horror game using biofeedback. In: DiGRA (2007)Google Scholar
  23. 23.
    Mishra, J., Gazzaley, A.: Closed-loop cognition: the next frontier arrives. Trends Cogn. Sci. 19(5), 242–243 (2015)CrossRefGoogle Scholar
  24. 24.
    Illich, I.: Tools For Conviviality. Harper and Row, New York (1973)Google Scholar
  25. 25.
    Serbedzija, N., Fairclough, S.H.: Reflective pervasive systems. ACM Transactions on Autonomous and Adaptive Systems 7(1) (2012)Google Scholar
  26. 26.
    Serbedzija, N., Fairclough, S.H.: Biocybernetic loops: from awareness to evolution. In: IEEE Congress on Evolutionary Computation, Trondheim, Norway (2009)Google Scholar
  27. 27.
    Wiener, N.: God and Golem Inc. MIT Press, Cambridge (1964)Google Scholar
  28. 28.
    Tomarken, A.J.: A psychometric perspective on psychophysiological measures. Psychological Assessment 7(3), 387–395 (1995)CrossRefGoogle Scholar
  29. 29.
    Rahimi, M., Hancock, P.A.: Optimization of hybrid production systems: the integration of robots into human-occupied work environments. In: Brown, O., Hendricks, H.W. (eds.) Human Factors in Organizational Design and Management – II, pp. 39–54. Elsevier, North-Holland (1986)Google Scholar
  30. 30.
    Fairclough, S.H.: Physiological data should remain confidential. Nature 505, 263 (2014)CrossRefGoogle Scholar
  31. 31.
    Parasuraman, R., Riley, V.: Humans and automation: use, misuse, disuse, abuse. Human Factors 39, 230–253 (1997)CrossRefGoogle Scholar
  32. 32.
    Miller, C.A.: Trust in adaptive automation: the role of etiquette in tuning trust via analogic and affective methods. In: First International Conference on Augmented Cognition, Las Vegas, NV (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Liverpool John Moores UniversityLiverpoolUK

Personalised recommendations