Advertisement

Non-ordinary Consciousness for Artificial Intelligence

  • Gabriel Axel Montes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10384)

Abstract

Humans are active agents in the design of artificial intelligence (AI), and our input into its development is critical. A case is made for recognizing the importance of including non-ordinary functional capacities of human consciousness in the development of synthetic life, in order for the latter to capture a wider range in the spectrum of neurobiological capabilities. These capacities can be revealed by studying self-cultivation practices designed by humans since prehistoric times for developing non-ordinary functionalities of consciousness. A neurophenomenological praxis is proposed as a model for self-cultivation by an agent in an entropic world. It is proposed that this approach will promote a more complete self-understanding in humans and enable a more thoroughly mutually-beneficial relationship between in life in vivo and in silico.

Keywords

Artificial intelligence Cognition Biomimetics Neuroscience Neurophenomenology Consciousness Philosophy Robotics Metacognition Mindfulness Mind-body Evolution Psychology Medicine Anthropology 

References

  1. 1.
    McGinn, C.: Prehension: The Hand and the Emergence of Humanity. MIT Press, Massachusetts (2015)Google Scholar
  2. 2.
    Bostrom, N.: How long before superintelligence? Int. J. Future Stud. 2 (1998)Google Scholar
  3. 3.
    Bostrom, N.: Superintelligence: Paths, Dangers, Strategies. Oxford University Press, Oxford (2014)Google Scholar
  4. 4.
    Tani, J.: Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena. Oxford University Press, Oxford (2016)CrossRefGoogle Scholar
  5. 5.
    Hawkins, J., Blakeslee, S.: On Intelligence. Henry Holt and Company, New York (2007)Google Scholar
  6. 6.
    Bowler, P.J.: Evolution: The History of an Idea. University of California Press, Berkeley (2003)Google Scholar
  7. 7.
    Carhart-Harris, R.L., Leech, R., Hellyer, P.J., et al.: The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs (2014)Google Scholar
  8. 8.
    Carhart-Harris, R.L., Erritzoe, D., Williams, T., et al.: Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin. Proc. Natl. Acad. Sci. 109, 2138–2143 (2012)CrossRefGoogle Scholar
  9. 9.
    Vollenweider, F.X., Kometer, M.: The neurobiology of psychedelic drugs: implications for the treatment of mood disorders. Nat. Rev. Neurosci. 11, 642–651 (2010)CrossRefGoogle Scholar
  10. 10.
    Grof, S.: LSD Psychotherapy. Multidisciplinary Association for Psychedelic Studies (M A P S), Santa Cruz (2001)Google Scholar
  11. 11.
    Tang, Y.-Y., Holzel, B.K., Posner, M.I.: The neuroscience of mindfulness meditation. Nat. Rev. Neurosci. 16, 213–225 (2015). doi: 10.1038/nrn3916 CrossRefGoogle Scholar
  12. 12.
    Zeidan, F., Martucci, K.T., Kraft, R.A., Gordon, N.S., McHaffie, J.G., Coghill, R.C.: Brain mechanisms supporting the modulation of pain by mindfulness meditation. J. Neurosci. 31, 5540–5548 (2011). doi: 10.1523/JNEUROSCI.5791-10.2011 CrossRefGoogle Scholar
  13. 13.
    Allen, M., Dietz, M., Blair, K.S., et al.: Cognitive-affective neural plasticity following active-controlled mindfulness intervention. J. Neurosci. 32, 15601–15610 (2012)CrossRefGoogle Scholar
  14. 14.
    Maturana, H.R., Varela, F.J.: Autopoiesis and Cognition: The Realization of the Living. Springer, Netherlands (1991)Google Scholar
  15. 15.
    Varela, F.J., Rosch, E., Thompson, E.: The Embodied Mind: Cognitive Science and Human Experience. MIT Press, Massachusetts (1992)Google Scholar
  16. 16.
    O’Regan, J.K., Noë, A.: A sensorimotor account of vision and visual consciousness. Behav. Brain Sci. 24, 939–973 (2001)CrossRefGoogle Scholar
  17. 17.
    Verschure, P.F., Voegtlin, T., Douglas, R.J.: Environmentally mediated synergy between perception and behaviour in mobile robots. Nature 425, 620 (2003). doi: 10.1038/nature02024 CrossRefGoogle Scholar
  18. 18.
    Verschure, P.F.M.J.: Synthetic consciousness: the distributed adaptive control perspective. Philos. Trans. R. Soc. B Biol. Sci. (2016). doi: 10.1098/rstb.2015.0448 Google Scholar
  19. 19.
    Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010). doi: 10.1038/nrn2787 CrossRefGoogle Scholar
  20. 20.
    Clark, A.: Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181–204 (2013)CrossRefGoogle Scholar
  21. 21.
    Schjoedt, U., Andersen, M.: How does religious experience work in predictive minds? Relig. Brain Behav. 1–4 (2017). doi: 10.1080/2153599X.2016.1249913
  22. 22.
    Tononi, G.: The integrated information theory of consciousness: an updated account. Arch. Ital. Biol. 150, 56–90 (2011)Google Scholar
  23. 23.
    Tononi, G.: Consciousness as integrated information: a provisional manifesto. Biol. Bull. (2016)Google Scholar
  24. 24.
    Hohwy, J.: Attention and conscious perception in the hypothesis testing brain. Front. Psychol. 3, 96 (2012). doi: 10.3389/fpsyg.2012.00096 CrossRefGoogle Scholar
  25. 25.
    Jeannerod, M.: Neural simulation of action: a unifying mechanism for motor cognition. NeuroImage 14, S103–S109 (2001). doi: 10.1006/nimg.2001.0832 CrossRefGoogle Scholar
  26. 26.
    Hölzel, B.K., Lazar, S.W., Gard, T., et al.: How does mindfulness meditation work? Proposing mechanisms of action from a conceptual and neural perspective. Perspect. Psychol. Sci. 6, 537–559 (2011). doi: 10.1177/1745691611419671 CrossRefGoogle Scholar
  27. 27.
    Delevoye-Turrell, Y., Bobineau, C.: Motor consciousness during intention-based and stimulus-based actions: modulating attention resources through mindfulness meditation. Front. Psychol. 3, 290 (2012). doi: 10.3389/fpsyg.2012.00290 CrossRefGoogle Scholar
  28. 28.
    Pearson, J., Naselaris, T., Holmes, E.A., Kosslyn, S.M.: Mental imagery: functional mechanisms and clinical applications. Trends Cogn. Sci. 19, 590–602 (2015). doi: 10.1016/j.tics.2015.08.003 CrossRefGoogle Scholar
  29. 29.
    Spiers, H.J., Cothi, W., Bendor, D.: Manipulating hippocampus-dependent memories: to enhance, delete or incept? In: Hannula, D.E., Duff, M.C. (eds.) The Hippocampus from Cells to Systems: Structure, Connectivity, and Functional Contributions to Memory and Flexible Cognition, pp. 123–137. Springer, Cham (2017). doi: 10.1007/978-3-319-50406-3_5 CrossRefGoogle Scholar
  30. 30.
    Kumaran, D., Hassabis, D., McClelland, J.L.: What learning systems do intelligent agents need? Complementary learning systems theory updated. Trends Cogn. Sci. 20, 512–534 (2016). doi: 10.1016/j.tics.2016.05.004 CrossRefGoogle Scholar
  31. 31.
    Ramirez, S., Liu, X., MacDonald, C.J., et al.: Activating positive memory engrams suppresses depression-like behaviour. Nature 522, 335–339 (2015)CrossRefGoogle Scholar
  32. 32.
    Nabavi, S., Fox, R., Proulx, C.D., et al.: Engineering a memory with LTD and LTP. Nature 511, 348–352 (2014)CrossRefGoogle Scholar
  33. 33.
    Redondo, R.L., Kim, J., Arons, A.L., et al.: Bidirectional switch of the valence associated with a hippocampal contextual memory engram. Nature 513, 426–430 (2014)CrossRefGoogle Scholar
  34. 34.
    Chong, T.T.-J., Apps, M., Giehl, K., et al.: Neurocomputational mechanisms underlying subjective valuation of effort costs. PLoS Biol. 15, e1002598 (2017). doi: 10.1371/journal.pbio.1002598 CrossRefGoogle Scholar
  35. 35.
    Ide, J.S., Shenoy, P., Yu, A.J., Li, C.R.: Bayesian prediction and evaluation in the anterior cingulate cortex. J. Neurosci. 33, 2039 (2013). doi: 10.1523/JNEUROSCI.2201-12.2013 CrossRefGoogle Scholar
  36. 36.
    Schmidt, L., Lebreton, M., Cléry-Melin, M.-L., et al.: Neural mechanisms underlying motivation of mental versus physical effort. PLoS Biol. 10, e1001266 (2012). doi: 10.1371/journal.pbio.1001266 CrossRefGoogle Scholar
  37. 37.
    Domenech, P., Redouté, J., Koechlin, E., Dreher, J.-C.: The neuro-computational architecture of value-based selection in the human brain. Cereb. Cortex (2017)Google Scholar
  38. 38.
    Klyubin, A.S., Polani, D., Nehaniv, C.L.: Empowerment: a universal agent-centric measure of control. In: 2005 IEEE Congress on Evolution Computation, vol. 1, pp. 128–135 (2005)Google Scholar
  39. 39.
    Leon, P.S., Knock, S.A., Woodman, M.M., et al.: The Virtual brain: a simulator of primate brain network dynamics. Inf. Based Methods Neuroimaging Anal. Struct. Funct. Dyn. 8 (2015)Google Scholar
  40. 40.
    Petitmengin, C.: Describing one’s subjective experience in the second person: an interview method for the science of consciousness. Phenomenol Cogn. Sci. 5, 229–269 (2006). doi: 10.1007/s11097-006-9022-2 CrossRefGoogle Scholar
  41. 41.
    Petitmengin, C., Lachaux, J.-P.: Microcognitive science: bridging experiential and neuronal microdynamics. Front. Hum. Neurosci. 7, 617 (2013). doi: 10.3389/fnhum.2013.00617 CrossRefGoogle Scholar
  42. 42.
    Petitmengin, C., Baulac, M., Navarro, V.: Seizure anticipation: Are neurophenomenological approaches able to detect preictal symptoms? Epilepsy Behav. 9, 298–306 (2006). doi: 10.1016/j.yebeh.2006.05.013 CrossRefGoogle Scholar
  43. 43.
    Mason, C.: Engineering kindness: building a machine with compassionate intelligence. Int. J. Synth. Emot. IJSE 6, 1–23 (2015)CrossRefGoogle Scholar
  44. 44.
    Prassler, E., Lawitzky, G., Stopp, A., et al.: Advances in Human-Robot Interaction. Springer, Heidelberg (2004)Google Scholar
  45. 45.
    Coleman, D.: Human-Robot Interactions: Principles, Technologies and Challenges. Nova Science Publishers, Incorporated, New York (2015)Google Scholar
  46. 46.
    Kanda, T., Ishiguro, H.: Human-Robot Interaction in Social Robotics. Taylor & Francis, New York (2012)CrossRefGoogle Scholar
  47. 47.
    Jentsch, F., Barnes, M., Harris, P.D., et al.: Human-Robot Interactions in Future Military Operations. Ashgate Publishing Limited, Aldershot (2012)Google Scholar
  48. 48.
    Browning, F.: The Fate of Gender: Nature, Nurture, and the Human Future. Bloomsbury Publishing, London (2016)Google Scholar
  49. 49.
    Freud, S., Strachey, J.: An Outline of Psycho-analysis. W. W. Norton, New York (1989)Google Scholar
  50. 50.
    Jung, C.G.: The Psychology of the Transference. Taylor & Francis, New York (2013)Google Scholar
  51. 51.
    Gallese, V., Goldman, A.: Mirror neurons and the simulation theory of mind-reading. Trends Cogn. Sci. 2, 493–501 (1998)CrossRefGoogle Scholar
  52. 52.
    Heyes, C.: Where do mirror neurons come from? Neurosci. Biobehav. Rev. 34, 575–583 (2010)CrossRefGoogle Scholar
  53. 53.
    Iacoboni, M.: Imitation, empathy, and mirror neurons. Annu. Rev. Psychol. 60, 653–670 (2009)CrossRefGoogle Scholar
  54. 54.
    Uddin, L.Q., Iacoboni, M., Lange, C., Keenan, J.P.: The self and social cognition: the role of cortical midline structures and mirror neurons. Trends Cogn. Sci. 11, 153–157 (2007)CrossRefGoogle Scholar
  55. 55.
    Matsuda, G., Hiraki, K., Ishiguro, H.: EEG-based mu rhythm suppression to measure the effects of appearance and motion on perceived human likeness of a robot. J. Hum. Rob. Interact. 5, 68–81 (2015)CrossRefGoogle Scholar
  56. 56.
    Pineda, J.A.: The functional significance of mu rhythms: translating “seeing” and “hearing” into “doing”. Brain Res. Rev. 50, 57–68 (2005). doi: 10.1016/j.brainresrev.2005.04.005 CrossRefGoogle Scholar
  57. 57.
    Ulloa, E.R., Pineda, J.A.: Recognition of point-light biological motion: Mu rhythms and mirror neuron activity. Behav. Brain Res. 183, 188–194 (2007). doi: 10.1016/j.bbr.2007.06.007 CrossRefGoogle Scholar
  58. 58.
    Europe’s robots to become “electronic persons” under draft plan. In: Reuters (2017). Accessed 9 Mar 2017Google Scholar
  59. 59.
    Montes, J.: The Mars Ice House (2015)Google Scholar
  60. 60.
    Richter, C.G., Babo-Rebelo, M., Schwartz, D., Tallon-Baudry, C.: Phase-amplitude coupling at the organism level: the amplitude of spontaneous alpha rhythm fluctuations varies with the phase of the infra-slow gastric basal rhythm. NeuroImage 146, 951–958 (2017). doi: 10.1016/j.neuroimage.2016.08.043 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of NewcastleNewcastleAustralia
  2. 2.Hunter Medical Research InstituteNewcastleAustralia

Personalised recommendations