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The Mind’s Chorus: Creativity Before Consciousness

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Shannon wants to feed not just data to a Brain, but cultural things! He wants to play music to it!

(Alan Turing, quoted by Hodges [12, p. 251])

Abstract

I present a theoretical, hypothetical model of creative cognition, broadly framed within a massively parallel view of mental computation, and based on statistical simulation of aspects of memory and perception, particularly sequence. The theory is located at a level of abstraction substantively above that of neural substrate; it models function and not detailed mechanism and works over symbolic representations of percepts. I support the proposal with evidence from a range of computational work concerning learning of and generation from statistical models and argue that the perceptual grounding of the mechanisms presented here may be generalised away, to account, ultimately, for original thought itself.

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Notes

  1. I regret that there is not enough space here to expand on Shanahan’s contribution to the Global Workspace Theory, which is tangential to the current discussion, supplying a neural mechanism by which the Global Workspace may be implemented. However, Shanahan’s Chapter 1, in particular, on the philosophy of consciousness studies is required reading.

  2. For example, in the comic operette of Gilbert and Sullivan.

  3. The word ‘tunes’ is quoted here because it is Schoenberg’s own usage, not because I intend to question its propriety.

  4. While pure co-occurrence is not the only candidate for linguistic learning—Sperber and Wilson [40] give another influential theory, for example—association, and therefore at least implicit co-occurrence, does seem to be present in all convincing theories.

  5. Indeed, some humans who suffer from anxiety, in the clinical sense, report intrusive, repetitive thoughts predicting problems or worries of one sort or another, the anxiety being aroused by fear of what might happen. Their situation would be explicable in terms of a breakdown of this mechanism.

  6. Coupled with a deficit in suppression of less likely outcomes, as above, this situation might lead to some root symptoms of schizophrenia: hallucinations, delusions, and cognitive disorganisation.

  7. A more readily available journal presentation of the key modelling work is also available, Pearce and Wiggins [28].

References

  1. Baars BJ. A cognitive theory of consciousness. Cambridge: Cambridge University Press; 1988.

    Google Scholar 

  2. Boden M. Creativity and Artificial Intelligence. Artificial Intelligence. 1998;103:347–356.

  3. Chalmers DJ. The conscious mind: in search of a fundamental theory. Oxford: Oxford University Press; 1996.

    Google Scholar 

  4. Cleary JG, Teahan WJ. Unbounded length contexts for PPM. Comput J. 1997;40(2/3):67–75. http://www3.oup.co.uk/computer_journal/subs/Volume_40/Issue_03/Cleary.pdf

  5. Cleary JG, Witten IH. Data compression using adaptive coding and partial string matching. IEEE Trans Commun. 1984;32(4):396–402.

    Article  Google Scholar 

  6. Conklin D. Prediction and entropy of music. Master’s thesis, Department of Computer Science, University of Calgary, Canada; 1990. http://pharos.cpsc.ucalgary.ca:80/Dienst/UI/2.0/Describe/ncstrl.ucalgary_cs/1989-352-14?abstract=.

  7. Corkill DD. Blackboard systems. AI Expert. 1991;6(9):40–47. doi://10.1.1.137.7801.

    Google Scholar 

  8. Dennett D. Consciousness explained. Boston: Little, Brown and Co.; 1991.

    Google Scholar 

  9. Edelman GM, Gally JA, Baars BJ. Biology of consciousness. Front Psychol. 2011;2. doi:10.3389/fpsyg.2011.00004.

  10. Ellenberger HF. The discovery of the unconscious: the history and evolution of dynamic psychiatry. New York: Basic Books; 1970.

    Google Scholar 

  11. Gärdenfors P. Conceptual Spaces: the geometry of thought. Cambridge, MA: MIT Press; 2000.

    Google Scholar 

  12. Hodges A. Alan Turing: the enigma. London: Vintage; 1992.

    Google Scholar 

  13. Holmes E. The life of Mozart: including his correspondence. Cambridge: Cambridge Library Collection, Cambridge University Press; 2009.

    Book  Google Scholar 

  14. Huron D. Sweet anticipation: music and the psychology of expectation. Cambridge, MA: Bradford Books, MIT Press; 2006.

    Google Scholar 

  15. Jackson JV. Perception, logic and action through structured motivated associative pandemonium. In: Butz M, Sigaud O, Pezzulo G, Baldassarre G, editors. Anticipatory behavior in adaptive learning systems: from brains to individual and social behavior, LNCS/LNAI, vol 4520; 2007.

  16. Kurzweil R. The singularity is near: when humans transcend biology. New York: Viking Adult; 2005.

    Google Scholar 

  17. Lakoff G, Johnson M. Metaphors we live by. Chicago, IL: University of Chicago Press; 1980.

    Google Scholar 

  18. MacKay DJC. Information theory, inference, and learning algorithms. Cambridge: Cambridge University Press; 2003. http://www.inference.phy.cam.ac.uk/mackay/itila/.

  19. Manning CD, Schütze H. Foundations of statistical natural language processing. Cambridge, MA: MIT Press; 1999.

    Google Scholar 

  20. Margulis EH, Beatty AP. Musical style, psychoaesthetics, and prospects for entropy as an analytic tool. Comput Music J 2008;32(4):64–78.

    Article  Google Scholar 

  21. McCreight EM. A space-economical suffix tree construction algorithm. J ACM 1976;23(2):262–272. doi:10.1145/321941.321946.

    Article  Google Scholar 

  22. Meyer LB. Emotion and meaning in music. Chicago: University of Chicago Press; 1956.

    Google Scholar 

  23. Meyer LB. Meaning in music and information theory. J Aesthet Art Crit. 1957;15(4):412–424.

    Article  Google Scholar 

  24. Minsky M. The society of mind. New York, NY: Simon and Schuster Inc.; 1985.

    Google Scholar 

  25. Moffat A. Implementing the PPM data compression scheme. IEEE Trans Commun. 1990;38(11):1917–1921.

    Article  Google Scholar 

  26. Pearce MT. The construction and evaluation of statistical models of melodic structure in music perception and composition. PhD thesis, Department of Computing, City University, London, UK; 2005.

  27. Pearce MT, Wiggins GA. Improved methods for statistical modelling of monophonic music. J New Music Res. 2004;33(4):367–385.

    Article  Google Scholar 

  28. Pearce MT, Wiggins GA. Expectation in melody: the influence of context and learning. Music Percept. 2006;23(5):377–405.

    Article  Google Scholar 

  29. Pearce MT, Wiggins GA. Evaluating cognitive models of musical composition. In: Cardoso A, Wiggins GA, editors. Proceedings of the 4th international joint workshop on computational creativity, Goldsmiths. London: University of London; 2007. p. 73–80.

    Google Scholar 

  30. Pearce MT, Wiggins GA. Auditory expectation: the information dynamics of music perception and cognition. Top Cogn Sci. 2012; (in press).

  31. Pearce MT, Conklin D, Wiggins GA. Methods for combining statistical models of music. In: Wiil UK, editor. Computer music modelling and retrieval. Heidelberg, Germany: Springer; 2005. p. 295–312. URL http://www.doc.gold.ac.uk/mas02gw/papers/cmmr04.pdf.

  32. Pearce MT, Herrojo Ruiz M, Kapasi S, Wiggins GA, Bhattacharya J. Unsupervised statistical learning underpins computational, behavioural and neural manifestations of musical expectation. NeuroImage. 2010a;50(1):303–314 doi:10.1016/j.neuroimage.2009.12.019.

    Article  Google Scholar 

  33. Pearce MT, Müllensiefen D, Wiggins GA. The role of expectation and probabilistic learning in auditory boundary perception: a model comparison. Perception. 2010b;9:1367–1391.

    Google Scholar 

  34. Plotkin H. Evolution in mind. Cambridge, MA: Harvard University Press; 1998.

    Google Scholar 

  35. Roberson D, Davidoff J, Davies IRL, Shapiro LR. Colour categories and category acquisition in Himba and English. In: Pitchford N, Bingham C, editors. Progress in colour studies. Amsterdam: John Benjamins, 2006. p. 159–172.

    Google Scholar 

  36. Schoenberg A. Letters. Faber, London, edited by Erwin Stein. Translated from the original German by Eithne Wilkins and Ernst Kaiser 1974.

  37. Shanahan M. Embodiment and the inner life: cognition and consciousness in the space of possible minds. Oxford: Oxford University Press; 2010.

    Book  Google Scholar 

  38. Shannon C. A mathematical theory of communication. Bell Syst Tech J. 1948;27:379–423, 623–56.

    Google Scholar 

  39. Smith B, editor. Frederick Delius and Peter Warlock: a friendship revealed. Oxford: Oxford University Press; 2000.

    Google Scholar 

  40. Sperber D, Wilson D. Relevance: communication and cognition, second edition 1995. Oxford: Blackwell; 1986a.

  41. Stuckey S. Creating music of geometry and longing. Cornell Univ Arts Sci Newsl. 1996;18(1). URL http://www.arts.cornell.edu/newsletr/fall96/stucky.htm.

  42. Taine H. On intelligence. London: Savill, Edwards and Co.; 1871.

    Google Scholar 

  43. Tononi G, Edelman GM. Consciousness and complexity. Science 1998;282(5395):1846–1851.

    Article  PubMed  CAS  Google Scholar 

  44. Wallas G. The art of thought. New York: Harcourt Brace; 1926.

    Google Scholar 

  45. Whorley R, Wiggins GA, Rhodes C, Pearce M. Development of techniques for the computational modelling of harmony. In: Ventura et al., editors. Proceedings of the first international conference on computational creativity; 2010.

  46. Wiggins GA. Computer models of (music) cognition. In: Rebuschat P, Rohrmeier M, Cross I, Hawkins J, editors. Language and music as cognitive systems. Oxford: Oxford University Press; 2011.

    Google Scholar 

  47. Wiggins GA, Pearce MT, Müllensiefen D. Computational modelling of music cognition and musical creativity. In: Dean R, editors. Oxford handbook of computer music, chap 19. Oxford: Oxford University Press; 2009. p. 383–420.

    Google Scholar 

  48. Wundt WM. Grundzüge der physiologischen psychologie. Leipzig: W. Engelman; 1874.

    Google Scholar 

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Acknowledgments

I gratefully acknowledge the influence of my colleagues Marcus Pearce, Jamie Forth, Alex McLean, Ollie Bown, and Roger Dean on this work. The thinking and the writing were funded by EPSRC, through grant EP/H01294X, ‘Information and neural dynamics in the perception of musical structure’. I am also grateful to three anonymous reviewers for their constructive feedback, which has improved the paper considerable. Finally, Murray Shanahan’s excellent book, cited above, helped me see the original idea.

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  1. Intelligent Sound and Music Systems, Centre for Digital Music, Queen Mary, University of London, Mile End Road, London, E1 4NS, UK

    Geraint A. Wiggins

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Wiggins, G.A. The Mind’s Chorus: Creativity Before Consciousness. Cogn Comput 4, 306–319 (2012). https://doi.org/10.1007/s12559-012-9151-6

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