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IDyOT: A Computational Theory of Creativity as Everyday Reasoning from Learned Information

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Computational Creativity Research: Towards Creative Machines

Part of the book series: Atlantis Thinking Machines ((ATLANTISTM,volume 7))

Abstract

We present progress towards a computational cognitive architecture, IDyOT (Information Dynamics of Thinking) that is intended to account for certain aspects of human creativity and other forms of cognitive processing in terms of a pre-conscious predictive loop. The theory is motivated in terms of the evolutionary pressure to be efficient. It makes several predictions that may be tested by building computational implementations and studying their behaviour.

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Notes

  1. 1.

    The interactions are particularly important: by considering them, we avoid the trap of naïvely assuming Fodorian modularity [15].

  2. 2.

    Exaptation is the appropriation of a biological capacity driven by given evolutionary pressures into a different function. An alternative view is that these capacities form spandrels, supporting other behaviours, but not becoming part of them.

  3. 3.

    We wish to avoid arguments over where the brain ends and the mind begins, so we use this epithet to refer to the whole assembly.

  4. 4.

    In earlier publications, we have referred to this as “inspiration” [53] and “non-conscious creativity”. However, “spontaneous” captures better the meaning we intend. Similarly, we have previously referred to deliberate creativity as “conscious creativity”.

  5. 5.

    We avoid the troublesome question of whether the phenomenon experienced as a conscious decision is really that, because it is not relevant here.

  6. 6.

    The number is not specified in Baars’ theory. In IDyOT, the number of generators has a direct bearing on the (statistical) prediction quality: as the number of generators increases, so does the likelihood of correct predictions.

  7. 7.

    The initial version of IDyOT has an abstract, symbolic representation of time; however, more developed versions will predict the real-world timing of perceptual input, as well as its content.

  8. 8.

    It will stabilise when it has produced an efficient model of the data that it is being exposed to.

  9. 9.

    That is to say, it cannot be computed in polynomial time by a von Neumann machine.

  10. 10.

    The International Phonetic Alphabet is used in dictionaries to specify a standard pronunciation of each word. Good dictionaries contain an explanation of the symbols in terms of the relevant language. IPA versions used here are taken from Apple’s UK English dictionary.

References

  1. Baars, B.J.: A Cognitive Theory of Consciousness. Cambridge University Press, Cambridge (1988)

    Google Scholar 

  2. Bengio, Y.: Learning deep architectures for ai. Found. Trends Mach. Learn. 2(1), 1–127 (2009). doi:10.1561/2200000006

    Article  MATH  MathSciNet  Google Scholar 

  3. Boden, M.A.: The Creative Mind: Myths and Mechanisms. Weidenfield and Nicholson, London (1990)

    Google Scholar 

  4. Brooks, R., Meltzoff, A.N.: The development of gaze following and its relation to language. Dev. Sci. 8(6), 535–543 (2005). doi:10.1111/j.1467-7687.2005.00445.x

    Article  Google Scholar 

  5. Chalmers, D.J.: The Conscious Mind: In Search of a Fundamental Theory. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  6. Cohen, P., Adams, N.: An algorithm for segmenting categorical time series into meaningful episodes. In: Hoffmann, F., Hand, D., Adams, N., Fisher, D., Guimaraes, G. (eds.) Advances in Intelligent Data Analysis. Lecture Notes in Computer Science, pp. 198–207. Springer, Berlin (2001). doi:10.1007/3-540-44816-0_20

  7. 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=

  8. Conklin, D., Witten, I.H.: Multiple viewpoint systems for music prediction. J. New Music Res. 24, 51–73 (1995)

    Article  Google Scholar 

  9. Corkill, D.D.: Blackboard systems. AI Expert 6(9), 40–47 (1991)

    Google Scholar 

  10. Egermann, H., Pearce, M., Wiggins, G., McAdams, S.: Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music. Cognit. Affect. Behav. Neurosci. 13(3), 533–553 (2013). doi:10.3758/s13415-013-0161-y

    Article  Google Scholar 

  11. Eshghi, A., Purver, M., Hough, J.: Probabilistic induction for an incremental semantic grammar. In: Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013)—Long Papers, pp. 107–118. Association for Computational Linguistics, Potsdam, Germany (2013). http://www.aclweb.org/anthology/W13-0110

  12. Fairbanks, G., Grubb, P.: A psychophysical investigation of vowel formants. J. Speech Hear. Res. 4, 203–219 (1961)

    Article  Google Scholar 

  13. Fink, A., Grabner, R.H., Benedek, M., Reishofer, G., Hauswirth, V., Fally, M., Neuper, C., Ebner, F., Neubauer, A.C.: The creative brain: investigation of brain activity during creative problem solving by means of eeg and fmri. Hum. Brain Map. 30, 734–748 (2009)

    Article  Google Scholar 

  14. Fitch, W.T., Hauser, M.D., Chomsky, N.: The evolution of the language faculty: clarifications and implications. Cognition 97, 179–210 (2005)

    Article  Google Scholar 

  15. Fodor, J.: Special sciences: or the disunity of science as a working hypothesis. Synthese 28, 97–115 (1974)

    Article  Google Scholar 

  16. Forth, J., Wiggins, G., McLean, A.: Unifying conceptual spaces: concept formation in musical creative systems. Mind. Mach. 20, 503–532 (2010). doi:10.1007/s11023-010-9207-x

    Article  Google Scholar 

  17. Gärdenfors, P.: Conceptual Spaces: The Geometry of Thought. MIT Press, Cambridge (2000)

    Google Scholar 

  18. Gobet, F., Lane, P.C.R., Croker, S., Cheng, P.C.H., Jones, G., Oliver, I., Pine, J.M.: Chunking mechanisms in human learning. TRENDS Cognit. Sci. 5(6), 236–243 (2001)

    Article  Google Scholar 

  19. Hale, J.: A probabilistic earley parser as a psycholinguistic model. In: Proceedings of NACL, pp. 159–166 (2001)

    Google Scholar 

  20. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  21. Holmes, E.: The Life of Mozart: Including His Correspondence. Cambridge University Press, Cambridge (2009)

    Book  Google Scholar 

  22. Honing, H.: Computational modeling of music cognition: a case study on model selection. Music Percept. 23(5), 365–376 (2006)

    Article  Google Scholar 

  23. Huron, D.: Sweet Anticipation: Music and the Psychology of Expectation. Bradford Books. MIT Press, Cambridge (2006)

    Google Scholar 

  24. Luck, M., McBurney, P., Preist, C.: Agent technology: enabling next generation computing. Agentlink. http://calcium.dcs.kcl.ac.uk/841/1/al2roadmap.pdf (2003)

  25. Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  26. Marr, D.: Vision: A Computational Approach. Freeman & Co., San Francisco (1982)

    Google Scholar 

  27. Marsden, A.: Response to Geraint Wiggins. J. Math. Music 6(2), 125–128 (2012)

    Article  MathSciNet  Google Scholar 

  28. Marslen-Wilson, W.D.: Function and process in spoken word recognition. In: Bouma, H., Bouwhuis, D. (eds.) Attention and Performance X: Control of Language Processes, pp. 125–150. Erlbaum, Hillsdale (1984)

    Google Scholar 

  29. McClamrock, R.: Marr’s three levels: a re-evaluation. Mind. Mach. 1(2), 185–196 (1991)

    Article  Google Scholar 

  30. Merker, B.: The efference cascade, consciousness, and its self: naturalizing the first person pivot of action control. Front. Psychol. 4(501) (2013). doi:10.3389/fpsyg.2013.00501

  31. Meyer, L.B.: Emotion and Meaning in Music. University of Chicago Press, Chicago (1956)

    Google Scholar 

  32. Narayanan, S., Jurafsky, D.: A bayesian model predicts human parse preference and reading time in sentence processing. In: Advances in Neural Information Processing Systems, vol. 14, pp. 59–65. MIT Press, Cambridge (2002)

    Google Scholar 

  33. Pearce, M.T.: The construction and evaluation of statistical models of melodic structure in music perception and composition. Ph.D. thesis, Department of Computing, City University, London (2005)

    Google Scholar 

  34. Pearce, M.T., Conklin, D., Wiggins, G.A.: Methods for combining statistical models of music. In: Wiil, U.K. (ed.) Computer Music Modelling and Retrieval, pp. 295–312. Springer, Heidelberg, Germany (2005). http://www.doc.gold.ac.uk/mas02gw/papers/cmmr04.pdf

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

    Article  Google Scholar 

  36. Pearce, M.T., Müllensiefen, D., Wiggins, G.A.: The role of expectation and probabilistic learning in auditory boundary perception: a model comparison. Perception 39(10), 1367–1391 (2010)

    Article  Google Scholar 

  37. Pearce, M.T., Wiggins, G.A.: Expectation in melody: the influence of context and learning. Music Percept. 23(5), 377–405 (2006)

    Article  Google Scholar 

  38. Pearce, M.T., Wiggins, G.A.: Evaluating cognitive models of musical composition. In: Cardoso, A., Wiggins, G.A. (eds.) Proceedings of the 4th International Joint Workshop on Computational Creativity, pp. 73–80. Goldsmiths, University of London, London (2007)

    Google Scholar 

  39. Pearce, M.T., Wiggins, G.A.: Auditory expectation: the information dynamics of music perception and cognition. Top. Cognit. Sci. 4(4), 625–652 (2012)

    Article  Google Scholar 

  40. Perruchet, P., Vinter, A.: Parser: a model for word segmentation. J. Mem. Lang. 39, 246–263 (1998)

    Article  Google Scholar 

  41. Ponsford, D., Wiggins, G.A., Mellish, C.: Statistical learning of harmonic movement. J. New Music Res. 28(2), 150–177 (1999). http://www.soi.city.ac.uk/geraint/papers/JNMR97.pdf

  42. Reynar, J.C., Ratnaparkhi, A.: A maximum entropy approach to identifying sentence boundaries. In: Proceedings of the 5th Conference on Applied Natural Language Processing, ANLC’97, pp. 16–19. Association for Computational Linguistics, Stroudsburg (1997). doi:10.3115/974557.974561

  43. Saffran, J.R., Griepentrog, G.J.: Absolute pitch in infant auditory learning: evidence for developmental reorganization. Dev. Psychol. 37(1), 74–85 (2001). http://www.waisman.wisc.edu/infantlearning/publications/DevPsychAP.pdf

  44. Servan-Schreiber, E., Anderson, J.R.: Learning artificial grammars with competitive chunking. J. Exp. Psychologyrimeatal Psychol. 16(4), 592–608 (1990)

    Google Scholar 

  45. Shanahan, M.: Embodiment and the Inner Life: Cognition and Consciousness in the Space of Possible Minds. Oxford University Press, Oxford (2010)

    Book  Google Scholar 

  46. Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27(379–423), 623–656 (1948)

    Article  MathSciNet  Google Scholar 

  47. Wallas, G.: The Art of Thought. Harcourt Brace, New York (1926)

    Google Scholar 

  48. Whorley, R.P., Wiggins, G.A., Rhodes, C., Pearce, M.T.: Multiple viewpoint systems: time complexity and the construction of domains for complex musical viewpoints in the harmonization problem. J. New Music Res. 42(3), 237–266 (2013). doi:10.1080/09298215.2013.831457. http://www.tandfonline.com/doi/abs/10.1080/09298215.2013.831457

  49. Wiggins, G.A.: A preliminary framework for description, analysis and comparison of creative systems. J. Knowl. Based Syst. 19(7), 449–458 (2006). doi:10.1016/j.knosys.2006.04.009

    Article  Google Scholar 

  50. Wiggins, G.A.: Searching for computational creativity. New Gener. Comput. 24(3), 209–222 (2006)

    Article  MATH  Google Scholar 

  51. Wiggins, G.A.: Models of musical similarity. Musicae Sci. Discuss. Forum 4A, 315–338 (2007)

    Google Scholar 

  52. Wiggins, G.A.: Computer models of (music) cognition. In: Rebuschat, P., Rohrmeier, M., Cross, I., Hawkins, J. (eds.) Language and Music as Cognitive Systems. Oxford University Press, Oxford (2011)

    Google Scholar 

  53. Wiggins, G.A.: Defining inspiration? modelling non-conscious creative process. In: Collins, D. (ed.) The Act of Musical Composition: Studies in the Creative Process. Ashgate, Aldershot (2012)

    Google Scholar 

  54. Wiggins, G.A.: I let the music speak: cross-domain application of a cognitive model of musical learning. In: Rebuschat, P., Williams, J. (eds.) Statistical Learning and Language Acquisition. Mouton De Gruyter, Amsterdam (2012)

    Google Scholar 

  55. Wiggins, G.A.: The mind’s chorus: creativity before consciousness. Cognit. Comput. 4(3), 306–319 (2012). doi:10.1007/s12559-012-9151-6

    Article  MathSciNet  Google Scholar 

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Acknowledgments

We gratefully acknowledge the contribution of our colleagues in the Intelligent Sound and Music Systems group at Goldsmiths, University of London and in the Computational Creativity Lab at Queen Mary University of London. In particular, we are grateful to Marcus Pearce, whose work on musical expectation originally inspired the current thinking, and to Kat Agres, Sascha Griffiths and Matt Purver for their insightful comments. Previous work on IDyOM (Sect. 7.3.1) was funded by EPSRC studentship number 00303840 to Marcus Pearce, and EPSRC research grants GR/S82220 and EP/H01294X to Marcus Pearce and the first author. The current work was funded by two project grants from the European Union Framework Programme 7, Lrn2Cre8 and ConCreTe. The projects ConCreTe and Lrn2Cre8 acknowledge the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET grants number 611733 and 610859 respectively.

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  1. Queen Mary University of London, London, UK

    Geraint A. Wiggins & Jamie Forth

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  1. Geraint A. Wiggins
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Correspondence to Geraint A. Wiggins .

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Editors and Affiliations

  1. Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany

    Tarek R. Besold

  2. IIIA-CSIC, Bellaterra (Barcelona), Spain

    Marco Schorlemmer

  3. CISA, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

    Alan Smaill

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Wiggins, G.A., Forth, J. (2015). IDyOT: A Computational Theory of Creativity as Everyday Reasoning from Learned Information. In: Besold, T., Schorlemmer, M., Smaill, A. (eds) Computational Creativity Research: Towards Creative Machines. Atlantis Thinking Machines, vol 7. Atlantis Press, Paris. https://doi.org/10.2991/978-94-6239-085-0_7

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  • DOI: https://doi.org/10.2991/978-94-6239-085-0_7

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