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.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The interactions are particularly important: by considering them, we avoid the trap of naïvely assuming Fodorian modularity [15].
- 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.
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.
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.
We avoid the troublesome question of whether the phenomenon experienced as a conscious decision is really that, because it is not relevant here.
- 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.
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.
It will stabilise when it has produced an efficient model of the data that it is being exposed to.
- 9.
That is to say, it cannot be computed in polynomial time by a von Neumann machine.
- 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
Baars, B.J.: A Cognitive Theory of Consciousness. Cambridge University Press, Cambridge (1988)
Bengio, Y.: Learning deep architectures for ai. Found. Trends Mach. Learn. 2(1), 1–127 (2009). doi:10.1561/2200000006
Boden, M.A.: The Creative Mind: Myths and Mechanisms. Weidenfield and Nicholson, London (1990)
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
Chalmers, D.J.: The Conscious Mind: In Search of a Fundamental Theory. Oxford University Press, Oxford (1996)
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
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=
Conklin, D., Witten, I.H.: Multiple viewpoint systems for music prediction. J. New Music Res. 24, 51–73 (1995)
Corkill, D.D.: Blackboard systems. AI Expert 6(9), 40–47 (1991)
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
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
Fairbanks, G., Grubb, P.: A psychophysical investigation of vowel formants. J. Speech Hear. Res. 4, 203–219 (1961)
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)
Fitch, W.T., Hauser, M.D., Chomsky, N.: The evolution of the language faculty: clarifications and implications. Cognition 97, 179–210 (2005)
Fodor, J.: Special sciences: or the disunity of science as a working hypothesis. Synthese 28, 97–115 (1974)
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
Gärdenfors, P.: Conceptual Spaces: The Geometry of Thought. MIT Press, Cambridge (2000)
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)
Hale, J.: A probabilistic earley parser as a psycholinguistic model. In: Proceedings of NACL, pp. 159–166 (2001)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Holmes, E.: The Life of Mozart: Including His Correspondence. Cambridge University Press, Cambridge (2009)
Honing, H.: Computational modeling of music cognition: a case study on model selection. Music Percept. 23(5), 365–376 (2006)
Huron, D.: Sweet Anticipation: Music and the Psychology of Expectation. Bradford Books. MIT Press, Cambridge (2006)
Luck, M., McBurney, P., Preist, C.: Agent technology: enabling next generation computing. Agentlink. http://calcium.dcs.kcl.ac.uk/841/1/al2roadmap.pdf (2003)
Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Marr, D.: Vision: A Computational Approach. Freeman & Co., San Francisco (1982)
Marsden, A.: Response to Geraint Wiggins. J. Math. Music 6(2), 125–128 (2012)
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)
McClamrock, R.: Marr’s three levels: a re-evaluation. Mind. Mach. 1(2), 185–196 (1991)
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
Meyer, L.B.: Emotion and Meaning in Music. University of Chicago Press, Chicago (1956)
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)
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)
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
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
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)
Pearce, M.T., Wiggins, G.A.: Expectation in melody: the influence of context and learning. Music Percept. 23(5), 377–405 (2006)
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)
Pearce, M.T., Wiggins, G.A.: Auditory expectation: the information dynamics of music perception and cognition. Top. Cognit. Sci. 4(4), 625–652 (2012)
Perruchet, P., Vinter, A.: Parser: a model for word segmentation. J. Mem. Lang. 39, 246–263 (1998)
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
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
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
Servan-Schreiber, E., Anderson, J.R.: Learning artificial grammars with competitive chunking. J. Exp. Psychologyrimeatal Psychol. 16(4), 592–608 (1990)
Shanahan, M.: Embodiment and the Inner Life: Cognition and Consciousness in the Space of Possible Minds. Oxford University Press, Oxford (2010)
Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27(379–423), 623–656 (1948)
Wallas, G.: The Art of Thought. Harcourt Brace, New York (1926)
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
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
Wiggins, G.A.: Searching for computational creativity. New Gener. Comput. 24(3), 209–222 (2006)
Wiggins, G.A.: Models of musical similarity. Musicae Sci. Discuss. Forum 4A, 315–338 (2007)
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)
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)
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)
Wiggins, G.A.: The mind’s chorus: creativity before consciousness. Cognit. Comput. 4(3), 306–319 (2012). doi:10.1007/s12559-012-9151-6
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Atlantis Press and the authors
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.2991/978-94-6239-085-0_7
Published:
Publisher Name: Atlantis Press, Paris
Print ISBN: 978-94-6239-084-3
Online ISBN: 978-94-6239-085-0
eBook Packages: Computer ScienceComputer Science (R0)