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Wiggins, Geraint A.; Pearce, Marcus T. (2006)
Publisher: University of California Press
Languages: English
Types: Article
The Implication-Realization (IR) theory (Narmour, 1990) posits two cognitive systems involved in the generation of melodic expectations: The first consists of a limited number of symbolic rules that are held to be innate and universal; the second reflects the top-down influences of acquired stylistic knowledge. Aspects of both systems have been implemented as quantitative models in research which has yielded empirical support for both components of the theory (Cuddy & Lunny, 1995; Krumhansl, 1995a, 1995b; Schellenberg, 1996, 1997). However, there is also evidence that the implemented bottom-up rules constitute too inflexible a model to account for the influence of the musical experience of the listener and the melodic context in which expectations are elicited. A theory is presented, according to which both bottom-up and top-down descriptions of observed patterns of melodic expectation may be accounted for in terms of the induction of statistical regularities in existing musical repertoires. A computational model that embodies this theory is developed and used to reanalyze existing experimental data on melodic expectancy. The results of three experiments with increasingly complex melodic stimuli demonstrate that this model is capable of accounting for listeners’ expectations as well as or better than the two-factor model of Schellenberg (1997).
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    • AARDEN, B. (2003). Dynamic melodic expectancy. Unpublished doctoral dissertation, Ohio State University, Columbus.
    • AHA, D. W., & BANKERT, R. L. (1996). A comparative evaluation of sequential feature selection algorithms. In D. Fisher & H. J. Lenz (Eds.), Learning from data: AI and statistics V (pp. 199-206). New York: Springer.
    • BALZANO, G. J. (1982). The pitch set as a level of description for studying musical pitch perception. In M. Clynes (Ed.), Music, mind and brain (pp. 321-351). New York: Plenum.
    • BERGESON, T. R. (1999). Melodic expectancy in infancy. Journal of the Acoustical Society of America, 106, 2285.
    • BHARUCHA, J. J. (1984). Anchoring effects in music: The resolution of dissonance. Cognitive Psychology, 16, 485-518.
    • BHARUCHA, J. J. (1987). Music cognition and perceptual facilitation: A connectionist framework. Music Perception, 5, 1-30.
    • BHARUCHA, J. J. (1993). Tonality and expectation. In R. Aiello (Ed.), Musical perceptions (pp. 213-239). Oxford: Oxford University Press.
    • BHARUCHA, J. J., & STOECKIG, K. (1986). Reaction time and musical expectancy: Priming of chords. Journal of Experimental Psychology: Human Perception and Performance, 12, 403-410.
    • BLUM, A., & LANGLEY, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97, 245-271.
    • CARLSEN, J. C. (1981). Some factors which influence melodic expectancy. Psychomusicology, 1, 12-29.
    • CASTELLANO, M. A., BHARUCHA, J. J., & KRUMHANSL, C. L. (1984). Tonal hierarchies in the music of North India. Journal of Experimental Psychology: General, 113, 394-412.
    • CHATER, N. (1996). Reconciling simplicity and likelihood principles in perceptual organisation. Psychological Review, 103, 566-581.
    • CHATER, N. (1999). The search for simplicity: A fundamental cognitive principle? The Quarterly Journal of Experimental Psychology, 52A, 273-302.
    • CHATER, N., & VITÁNYI, P. (2003). Simplicity: A unifying principle in cognitive science? Trends in Cognitive Sciences, 7, 19-22.
    • COHEN, A. J. (2000). Development of tonality induction: Plasticity, exposure and training. Music Perception, 17, 437-459.
    • CONKLIN, D. (2002). Representation and discovery of vertical patterns in music. In C. Anagnostopoulou, M. Ferrand, & A. Smaill (Eds.), Proceedings of the second international conference of music and artificial intelligence: Vol. 2445 (pp. 32-42). Berlin: Springer.
    • CONKLIN, D., & WITTEN, I. H. (1995). Multiple viewpoint systems for music prediction. Journal of New Music Research, 24, 51-73.
    • COVER, T. M., & KING, R. C. (1978). A convergent gambling estimate of the entropy of English. IEEE Transactions on Information Theory, 24, 413-421.
    • CREIGHTON, H. (1966). Songs and ballads from Nova Scotia. New York: Dover.
    • CROSS, I. (1995). Review of The analysis and cognition of melodic complexity: The implication-realization model, Narmour (1992). Music Perception, 12, 486-509.
    • CUDDY, L. L., & LUNNY, C. A. (1995). Expectancies generated by melodic intervals: Perceptual judgements of continuity. Perception and Psychophysics, 57, 451-462.
    • CUTTING, J. E., BRUNO, N., BRADY, N. P., & MOORE, C. (1992). Selectivity, scope, and simplicity of models: A lesson from fitting judgements of perceived depth. Journal of Experimental Psychology: General, 121, 364-381.
    • DELIÈGE, I. (1987). Grouping conditions in listening to music: An approach to Lerdahl and Jackendoff 's grouping preference rules. Music Perception, 4, 325-360.
    • DESAIN, P., HONING, H., THIENEN, H. VAN, & WINDSOR, L. (1998). Computational modelling of music cognition: Problem or solution. Music Perception, 16, 151-166.
    • DOWLING, W. J. (1994). Melodic contour in hearing and remembering melodies. In R. Aiello & J. Sloboda (Eds.), Musical perceptions (pp. 173-190). Oxford: Oxford University Press.
    • DOWLING, W. J., & BARTLETT, J. C. (1981). The importance of interval information in long-term memory for melodies. Psychomusicology, 1, 30-49.
    • ECK, D. (2002). Finding downbeats with a relaxation oscillator. Psychological Research, 66, 18-25.
    • EEROLA, T. (2004a). Data-driven influences on melodic expectancy: Continuations in North Sami Yoiks rated by South African traditional healers. In S. D. Lipscomb, R. Ashley, R. O. Gjerdingen, & P. Webster (Eds.), Proceedings of the Eighth International Conference of Music Perception and Cognition (pp. 83-87). Adelaide, Australia: Causal Productions.
    • EEROLA, T. (2004b). The dynamics of musical expectancy: Crosscultural and statistical approaches to melodic expectations. Doctoral dissertation, Faculty of Humanities, University of Jyväskylä, Finland. (Jyväskylä Studies in Humanities, 9)
    • EEROLA, T., TOIVIAINEN, P., & KRUMHANSL, C. L. (2002). Realtime prediction of melodies: Continuous predictability judgements and dynamic models. In C. Stevens, D. Burnham, E. Schubert, & J. Renwick (Eds.), Proceedings of the Seventh International Conference on Music Perception and Cognition (pp. 473-476). Adelaide, Australia: Causal Productions.
    • ELMAN, J. L., BATES, E. A., JOHNSON, M. H., KARMILOFF-SMITH, A., PARISI, D., & PLUNKETT, K. (1996). Rethinking innateness: A connectionist perspective on development. Cambridge, MA: MIT Press.
    • FERRAND, M., NELSON, P., & WIGGINS, G. (2003). Unsupervised learning of melodic segmentation: A memory-based approach. In R. Kopiez, A. C. Lehmann, & C. Wolf (Eds.), Proceedings of the 5th Triennial ESCOM Conference (pp. 141-144). Hanover, Germany: Hanover University of Music and Drama.
    • GJERDINGEN, R. O. (1999). Apparent motion in music? In N. Griffith & P. M. Todd (Eds.), Musical networks: Parallel distributed perception and performance (pp. 141-173). Cambridge, MA: MIT Press/Bradford Books.
    • HITTNER, J. B., MAY, K., & SILVER, N. C. (2003). A Monte Carlo evaluation of tests for comparing dependent correlations. The Journal of General Psychology, 130, 149-168.
    • JACKENDOFF, R. (1987). Consciousness and the computational mind. Cambridge, MA: MIT Press.
    • JAYNES, E. T. (2003). Probability theory: The logic of science. Cambridge, UK: Cambridge University Press.
    • JONES, M. R. (1987). Dynamic pattern structure in music: Recent theory and research. Perception and Psychophysics, 41, 621-634.
    • JONES, M. R., & BOLTZ, M. G. (1989). Dynamic attending and responses to time. Psychological Review, 96, 459-491.
    • KESSLER, E. J., HANSEN, C., & SHEPARD, R. N. (1984). Tonal schemata in the perception of music in Bali and the West. Music Perception, 2, 131-165.
    • KOHAVI, R., & JOHN, G. H. (1996). Wrappers for feature subset selection. Artificial Intelligence, 97, 273-324.
    • KRUMHANSL, C. L. (1990). Cognitive foundations of musical pitch. Oxford: Oxford University Press.
    • KRUMHANSL, C. L. (1995a). Effects of musical context on similarity and expectancy. Systematische Musikwissenschaft, 3, 211-250.
    • KRUMHANSL, C. L. (1995b). Music psychology and music theory: Problems and prospects. Music Theory Spectrum, 17, 53-90.
    • KRUMHANSL, C. L. (1997). Effects of perceptual organisation and musical form on melodic expectancies. In M. Leman (Ed.), Music, Gestalt and computing: Studies in cognitive systematic musicology (pp. 294-319). Berlin: Springer.
    • KRUMHANSL, C. L., & KESSLER, E. J. (1982). Tracing the dynamic changes in perceived tonal organisation in a spatial representation of musical keys. Psychological Review, 89, 334-368.
    • KRUMHANSL, C. L., LOUHIVUORI, J., TOIVIAINEN, P., JÄRVINEN, T., & EEROLA, T. (1999). Melodic expectation in Finnish spiritual hymns: Convergence of statistical, behavioural and computational approaches. Music Perception, 17, 151-195.
    • KRUMHANSL, C. L., TOIVANEN, P., EEROLA, T., TOIVIAINEN, P., JÄRVINEN, T., & LOUHIVUORI, J. (2000). Cross-cultural music cognition: Cognitive methodology applied to North Sami yoiks. Cognition, 76, 13-58.
    • LERDAHL, F., & JACKENDOFF, R. (1983). A generative theory of tonal music. Cambridge, MA: MIT Press.
    • LONGUET-HIGGINS, H. C., & STEEDMAN, M. J. (1971). On interpreting Bach. In B. Meltzer & D. Michie (Eds.), Machine intelligence 6 (pp. 221-241). Edinburgh, UK: Edinburgh University Press.
    • MACKAY, D. J. C. (2003). Information theory, inference, and learning algorithms. Cambridge, UK: Cambridge University Press.
    • MANNING, C. D., & SCHÜTZE, H. (1999). Foundations of statistical natural language processing. Cambridge, MA: MIT Press.
    • MANZARA, L. C., WITTEN, I. H., & JAMES, M. (1992). On the entropy of music: An experiment with Bach chorale melodies. Leonardo, 2, 81-88.
    • MARR, D. (1982). Vision. San Francisco: W. H. Freeman.
    • MCCLAMROCK, R. (1991). Marr's three levels: A re-evaluation. Minds and Machines, 1, 185-196.
    • MEYER, L. B. (1956). Emotion and meaning in music. Chicago: University of Chicago Press.
    • MEYER, L. B. (1973). Explaining music: Essays and explorations. Chicago: University of Chicago Press.
    • MITCHELL, T. M. (1997). Machine learning. New York: McGraw Hill.
    • NARMOUR, E. (1990). The analysis and cognition of basic melodic structures: The implication-realisation model. Chicago: University of Chicago Press.
    • NARMOUR, E. (1991). The top-down and bottom-up systems of musical implication: Building on Meyer's theory of emotional syntax. Music Perception, 9, 1-26.
    • NARMOUR, E. (1992). The analysis and cognition of melodic complexity: The implication-realisation model. Chicago: University of Chicago Press.
    • NARMOUR, E. (1999). Hierarchical expectation and musical style. In D. Deutsch (Ed.), The psychology of music (2nd ed., pp. 441-472). New York: Academic Press.
    • NOLAN, D. (1997). Quantitative parsimony. British Journal for the Philosophy of Science, 48, 329-343.
    • ORAM, N., & CUDDY, L. L. (1995). Responsiveness of Western adults to pitch-distributional information in melodic sequences. Psychological Research, 57, 103-118.
    • PALMER, C. (1997). Music performance. Annual Review of Psychology, 48, 115-138.
    • PALMER, R. (Ed.). (1983). Folk songs collected by Ralph Vaughan Williams. London: Dent.
    • PAUL, G. (1993). Approaches to abductive reasoning: An overview. Artificial Intelligence Review, 7, 109-152.
    • PEARCE, M. T., CONKLIN, D., & WIGGINS, G. A. (2005). Methods for combining statistical models of music. In U. K. Wiil (Ed.), Computer music modelling and retrieval (pp. 295-312). Heidelberg, Germany: Springer Verlag.
    • PEARCE, M. T., & WIGGINS, G. A. (2004). Improved methods for statistical modelling of monophonic music. Journal of New Music Research, 33, 367-385.
    • PLUNKETT, K., & MARCHMAN, V. (1996). Learning from a connectionist model of the acquisition of the past tense. Cognition, 61, 299-308.
    • POPPER, K. (1959). The logic of scientific discovery. London: Hutchinson and Co.
    • POVEL, D. J., & JANSEN, E. (2002). Harmonic factors in the perception of tonal melodies. Music Perception, 20, 51-85.
    • REIS, B. Y. (1999). Simulating music learning with autonomous listening agents: Entropy, ambiguity and context. Unpublished doctoral dissertation, Computer Laboratory, University of Cambridge, UK.
    • RIEMENSCHNEIDER, A. (1941). 371 harmonised chorales and 69 chorale melodies with figured bass. New York: G. Schirmer.
    • RUSSO, F. A., & CUDDY, L. L. (1999, March). A common origin for vocal accuracy and melodic expectancy: Vocal constraints. Paper presented at the Joint Meeting of the Acoustical Society of America and the European Acoustics Association, Berlin, Germany. (Published in Journal of the Acoustical Society of America, 105, 1217)
    • SAFFRAN, J. R., JOHNSON, E. K., ASLIN, R. N., & NEWPORT, E. L. (1999). Statistical learning of tone sequences by human infants and adults. Cognition, 70, 27-52.
    • SCHAFFRATH, H. (1992). The ESAC databases and MAPPET software. Computing in Musicology, 8, 66.
    • SCHAFFRATH, H. (1994). The ESAC electronic songbooks. Computing in Musicology, 9, 78.
    • SCHAFFRATH, H. (1995). The Essen folksong collection. In D. Huron (Ed.), Database containing 6,255 folksong transcriptions in the Kern format and a 34-page research guide [computer database]. Menlo Park, CA: CCARH.
    • ScHELLENBERG, E. G. (1996). Expectancy in melody: Tests of the implication-realisation model. Cognition, 58, 75-125.
    • ScHELLENBERG, E. G. (1997). Simplifying the implicationrealisation model of melodic expectancy. Music Perception, 14, 295-318.
    • ScHELLENBERG, E. G., Adachi, M., Purdy, K. T., & McKinnon, M. C. (2002). Expectancy in melody: Tests of children and adults. Journal of Experimental Psychology: General, 131, 511-537.
    • SCHMUCKLER, M. A. (1989). Expectation in music: Investigation of melodic and harmonic processes. Music Perception, 7, 109-150.
    • SCHMUCKLER, M. A. (1990). The performance of global expectations. Psychomusicology, 9, 122-147.
    • SCHMUCKLER, M. A. (1997). Expectancy effects in memory for melodies. Canadian Journal of Experimental Psychology, 51, 292-305.
    • SCHUBERT, E. (2001). Continuous measurement of self-report emotional responses to music. In P. N. Juslin & J. A. Sloboda (Eds.), Music and emotion (pp. 393-414). Oxford: Oxford University Press.
    • SHARP, C. J. (Ed.). (1920). English folk songs (Vols. 1-2, selected edition). London: Novello.
    • SHEPARD, R. N. (1982). Structural representations of musical pitch. In D. Deutsch (Ed.), Psychology of music (pp. 343-390). New York: Academic Press.
    • SOBER, E. (1981). The principle of parsimony. British Journal for the Philosophy of Science, 32, 145-156.
    • STEIGER, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87, 245-251.
    • THOMPSON, W. F. (1996). Eugene Narmour: The analysis and cognition of basic musical structures (1990) and The analysis and cognition of melodic complexity (1992): A review and empirical assessment. Journal of the American Musicological Society, 49, 127-145.
    • THOMPSON, W. F., CUDDY, L. L., & PLAUS, C. (1997). Expectancies generated by melodic intervals: Evaluation of principles of melodic implication in a melody-completion task. Perception and Psychophysics, 59, 1069-1076.
    • THOMPSON, W. F., & STAINTON, M. (1996). Using Humdrum to analyse melodic structure: An assessment of Narmour's implication-realisation model. Computing in Musicology, 12, 24-33.
    • THOMPSON, W. F., & STAINTON, M. (1998). Expectancy in Bohemian folk song melodies: Evaluation of implicative principles for implicative and closural intervals. Music Perception, 15, 231-252.
    • TOIVIAINEN, P., & EEROLA, T. (2004). The role of accent periodicities in metre induction: A classification study. In S. D. Lipscomb, R. Ashley, R. O. Gjerdingen, & P. Webster (Eds.), Proceedings of the Eighth International Conference of Music Perception and Cognition (pp. 422-425). Adelaide, Australia: Causal Productions.
    • TOIVIAINEN, P., & KRUMHANSL, C. L. (2003). Measuring and modelling real-time responses to music: The dynamics of tonality induction. Perception, 32, 741-766.
    • UNYK, A. M., & CARLSEN, J. C. (1987). The influence of expectancy on melodic perception. Psychomusicology, 7, 3-23.
    • VON HIPPEL, P. T. (2002). Melodic-expectation rules as learned heuristics. In C. Stevens, D. Burnham, E. Schubert, & J. Renwick (Eds.), Proceedings of the Seventh International Conference on Music Perception and Cognition (pp. 315-317). Adelaide, Australia: Causal Productions.
    • VON HIPPEL, P. T., & HURON, D. (2000). Why do skips precede reversals? The effects of tessitura on melodic structure. Music Perception, 18, 59-85.
    • VOS, P. G. (2000). Tonality induction: Theoretical problems and dilemmas. Music Perception, 17, 403-416.
    • VOS, P. G., & PASVEER, D. (2002). Goodness ratings of melodic openings and closures. Perception and Psychophysics, 64, 631-639.
    • VOS, P. G., & TROOST, J. M. (1989). Ascending and descending melodic intervals: Statistical findings and their perceptual relevance. Music Perception, 6, 383-396.
    • WITTEN, I. H., MANZARA, L. C., & CONKLIN, D. (1994). Comparing human and computational models of music prediction. Computer Music Journal, 18, 70-80.
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