The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Expectation in Melody: The Influence of Context and Learning
Open accessAbstract
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).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,947
The Logic of Scientific Discovery
(2013) · A Realist Theory of Science · doi:10.4324/9780203090732-9 · Cited by 7,731
Information theory, inference, and learning algorithms
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5949 · Cited by 6,362
Tests for comparing elements of a correlation matrix
Steiger, J. H. (1980) · Psychological Bulletin · doi:10.1037/0033-2909.87.2.245 · Cited by 4,621 · cited 2×
“…of the two models, since the individual participant data were not available for any of the experiments and the two models are not nested, Williams’ t statistic for comparing dependent correlations (Hittner, May, & Silver, 2003; Steiger, 1980) was used to compare the two models in each experiment.”
MethodProbability theory the logic of science
Jaynes, E. T., & Bretthorst, G. L. (2003) · Cited by 4,575
Probability Theory: The Logic of Science
Jaynes, E. T. (2003) · doi:10.1017/cbo9780511790423 · Cited by 4,045
A Generative Theory of Tonal Music
Child, P., Lerdahl, F., & Jackendoff, R. (1984) · Computer Music Journal · doi:10.2307/3679778 · Cited by 3,751
Selection of relevant features and examples in machine learning
Blum, A. L., & Langley, P. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00063-5 · Cited by 3,298
Rethinking Innateness: A Connectionist Perspective on Development
Harris, M. J., Karmiloff‐Smith, A., Parisi, D., & Plunkett, K. (1996) · Medical Entomology and Zoology · Cited by 1,990
Meyer, L. B. (1961) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226521374.001.0001 · Cited by 1,957
Rethinking Innateness: A Connectionist Perspective on Development
Elman, J., Bates, E., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996) · doi:10.7551/mitpress/5929.001.0001 · Cited by 1,511
Statistical learning of tone sequences by human infants and adults
Saffran, J. R., Johnson, E. K., Aslin, R. N., & Newport, E. L. (1999) · Cognition · doi:10.1016/s0010-0277(98)00075-4 · Cited by 1,429
Cognitive Foundations of Musical Pitch
Krumhansl, C. L. (2001) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195148367.001.0001 · Cited by 1,235
Dynamic attending and responses to time.
Jones, M. R., & Boltz, M. G. (1989) · Psychological Review · doi:10.1037//0033-295x.96.3.459 · Cited by 1,121
Consciousness and the Computational Mind
Jackendoff, R. S. (1987) · Works - Scholarship, Research, & Creative Expression (Swarthmore College) · Cited by 1,000
Krumhansl, C. L., & Kessler, E. J. (1982) · Psychological Review · doi:10.1037//0033-295x.89.4.334 · Cited by 930
InformationTheory, Inference, and Learning Algorithms
Mackay, D. J. C. (2003) · Cited by 648
Simplicity: a unifying principle in cognitive science?
Chater, N., & Vitányi, P. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)00005-0 · Cited by 494
The Analysis and Cognition of Basic Melodic Structures: The Implication-Realization Model
Zbikowski, L., & Narmour, E. (1993) · Journal of Music Theory · doi:10.2307/843949 · Cited by 491
A Comparative Evaluation of Sequential Feature Selection Algorithms
Aha, D. W., & Bankert, R. L. (1996) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2404-4_19 · Cited by 445
A Monte Carlo Evaluation of Tests for Comparing Dependent Correlations
Hittner, J. B., May, K., & Silver, N. C. (2003) · The Journal of General Psychology · doi:10.1080/00221300309601282 · Cited by 425
Palmer, C. (1997) · Annual Review of Psychology · doi:10.1146/annurev.psych.48.1.115 · Cited by 419
Music Cognition and Perceptual Facilitation: A Connectionist Framework
Bharucha, J. J. (1987) · Music Perception · doi:10.2307/40285384 · Cited by 379
Reaction time and musical expectancy: Priming of chords.
Bharucha, J. J., & Stoeckig, K. (1986) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037//0096-1523.12.4.403 · Cited by 351
The Search for Simplicity: A Fundamental Cognitive Principle?
Chater, N. (1999) · The Quarterly Journal of Experimental Psychology A · doi:10.1080/027249899391070 · Cited by 327
Multiple viewpoint systems for music prediction
Conklin, D., & Witten, I. H. (1995) · Journal of New Music Research · doi:10.1080/09298219508570672 · Cited by 327
The Analysis and Cognition of Melodic Complexity: The Implication-Realization Model
Gjerdingen, R. O., & Narmour, E. (1994) · Notes · doi:10.2307/898334 · Cited by 269
Expectation in Music: Investigation of Melodic and Harmonic Processes
Schmuckler, M. A. (1989) · Music Perception · doi:10.2307/40285454 · Cited by 253
Reconciling simplicity and likelihood principles in perceptual organization.
Chater, N. (1996) · Psychological Review · doi:10.1037//0033-295x.103.3.566 · Cited by 230
Anchoring effects in music: The resolution of dissonance
Bharucha, J. J. (1984) · Cognitive Psychology · doi:10.1016/0010-0285(84)90018-5 · Cited by 225
Deliege, I. (1987) · Music Perception · doi:10.2307/40285378 · Cited by 224
A convergent gambling estimate of the entropy of English
Cover, T., & King, R. (1978) · IEEE Transactions on Information Theory · doi:10.1109/tit.1978.1055912 · Cited by 220
Dynamic pattern structure in music: Recent theory and research
Jones, M. R. (1987) · Perception & Psychophysics · doi:10.3758/bf03210494 · Cited by 211
Cross-cultural music cognition: cognitive methodology applied to North Sami yoiks
Krumhansl, C. (2000) · Cognition · doi:10.1016/s0010-0277(00)00068-8 · Cited by 208
The importance of interval information in long-term memory for melodies.
Dowling, W. J., & Bartlett, J. C. (1981) · Psychomusicology: A Journal of Research in Music Cognition · doi:10.1037/h0094275 · Cited by 197
Structural Representations of Musical Pitch
Shepard, R. N. (1982) · Psychology of Music · doi:10.1016/b978-0-12-213562-0.50015-2 · Cited by 192
Music Psychology and Music Theory: Problems and Prospects
Krumhansl, C. L. (1995) · Music Theory Spectrum · doi:10.1525/mts.1995.17.1.02a00030 · Cited by 185
SOBER, E. (1981) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/32.2.145 · Cited by 181
Expectancies generated by melodic intervals: Perceptual judgments of melodic continuity
Cuddy, L. L., & Lunney, C. A. (1995) · Perception & Psychophysics · doi:10.3758/bf03213071 · Cited by 181
Tonal Schemata in the Perception of Music in Bali and in the West
Kessler, E. J., Hansen, C., & Shepard, R. N. (1984) · Music Perception · doi:10.2307/40285289 · Cited by 177
Nolan, D. (1997) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/48.3.329 · Cited by 173
Some factors which influence melodic expectancy.
Carlsen, J. C. (1981) · Psychomusicology: A Journal of Research in Music Cognition · doi:10.1037/h0094276 · Cited by 169
Marr's three levels: A re-evaluation
McClamrock, R. (1991) · Minds and Machines · doi:10.1007/bf00361036 · Cited by 162
Ascending and Descending Melodic Intervals: Statistical Findings and Their Perceptual Relevance
Vos, P. G., & Troost, J. M. (1989) · Music Perception An Interdisciplinary Journal · doi:10.2307/40285439 · Cited by 161
Approaches to abductive reasoning: an overview
Paul, G. (1993) · Artificial Intelligence Review · doi:10.1007/bf00849080 · Cited by 146
Krumhansl, C. L., Louhivuori, J., Toiviainen, P., Järvinen, T., & Eerola, T. (1999) · Music Perception An Interdisciplinary Journal · doi:10.2307/40285890 · Cited by 136
Narmour, E. (1991) · Music Perception An Interdisciplinary Journal · doi:10.2307/40286156 · Cited by 134
Improved Methods for Statistical Modelling of Monophonic Music
Pearce, M. T., & Wiggins, G. A. (2004) · Journal of New Music Research · doi:10.1080/0929821052000343840 · Cited by 131
Self-citationResponsiveness of Western adults to pitch-distributional information in melodic sequences
Oram, N., Cuddy, L. L., & Oram, N. (1995) · Psychological Research · doi:10.1007/bf00447080 · Cited by 121
Selectivity, scope, and simplicity of models: A lesson from fitting judgments of perceived depth.
Cutting, J. E., Bruno, N., Brady, N. P., & Moore, C. (1992) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.121.3.364 · Cited by 108
Learning from a connectionist model of the acquisition of the English past tense
Plunkett, K., & Marchman, V. A. (1996) · Cognition · doi:10.1016/s0010-0277(96)00721-4 · Cited by 103
Representation and Discovery of Vertical Patterns in Music
Conklin, D. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45722-4_5 · Cited by 102
Why Do Skips Precede Reversals? The Effect of Tessitura on Melodic Structure
von Hippel, P. T., & Huron, D. (2000) · Music Perception An Interdisciplinary Journal · doi:10.2307/40285901 · Cited by 100
Bharucha, J. J. (1994) · Cited by 94
The Pitch Set as a Level of Description for Studying Musical Pitch Perception
Balzano, G. J. (1982) · Springer eBooks · doi:10.1007/978-1-4684-8917-0_17 · Cited by 81
Aarden, B. (2003) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 77
Expectancy in melody: Tests of children and adults.
Schellenberg, E. G., Adachi, M., Purdy, K. T., & McKinnon, M. C. (2002) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.131.4.511 · Cited by 76
Methods for Combining Statistical Models of Music
Pearce, M. T., Conklin, D., & Wiggins, G. A. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-31807-1_22 · Cited by 72
Self-citationThompson, W. F., & Stainton, M. (1998) · Music Perception An Interdisciplinary Journal · doi:10.2307/40285766 · Cited by 48
Expectancy effects in memory for melodies.
Schmuckler, M. A. (1997) · Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · doi:10.1037/1196-1961.51.4.292 · Cited by 41
Hierarchical Expectation and Musical Style
Narmour, E. (1999) · Elsevier eBooks · doi:10.1016/b978-012213564-4/50013-5 · Cited by 40
Development of tonality induction: plasticity, exposure, and training.
Cohen, A. J. (2000) · Music Perception · doi:10.2307/40285828 · Cited by 37
Computational Modeling of Music Cognition: Problem or Solution?
Desain, P., Honing, H., Vanthienen, H., & Windsor, W. L. (1998) · Music Perception An Interdisciplinary Journal · doi:10.2307/40285783 · Cited by 29
Effects of perceptual organization and musical form on melodic expectancies
Krumhansl, C. L. (1997) · Lecture notes in computer science · doi:10.1007/bfb0034122 · Cited by 22
Cited by
Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns
Barascud, N., Pearce, M. T., Griffiths, T. D., Friston, K. J., & Chait, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508523113 · Cited by 296
Self-citationBrattico, E., & Pearce, M. (2013) · Psychology of Aesthetics, Creativity, and the Arts · doi:10.1037/a0031624 · Cited by 285
Self-citation
Metadata
- Authors
- Pearce, M. T., & Wiggins, G. A.
- Venue
- Music Perception
- Publisher
- University of California Press
- Published
- 2006-06
- Volume
- 23
- Issue
- 5
- Pages
- 377-405
- ISSN
- 0730-7829 (ISSN-L)
- 1533-8312
- OpenAlex
- W2039209917
- Cited by
- 297
- References
- 65
- FWCI
- 3.22
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.