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
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Expectation in Melody: The Influence of Context and Learning. https://app.cochat.ai/papers/10.1525/mp.2006.23.5.377
- The paper
- Pearce, M. T., & Wiggins, G. A. (2006). Expectation in Melody: The Influence of Context and Learning. Music Perception. https://doi.org/10.1525/mp.2006.23.5.377
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.