The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Learn locally, act globally: Learning language from variation set cues
Open accessAbstract
Variation set structure — partial overlap of successive utterances in child-directed speech — has been shown to correlate with progress in children’s acquisition of syntax. We demonstrate the benefits of variation set structure directly: in miniature artificial languages, arranging a certain proportion of utterances in a training corpus in variation sets facilitated word and phrase constituent learning in adults. Our findings have implications for understanding the mechanisms of L1 acquisition by children, and for the development of more efficient algorithms for automatic language acquisition, as well as better methods for L2 instruction.
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
Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models
Jaeger, T. F. (2008) · Journal of Memory and Language · doi:10.1016/j.jml.2007.11.007 · Cited by 3,317
Toward a mechanistic psychology of dialogue.
Pickering, M. J., & Garrod, S. (2004) · Behavioral and Brain Sciences · doi:10.1017/s0140525x04000056 · Cited by 2,678
Cited by
The myth of language universals: Language diversity and its importance for cognitive science
Evans, N., & Levinson, S. C. (2009) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0999094x · Cited by 2,623
Motherese in Interaction: At the Cross-Road of Emotion and Cognition? (A Systematic Review)
Saint-Georges, C., Chetouani, M., Cassel, R., Apicella, F., Mahdhaoui, A., Muratori, F., Laznik, M.-C., & Cohen, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0078103 · Cited by 343
Statistical learning as an individual ability: Theoretical perspectives and empirical evidence
Siegelman, N., & Frost, R. (2015) · Journal of Memory and Language · doi:10.1016/j.jml.2015.02.001 · Cited by 317
Language learning, socioeconomic status, and child‐directed speech
Schwab, J. F., & Lew‐Williams, C. (2016) · WIREs Cognitive Science · doi:10.1002/wcs.1393 · Cited by 287
Matthews, D., & Bannard, C. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2009.01091.x · Cited by 100
Metadata
- Authors
- Onnis, L., Waterfall, H. R., & Edelman, S.
- Venue
- Cognition
- Publisher
- Elsevier BV
- Published
- 2008-12
- Volume
- 109
- Issue
- 3
- Pages
- 423-430
- ISSN
- 0010-0277 (ISSN-L)
- OpenAlex
- W2100151927
- Cited by
- 201
- References
- 2
- FWCI
- 7.56
- Type
- journal article
- Language
- en
- Open access
- green
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: Learn locally, act globally: Learning language from variation set cues. https://app.cochat.ai/papers/10.1016/j.cognition.2008.10.004
- The paper
- Onnis, L., Waterfall, H. R., & Edelman, S. (2008). Learn locally, act globally: Learning language from variation set cues. Cognition. https://doi.org/10.1016/j.cognition.2008.10.004
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.