The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Object name Learning Provides On-the-Job Training for Attention
Abstract
By the age of 3, children easily learn to name new objects, extending new names for unfamiliar objects by similarity in shape. Two experiments tested the proposal that experience in learning object names tunes children's attention to the properties relevant for naming—in the present case, to the property of shape—and thus facilitates the learning of more object names. In Experiment 1, a 9-week longitudinal study, 17-month-old children who repeatedly played with and heard names for members of unfamiliar object categories well organized by shape formed the generalization that only objects with similar shapes have the same name. Trained children also showed a dramatic increase in acquisition of new object names outside of the laboratory during the course of the study. Experiment 2 replicated these findings and showed that they depended on children's learning both a coherent category structure and object names. Thus, children who learn specific names for specific things in categories with a common organizing property—in this case, shape—also learn to attend to just the right property—in this case, shape—for learning more object names.
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
(1960) · Medical Entomology and Zoology · Cited by 5,851
Recognition-by-components: A theory of human image understanding.
Biederman, I. (1987) · Psychological Review · doi:10.1037/0033-295x.94.2.115 · Cited by 5,621
The Psychology of Learning and Motivation
(2021) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(21)x0002-3 · Cited by 5,027
Cognitive Development and Acquisition of Language
(1973) · Elsevier eBooks · doi:10.1016/c2009-0-22101-5 · Cited by 2,193
How Children Learn the Meanings of Words
Bloom, P. (2000) · The MIT Press eBooks · doi:10.7551/mitpress/3577.001.0001 · Cited by 1,922
ROSCH, E. H. (1973) · Cognitive Development and Acquisition of Language · doi:10.1016/b978-0-12-505850-6.50010-4 · Cited by 1,369
The importance of shape in early lexical learning
Landau, B., Smith, L. B., & Jones, S. S. (1988) · Cognitive Development · doi:10.1016/0885-2014(88)90014-7 · Cited by 1,174
Self-citationCategorization and naming in children: problems of induction
(1990) · Choice Reviews Online · doi:10.5860/choice.27-2985 · Cited by 1,046
Influences of categorization on perceptual discrimination.
Goldstone, R. L. (1994) · Journal of Experimental Psychology: General · doi:10.1037//0096-3445.123.2.178 · Cited by 714
Early noun vocabularies: do ontology, category structure and syntax correspond?
Samuelson, L. K., & Smith, L. B. (1999) · Cognition · doi:10.1016/s0010-0277(99)00034-7 · Cited by 456
Self-citation
Cited by
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Unmasking Clever Hans predictors and assessing what machines really learn
Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019) · Nature Communications · doi:10.1038/s41467-019-08987-4 · Cited by 1,047
Theory-based Bayesian models of inductive learning and reasoning
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.009 · Cited by 880
Can language restructure cognition? The case for space
Majid, A., Bowerman, M., Kita, S., Haun, D. B. M., & Levinson, S. C. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.01.003 · Cited by 872
Metadata
- Authors
- Smith, L. B., Jones, S. S., Landau, B., Gershkoff-Stowe, L., & Samuelson, L.
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 2002-01
- Volume
- 13
- Issue
- 1
- Pages
- 13-19
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- OpenAlex
- W2125500543
- Cited by
- 632
- References
- 17
- FWCI
- 35.70
- Type
- journal article
- Language
- en
- Open access
- closed
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: Object name Learning Provides On-the-Job Training for Attention. https://app.cochat.ai/papers/10.1111/1467-9280.00403
- The paper
- Smith, L. B., Jones, S. S., Landau, B., Gershkoff-Stowe, L., & Samuelson, L. (2002). Object name Learning Provides On-the-Job Training for Attention. Psychological Science. https://doi.org/10.1111/1467-9280.00403
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.