The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Empirical assessment of stimulus poverty arguments
Abstract
This article examines a type of argument for linguistic nativism that takes the following form: (i) a fact about some natural language is exhibited that allegedly could not be learned from experience without access to a certain kind of (positive) data; (ii) it is claimed that data of the type in question are not found in normal linguistic experience; hence (iii) it is concluded that people cannot be learning the language from mere exposure to language use. We analyze the components of this sort of argument carefully, and examine four exemplars, none of which hold up. We conclude that linguists have some additional work to do if they wish to sustain their claims about having provided support for linguistic nativism, and we offer some reasons for thinking that the relevant kind of future work on this issue is likely to further undermine the linguistic nativist position.
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
ASPECTS OF THE THEORY OF SYNTAX
Chomsky, N. (1964) · doi:10.21236/ad0616323 · Cited by 13,587
Structure Dependence in Grammar Formation
Crain, S., & Nakayama, M. (1987) · Language · doi:10.2307/415004 · Cited by 402
Language Acquisition and Use: Learning and Applying Probabilistic Constraints
Seidenberg, M. S. (1997) · Science · doi:10.1126/science.275.5306.1599 · Cited by 338
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
Constructions: A new theoretical approach to language
Goldberg, A. E. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(03)00080-9 · Cited by 1,812
Neural Network Acceptability Judgments
Warstadt, A., Singh, A., & Bowman, S. R. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00290 · Cited by 1,215
Culicover, P. W., & Jackendoff, R. (2005) · doi:10.1093/acprof:oso/9780199271092.001.0001 · Cited by 1,079
Haidt, J., & Joseph, C. (2008) · The Innate Mind, Volume 3 · doi:10.1093/acprof:oso/9780195332834.003.0019 · Cited by 650
Metadata
- Authors
- Pullum, G. K., & Scholz, B. C.
- Venue
- The Linguistic Review
- Publisher
- Walter de Gruyter GmbH
- Published
- 2002-01-26
- Volume
- 18
- Issue
- 1-2
- ISSN
- 0167-6318 (ISSN-L)
- 1613-3676
- OpenAlex
- W1997396881
- Cited by
- 919
- References
- 3
- FWCI
- 504.75
- 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: Empirical assessment of stimulus poverty arguments. https://app.cochat.ai/papers/10.1515/tlir.19.1-2.9
- The paper
- Pullum, G. K., & Scholz, B. C. (2002). Empirical assessment of stimulus poverty arguments. The Linguistic Review. https://doi.org/10.1515/tlir.19.1-2.9
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.