The DOI matches its Crossref record.
The Principle of Contrast: A Constraint on Language Acquisition
Abstract
Scholars of language and psycholinguistics have been among the first to stress the importance of rules in describing human behavior. Since the early work of Chomsky, a great deal of progress has been made by viewing language structure in terms of a system of rules. These insights clearly contribute to what Marr has called the computational theory of language - an abstract description of language itself and of the tasks facing the language learner and user. Our approach to the question of language representation and learning does not arise from within the tradition of linguistic theory. We are not linguists, but psychologists who have discovered that a new class of models based on highly parallel processing architectures appears to provide rather good accounts of a number of aspects of cognition. We call these models Parallel Distributed Processing (PDP) models.
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
Cited by
Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.211 · Cited by 6,282
Conceptual pacts and lexical choice in conversation.
Brennan, S. E., & Clark, H. H. (1996) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.22.6.1482 · Cited by 1,457
Children's use of mutual exclusivity to constrain the meanings of words
Markman, E. M., & Wachtel, G. F. (1988) · Cognitive Psychology · doi:10.1016/0010-0285(88)90017-5 · Cited by 1,337
Structural priming: A critical review.
Pickering, M. J., & Ferreira, V. S. (2008) · Psychological Bulletin · doi:10.1037/0033-2909.134.3.427 · Cited by 1,047
Predicting pragmatic reasoning in language games
Frank, M. C., & Goodman, N. D. (2012) · Science · doi:10.1126/science.1218633 · Cited by 868
Metadata
- Venue
- Mechanisms of Language Acquisition
- Publisher
- Psychology Press
- Published
- 2014-02-04
- Pages
- 17-50
- OpenAlex
- W50251313
- Cited by
- 664
- References
- 0
- FWCI
- 65.52
- Type
- book chapter
- 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: The Principle of Contrast: A Constraint on Language Acquisition. https://app.cochat.ai/papers/10.4324/9781315798721-7
- The paper
- The Principle of Contrast: A Constraint on Language Acquisition. (2014). Mechanisms of Language Acquisition. https://doi.org/10.4324/9781315798721-7
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.