The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Implicit statistical learning in language processing: Word predictability is the key☆
Open accessAbstract
Fundamental learning abilities related to the implicit encoding of sequential structure have been postulated to underlie language acquisition and processing. However, there is very little direct evidence to date supporting such a link between implicit statistical learning and language. In three experiments using novel methods of assessing implicit learning and language abilities, we show that sensitivity to sequential structure -- as measured by improvements to immediate memory span for structurally-consistent input sequences -- is significantly correlated with the ability to use knowledge of word predictability to aid speech perception under degraded listening conditions. Importantly, the association remained even after controlling for participant performance on other cognitive tasks, including short-term and working memory, intelligence, attention and inhibition, and vocabulary knowledge. Thus, the evidence suggests that implicit learning abilities are essential for acquiring long-term knowledge of the sequential structure of language -- i.e., knowledge of word predictability – and that individual differences on such abilities impact speech perception in everyday situations. These findings provide a new theoretical rationale linking basic learning phenomena to specific aspects of spoken language processing in adults, and may furthermore indicate new fruitful directions for investigating both typical and atypical language development.
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
Early language acquisition: cracking the speech code
Kuhl, P. K. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1533 · Cited by 2,291
Learning and development in neural networks: the importance of starting small
Elman, J. L. (1993) · Cognition · doi:10.1016/0010-0277(93)90058-4 · Cited by 1,988 · cited 1×
“In fact, counter-intuitively, some research suggests that smaller memory capacities may actually be beneficial for learning complex input because it acts as a filter to reduce the complexity of the problem space, making it more manageable (Elman, 1993; Kareev, Lieberman, & Lev, 1997; Newport, 1990).”
BackgroundContributions of memory circuits to language: the declarative/procedural model
Ullman, M. T. (2004) · Cognition · doi:10.1016/j.cognition.2003.10.008 · Cited by 1,893
Maturational constraints on language learning
Newport, E. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1401_2 · Cited by 1,738
The proactive brain: using analogies and associations to generate predictions
Bar, M. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.05.005 · Cited by 1,144
Statistical Language Learning: Mechanisms and Constraints
Saffran, J. R. (2003) · Current Directions in Psychological Science · doi:10.1111/1467-8721.01243 · Cited by 624
Tsao, F.-M., Liu, H.-M., & Kuhl, P. K. (2004) · Child Development · doi:10.1111/j.1467-8624.2004.00726.x · Cited by 588
The link between statistical segmentation and word learning in adults
Mirman, D., Magnuson, J. S., Estes, K. G., & Dixon, J. A. (2008) · Cognition · doi:10.1016/j.cognition.2008.02.003 · Cited by 106
Cited by
An integrated theory of language production and comprehension
Pickering, M. J., & Garrod, S. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12001495 · Cited by 1,569
Predicting while comprehending language: A theory and review.
Pickering, M. J., & Gambi, C. (2018) · Psychological Bulletin · doi:10.1037/bul0000158 · Cited by 518
Four central questions about prediction in language processing
Huettig, F. (2015) · Brain Research · doi:10.1016/j.brainres.2015.02.014 · Cited by 405
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
Implicit statistical learning is directly associated with the acquisition of syntax
Kidd, E. (2012) · Developmental Psychology · doi:10.1037/a0025405 · Cited by 310
Metadata
- Authors
- Conway, C. M., Bauernschmidt, A., Huang, S. S., & Pisoni, D. B.
- Venue
- Cognition
- Publisher
- Elsevier BV
- Published
- 2010-03
- Volume
- 114
- Issue
- 3
- Pages
- 356-371
- ISSN
- 0010-0277 (ISSN-L)
- OpenAlex
- W2036514795
- Cited by
- 340
- References
- 8
- FWCI
- 24.22
- 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: Implicit statistical learning in language processing: Word predictability is the key☆. https://app.cochat.ai/papers/10.1016/j.cognition.2009.10.009
- The paper
- Conway, C. M., Bauernschmidt, A., Huang, S. S., & Pisoni, D. B. (2010). Implicit statistical learning in language processing: Word predictability is the key☆. Cognition. https://doi.org/10.1016/j.cognition.2009.10.009
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.