The DOI matches its Crossref record; 87 of the 90 references it cites were found in a registry or an index.
Probabilistic models of language processing and acquisition
Abstract
Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception.
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“Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language [1-10]”
From Bayesian Brains without Probabilities (2016) · cites this paper as [6]
In the citing paper
“Language interpretation is a high-level cognitive task which shows a good correspondence to Bayesian models [5,6].”
From Bayesian Brains without Probabilities (2016) · cites this paper as [6]
Citations
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Cited by
Sorted by citations
Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924
How to grow a mind: Statistics, structure, and abstraction
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Metadata
- Authors
- Chater, N., & Manning, C. D.
- Affiliations
- Stanford University (US)
- Corresponding author
- Manning, Christopher D.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2006-07
- Volume
- 10
- Issue
- 7
- Pages
- 335-344
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2162693531
- Semantic Scholar
- c351bcc20d0da75f23daaf20846cad0b93c4f36b
- Cited by
- 411
- References
- 90
- Citations by year
- 2026: 12 · 2025: 9 · 2024: 36 · 2023: 10 · 2022: 12 · 2021: 9 · 2020: 35 · 2019: 53 · 2018: 14 · 2017: 9 · 2016: 21 · 2015: 30 · 2014: 19 · 2013: 18 · 2012: 19
- FWCI
- 4.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Economic and Social Research Council (RES-538-28-1001)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Keywords
- probabilistic models, language acquisition, language comprehension, language production, probabilistic inference, symbolic models, poverty of the stimulus, ambiguity resolution, categorization, online corpora
- MeSH terms
- Brain, Cognition, Concept Formation, Humans, Language Development, Phonetics, Probability Theory, Psycholinguistics, Reading, Semantics, Speech Perception, Models, Statistical, Comprehension, Uncertainty
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