Cited by 414Language Development and DisordersNatural Language Processing TechniquesNeurobiology of Language and Bilingualism

Probabilistic models of language processing and acquisition

Chater, Nick, Manning, Christopher D.·2006·Trends in Cognitive Sciences

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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Crossref · 10.1016/j.tics.2006.05.006

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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.

    “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]

  • Language interpretation is a high-level cognitive task which shows a good correspondence to Bayesian models.

    “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]

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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
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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