Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Probabilistic models of language processing and acquisition.

Nick Chater1, Christopher D Manning

  • 1Department of Psychology, University College London, Gower Street, London, WC1E 6BT, UK. n.chater@ucl.ac.uk

Trends in Cognitive Sciences
|June 21, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Agreements and disagreements with resource-rational contractualism.

The Behavioral and brain sciences·2026
Same author

The Political Psychology of Economic Inequality.

Psychological science in the public interest : a journal of the American Psychological Society·2026
Same author

Random generation is what comes to mind in naturalistic settings.

Cognition·2026
Same author

Imagining and building wise machines: the centrality of AI metacognition.

Trends in cognitive sciences·2026
Same author

The Lancet Commission on improving population health post-COVID-19.

Lancet (London, England)·2025
Same author

Social Tinkering: The Social Foundations of Cultural Complexity.

The Behavioral and brain sciences·2025
Same journal

Misinformation as strategy: Epistemic consequences and the undermining of shared truth.

Trends in cognitive sciences·2026
Same journal

Geographical psychology: Spatial variation in psychological phenomena and their consequences.

Trends in cognitive sciences·2026
Same journal

Multi-brain neurofeedback: what are we training for?

Trends in cognitive sciences·2026
Same journal

The developing vocal self.

Trends in cognitive sciences·2026
Same journal

Searching beyond decrements: Attentional guidance across the adult lifespan.

Trends in cognitive sciences·2026
Same journal

Looking into working memory through micro eye movements.

Trends in cognitive sciences·2026
See all related articles

Probabilistic models offer new ways to understand language. These models explain how humans process and acquire language, integrating symbolic structures with probabilistic inference for robust cognitive science insights.

Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Psycholinguistics

Background:

  • Traditional cognitive science approaches struggle with language's complexity.
  • Symbolic models lack sufficient explanatory power for language acquisition and processing.
  • A need exists for integrating probabilistic frameworks with symbolic language structures.

Purpose of the Study:

  • To review probabilistic models applied to symbolic language structures.
  • To explore how these models explain language comprehension, production, and acquisition.
  • To highlight the synergy between probabilistic methods and symbolic approaches in cognitive science.

Main Methods:

  • Examination of probabilistic models defined over traditional symbolic structures.
  • Analysis of probabilistic inference in language comprehension and production.

Related Experiment Videos

  • Review of model selection processes in language acquisition, considering innate constraints and input.

    • Main Results:

    • Probabilistic models successfully account for language learning and processing while retaining symbolic sophistication.
    • Recent theoretical advancements and corpus data enable robust testing of these models.
    • Evidence suggests probabilistic constraints in language processing and acquisition, challenging 'poverty of the stimulus' arguments.

    Conclusions:

    • Probabilistic models provide a powerful framework for understanding fundamental questions in language and cognition.
    • These models offer sophisticated explanations for language acquisition and processing.
    • Links between probabilistic language theories and perception theories (categorization, ambiguity resolution) are promising.