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Related Experiment Videos

Acquiring and processing verb argument structure: distributional learning in a miniature language.

Elizabeth Wonnacott1, Elissa L Newport, Michael K Tanenhaus

  • 1Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD, UK. elizabeth.wonnacott@psy.ox.ac.uk

Cognitive Psychology
|July 31, 2007
PubMed
Summary
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Adults balance specific word rules with general language patterns. This study shows learners acquire both verb-specific and general rules from language input, even without semantic cues.

Area of Science:

  • Psycholinguistics
  • Cognitive Science
  • Computational Linguistics

Background:

  • Adult language knowledge balances lexically-based and general patterns.
  • Verb argument structure generalization presents learnability challenges, with debated roles for semantics.
  • Interactions between verb-specific and general constraints in sentence processing are also debated.

Purpose of the Study:

  • To investigate how learners acquire both verb-specific and verb-general patterns in artificial languages.
  • To explore the role of distributional information in the absence of semantic cues.
  • To examine the impact of these learned patterns on production, judgments, and real-time processing.

Main Methods:

  • Utilized artificial language learning across three experiments.

Related Experiment Videos

  • Employed languages lacking semantic cues for verb distribution.
  • Analyzed distributional information from linguistic input.

    • Main Results:

    • Learners successfully acquired both verb-specific and verb-general patterns.
    • Learned patterns influenced production, judgments, and real-time processing, mirroring natural language acquisition.
    • Learners demonstrated a rational approach to utilizing input statistics.

    Conclusions:

    • Distributional information alone is sufficient for acquiring both specific and general linguistic patterns.
    • Learners rationally integrate different statistical cues from the input.
    • A Bayesian framework for statistical learning may effectively model these findings.