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  1. Home
  3. Resolving The Vagueness Of Quantifiers With Explicit Expectations.
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  3. Resolving The Vagueness Of Quantifiers With Explicit Expectations.

Related Experiment Video

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

Resolving the Vagueness of Quantifiers With Explicit Expectations.

Skyler Jove Reese1, Masoud Jasbi1, Emily Morgan1

  • 1Department of Linguistics, University of California, Davis, Davis, CA, USA.

Open Mind : Discoveries in Cognitive Science
|June 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Vague quantifiers like "many" have context-dependent meanings. This study tested a Bayesian model, finding some evidence for stable semantic thresholds but also for context-specific variations in quantifier meaning.

Keywords:
Bayesian hierarchical modelingcomputational linguisticsgeneralized quantifier theorypsycholinguisticsquantifierssemanticsstatistical modelingvagueness

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Area of Science:

  • Linguistics
  • Cognitive Science
  • Psychology

Background:

  • Vague quantifiers (e.g., 'many', 'few') present challenges for formal semantic theories due to their context-dependent nature.
  • Existing models struggle to capture the variability in quantity denoted by these terms across different contexts.
  • Understanding quantifier meaning is crucial for theories of language and cognition.

Purpose of the Study:

  • To extend and experimentally test a Bayesian model of quantifier meaning.
  • To investigate whether semantic thresholds for quantifiers are stable across contexts or context-dependent.
  • To differentiate between stable semantic bounds and context-specific pragmatic interpretations.

Main Methods:

  • Conducted two experiments: one eliciting contextual expectations and another collecting cardinality judgments.
  • Extended a Bayesian model representing quantifiers as cumulative density thresholds.
  • Fit five hierarchical Bayesian models and used information criteria (WAIC, DIC) for model comparison.

    • Main Results:

    • Estimated quantifier thresholds showed high overlap across contexts, suggesting some degree of stability.
    • Models incorporating individualized, context-specific thresholds consistently outperformed context-stable models.
    • Semantically motivated bounds demonstrated greater stability than pragmatically motivated bounds.

    Conclusions:

    • Results provide conflicting evidence regarding the stability of quantifier semantic thresholds.
    • Contextual variability in vague quantifiers likely arises from differences in expected value distributions.
    • Future research should further explore the interplay between stable semantic representations and context-dependent pragmatic inferences.