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Related Experiment Video

Updated: Jun 12, 2026

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

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

Summary

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 modeling computational linguistics generalized quantifier theory psycholinguistics quantifiers semantics statistical modeling vagueness

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Last Updated: Jun 12, 2026

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

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

Published on: May 9, 2019

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

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.