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Most quantifiers have many meanings.

Sonia Ramotowska1, Julia Haaf2, Leendert Van Maanen3

  • 1Institute for Logic, Language and Computation, University of Amsterdam, Science Park 107, 1098 XG, Amsterdam, The Netherlands. s.ramotowska@uva.nl.

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Summary
This summary is machine-generated.

Individuals map quantifiers like "few" and "most" to numbers differently, showing varied understanding of vague terms. This study reveals distinct subgroups based on how they interpret quantifier meanings.

Keywords:
Hierarchical Bayesian modelQuantifiersResponse errorVagueness

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Area of Science:

  • Cognitive Science
  • Psycholinguistics
  • Computational Linguistics

Background:

  • Quantifiers like 'few' and 'most' are essential for expressing quantity but possess inherent vagueness.
  • Understanding how humans interpret and mentally represent these quantifiers is crucial for semantic theories.
  • Existing models struggle to fully account for the variability in quantifier interpretation.

Purpose of the Study:

  • To investigate individual differences in how people map quantifiers to numerical values.
  • To explore the ordering of quantifiers on a mental number line.
  • To identify sources of variation in quantifier meaning representation.

Main Methods:

  • An online experiment collected binary truth-value judgments for five English quantifiers.
  • A Bayesian three-parameter logistic regression model was employed for data analysis.
  • Clustering analysis identified subgroups based on quantifier-to-number mappings.

Main Results:

  • Three sources of individual differences were identified: truth conditions, vagueness, and response error.
  • Four distinct subgroups of participants emerged, differing in quantifier interpretation and mental number line representation.
  • Quantifier interpretation varies significantly across individuals.

Conclusions:

  • Semantic representations of quantifiers are influenced by multiple sources of individual differences.
  • Findings challenge purely bivalent, truth-conditional approaches to quantifiers.
  • Fuzzy logic approaches require further refinement to capture observed individual variations in quantifier meaning.