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Related Experiment Videos

Are Semantic Representations Stable? A Bayesian Framework Applied to the Study of Quantifier Meaning.

Alexandra Sarafoglou¹, Anne S F Giacobello², Henrik R Godmann¹

¹Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

Computational Brain & Behavior

|June 11, 2026

Summary

Semantic representations of quantifiers like "most" are not stable over time or across paradigms, though their individual orderings remain consistent. Our Bayesian model reveals these dynamics.

Keywords:

Bayes factors Individual differences Inequality constraints Semantic representations

Related Experiment Videos

Area of Science:

Cognitive Science
Computational Linguistics
Psycholinguistics

Background:

Semantic representations of natural language quantifiers are crucial for understanding meaning.
Previous research has explored quantifier semantics, but often assumes stable representations.
Bayesian hierarchical modeling offers a framework to disentangle semantic parameters.

Purpose of the Study:

To propose and apply a Bayesian hierarchical model to analyze semantic representations of quantifiers.
To investigate the stability of semantic representations over time and across different experimental paradigms.
To disentangle three key semantic parameters: meaning threshold, vagueness, and response noise.

Main Methods:

Developed a Bayesian hierarchical model to analyze quantifier semantics.
Analyzed existing data (Ramotowska et al., 2023) to assess temporal stability.
Conducted a new experiment comparing linguistic and visual paradigms to assess cross-paradigm stability.

Main Results:

Found significant evidence that semantic representations change over time.
Observed that the relative ordering of meaning thresholds within individuals remained stable over time.
Detected significant differences in between-subject variability and vagueness across linguistic and visual paradigms.

Conclusions:

Semantic representations of logical vocabulary are not fixed but dynamic.
Individual-level relative ordering of meaning thresholds demonstrates stability.
The proposed Bayesian model effectively captures quantifier semantics, individual differences, and potential instability.