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Probabilistic pragmatics explains gradience and focality in natural language quantification.

Bob van Tiel^1,2, Michael Franke³, Uli Sauerland²

¹Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 AJ, Nijmegen, The Netherlands; bobvantiel@gmail.com.

Proceedings of the National Academy of Sciences of the United States of America

|February 23, 2021

Summary

Meaning in language, whether truth-conditional or prototype-based, can be modeled using probabilistic pragmatics. Empirical data on quantity words suggest prototypes are key, but truth-conditional models also perform well with pragmatic reasoning.

Keywords:

language pragmatics probabilistic reasoning quantifiers semantics

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Area of Science:

Cognitive Science
Linguistics
Philosophy of Language

Background:

Contrasting theories of linguistic meaning: truth-conditional semantics versus prototype-based semantics.
Existing semantic theories lack direct predictions for quantitative language use.
Need for empirical investigation into how meaning relates to language production.

Purpose of the Study:

To empirically compare truth-conditional and prototype-based semantic theories.
To investigate the role of probabilistic pragmatic models in linking meaning and language use.
To analyze the production of quantity words (e.g., 'some', 'all') as a case study.

Main Methods:

Conducted a large-scale production study on the use of quantity words.
Developed and compared computational models based on truth-conditional and prototype-based semantics.
Incorporated cognitive factors like salience and numerosity representation into the models.
Utilized probabilistic pragmatic models to connect semantic theories with language use data.

Main Results:

Production data suggest that quantity words are understood through prototypes.
Both truth-conditional and prototype-based models, when augmented with pragmatic reasoning, explain the production data equally well.
Probabilistic pragmatic models effectively link linguistic meaning to observed language use.

Conclusions:

Prototype-based understanding of quantity words is supported by empirical data.
Truth-conditional semantics can account for language use data when complemented by pragmatic inference about listener uptake.
Probabilistic pragmatic models offer a unifying framework for understanding the relationship between abstract meaning and language behavior.