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Warm (for Winter): Inferring Comparison Classes in Communication.

Michael Henry Tessler1,2, Noah D Goodman2,3

  • 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.

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Summary
This summary is machine-generated.

Listeners infer context by considering what a speaker would find remarkable to mention. This Bayesian pragmatic inference model reveals usage frequency and basic-level categories influence understanding comparative language.

Keywords:
Bayesian cognitive modelBayesian data analysisComparison classContext | AdjectivesPragmaticsRational Speech ActReference class

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Area of Science:

  • Linguistics
  • Cognitive Science
  • Pragmatics

Background:

  • Context is crucial for understanding natural language meaning, but the specific contextual elements, like comparison classes for gradable adjectives, are often implicit.
  • Theories acknowledge the importance of comparison classes but lack insight into how listeners determine them.

Purpose of the Study:

  • To extend a Bayesian model of pragmatic inference to flexibly reason about comparison classes.
  • To test the model's predictions regarding context inference in a large-scale experiment.

Main Methods:

  • Utilized a Bayesian model of pragmatic inference, focusing on gradable adjectives (e.g., 'warm').
  • Conducted a large-scale free-production experiment to test model predictions.
  • Employed Bayesian data analysis for quantitative synthesis of model and experimental data.

Main Results:

  • Human listeners infer comparison classes by considering what observations would be remarkable for a speaker to mention.
  • Usage frequency and a preference for basic-level categories significantly influence comparison class inference.
  • The study provides new data on the mechanisms of context recovery in language comprehension.

Conclusions:

  • Listeners actively infer implicit contextual information, specifically comparison classes, to understand gradable adjectives.
  • Bayesian pragmatic inference offers a robust framework for modeling context sensitivity in language.
  • Understanding the factors influencing comparison class selection enhances our knowledge of language meaning and communication.