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The language of generalization.

Michael Henry Tessler1, Noah D Goodman1

  • 1Department of Psychology, Stanford University.

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Summary
This summary is machine-generated.

This study introduces a new probabilistic model for understanding how language conveys generalizations. The model explains how frequency and prior expectations shape our comprehension of general statements like "birds fly."

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Area of Science:

  • Cognitive Science
  • Linguistics
  • Psychology

Background:

  • Generalizations are fundamental to human communication and learning.
  • Previous formalizations of generalized language have faced challenges in explaining human understanding.
  • The philosophical basis of generalization in language remains complex.

Purpose of the Study:

  • To propose the first formal, probabilistic model of language-based generalizations.
  • To provide quantitative predictions about human comprehension of general statements.
  • To address the philosophical puzzle of how language conveys generalizable knowledge.

Main Methods:

  • Developed a probabilistic model of language understanding for generalizations.
  • Tested the model across three case studies: generic, habitual, and causal language.
  • Analyzed human endorsements of generalizations based on the model's predictions.

Main Results:

  • The proposed model successfully explains the gradience in human judgments of generalizations.
  • Demonstrated the model's applicability to diverse linguistic expressions of generality.
  • Provided a formal account for how "relatively often" is interpreted based on prior expectations.

Conclusions:

  • The model offers a novel framework for understanding abstract knowledge acquisition from language.
  • This work bridges formal semantics and cognitive psychology in the study of generalization.
  • Opens new avenues for research into the precise mechanisms of learning from linguistic generalizations.