The Language of Generalization

TL;DR

This paper presents a formal model of how language conveys generalizable knowledge, explaining human understanding across categories, events, and causes through probabilistic semantics.

Contribution

It introduces the first formal, quantitative account of the language of generalization, integrating truth-conditional semantics with probabilistic beliefs.

Findings

Model explains gradience in human endorsement of generalizations

Successfully tested across categories, events, and causal language

Provides a framework for understanding how abstract knowledge is learned from language

Abstract

Language provides simple ways of communicating generalizable knowledge to each other (e.g., "Birds fly", "John hikes", "Fire makes smoke"). Though found in every language and emerging early in development, the language of generalization is philosophically puzzling and has resisted precise formalization. Here, we propose the first formal account of generalizations conveyed with language that makes quantitative predictions about human understanding. We test our model in three diverse domains: generalizations about categories (generic language), events (habitual language), and causes (causal language). The model explains the gradience in human endorsement through the interplay between a simple truth-conditional semantic theory and diverse beliefs about properties, formalized in a probabilistic model of language understanding. This work opens the door to understanding precisely how abstract knowledge is learned from language.