The Language of Generalization
This work addresses a foundational puzzle in linguistics and cognitive science, offering a precise tool for studying how abstract knowledge is learned from language, though it is incremental in building on existing semantic theories.
The authors tackled the problem of formalizing how language conveys generalizable knowledge, proposing the first formal model that quantitatively predicts human understanding across categories, events, and causes, explaining gradience in endorsement through a probabilistic framework.
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.