  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.
