- Michael Henry Tessler: Department of Psychology, Stanford University.
- Noah D Goodman: Department of Psychology, Stanford University.
Language provides simple ways of communicating generalizable knowledge to each other (e.g., "Birds fly," "John hikes," and "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. The basic idea is that the language of generalization expresses that an event or a property occurs relatively often, where what counts as relatively often depends upon one's prior expectations. We formalize this simple idea in a probabilistic model of language understanding, which we test in 3 diverse case studies: generalizations about categories (generic language), events (habitual language), and causes (causal language). We find that the model explains the gradience in human endorsements that has perplexed previous attempts to formalize this swath of linguistic expressions. This work opens the door to understanding precisely how abstract knowledge is learned from language. (PsycINFO Database Record (c) 2019 APA, all rights reserved).