Learning from generic language.

Abstract

Generic language (e.g., "Birds have hollow bones") conveys generalizations about categories and is a fundamental and ubiquitous mechanism of learning about the world. How exactly this learning occurs, however, is unclear: Generics exhibit so much flexibility in how they are interpreted that a quantitative theory of how generics update beliefs has not only not been developed or tested, but is often explicitly dismissed. Here, we explore a hypothesis introduced by Tessler and Goodman (2019) that generics update beliefs via an uncertain threshold like a vague quantifier: A category–property generic (Ks F) means that the property F is expected to be relatively widespread in the category K, where what counts as being relatively widespread is a priori uncertain and resolved by consulting one’s prior beliefs about the property. We compare this model to a family of alternatives with the same background knowledge but where generics have a fixed, precise meaning; in addition, we formalize a quantitative model of a strictly conceptual-based approach to generics, where generics are a direct connection to conceptual knowledge. Across three experiments in which we both measure and manipulate prior beliefs, we find the uncertain meaning approach to generics to be the best explanation of participants’ highly heterogeneous interpretations of novel generic statements. This result taken together with the result that the same model of generic meaning can explain the variability in generic endorsements as shown by Tessler & Goodman (2019) suggests that generics are an effective medium for faithful transmission of knowledge between interlocutors. This work adds to the growing enterprise of formal, quantitative studies of human understanding of generic language.