Proceedings of the Annual Meeting of the Cognitive Science Society

Generic language (e.g., “Birds fly”) conveys generalizationsabout categories and is essential for learning beyond our directexperience. The meaning of generic language is notoriouslyhard to specify, however (e.g., penguins don’t fly). Tessler andGoodman (2019b) proposed a model for generics that is math-ematically equivalent to Bayesian belief-updating based on asingle pedagogical example, suggesting a deep connection be-tween learning from experience and learning from language.Relatedly, Csibra and Shamsudheen (2015) argue that genericsare inherently pedagogical, understood by infants as referringto a member of a kind. In two experiments with adults, wequantify the exchange-rate between generics and observationsby relating their belief-updating capacity, varying both thenumber of observations and whether they are presented ped-agogically or incidentally. We find generics convey strongergeneralizations than single pedagogical observations (Expt. 1),even when the property is explicitly demarcated (Expt. 2). Wesuggest revisions to the vague quantifier model of generics thatwould allow it to accommodate this intriguing exchange-rate.