 Tessler and Goodman (2019) offer an account of generics that is rooted in a Bayesian computational approach to cognition. They model acceptance/rejection of a generic as a speaker’s decision to produce an utterance of the generic to a naive listener. The goal of the speaker, on their model, is to produce an utterance that aligns the listener’s beliefs about the prevalence of the property among members of the kind (the “referent prevalence” in their terminology) with the speaker’s beliefs. The speaker’s decision is driven by whether the referent prevalence is more likely under the listener’s prior beliefs vs. their posterior beliefs after hearing the generic. On their model, generics have a threshold semantics, according to which a generic is true if the referent prevalence exceeds a threshold, however this threshold is unspecified and unknown by both speaker and hearer. Bayesian computational modeling provides a mathematical framework in which such uncertainty can be readily handled; specifically, interpretation on the model involves integrating over the possible values of this threshold. A listener’s posterior beliefs are arrived at by Bayesian updating, such that they are proportional to the product of the listener’s prior beliefs about the distribution of the prevalence \(p\) of the property in question (i.e., over comparable kinds), \(Pr(p),\) the probability of possible thresholds \(\theta\), \(Pr(\theta),\) and a delta function that is 1 if \(p \gt \theta\) and 0 otherwise.