 Computational models of learning the meaning of quantifiers have been given; for example by Clark (2011a) in the semantic automata setting. In a recent development, Steinert-Threlkeld and Szymanik (forthcoming) studies learnability with the technology of neural networks, testing whether certain quantifiers satisfying three commonly proposed universals—that simple determiner denotations are monotone, Isom, and Conserv, respectively—are easier to learn than quantifiers that do not have these properties. For each universal, the time it takes the network to learn a quantifier satisfying it is compared to the time it takes to learn a quantifier that doesn’t. It turns out that monotone and Isom are easier than non-monotone and non-Isom ones, whereas there is no detectable difference for Conserv.[45]