Adult learning of a novel quantifier tracks semantic universals

Abstract

Natural languages share common properties called universals. In the domain of quantification, three semantic universals were discovered: monotonicity (convexity), quantity, and conservativity. Researchers have been trying to explain the origin of semantic universals for decades. In this study, we tested one of the proposed explanations, the learnability hypothesis. According to this hypothesis, quantifiers that satisfy universals are easier to learn and, therefore, more likely to be lexicalized in natural language. We tested the learnability hypothesis in a large-scale, online, between-subjects design experiment, in which participants learned a new quantifier, gleeb. Gleeb corresponded to one of the following quantifiers: monotone, convex, and quantitative at least 3 and at most 2, non-monotone and convex between 3 and 6, non-convex even number, non-quantitative the first 3, the last 3, conservative not all, and non-conservative not only. We found that all universals affected the speed of acquisition: participants learned conservative quantifiers faster than non-conservative quantifiers, quantitative quantifiers faster than non-quantitative quantifiers, and upward monotone quantifiers faster than non-monotone quantifiers (however, the lack of convexity did not affect the rate of learning more). In conclusion, our results provide evidence for the learnability hypothesis.