Year: 2021
Type: article
Abstract: Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.
Source: Cognitive Science
DOI: 10.1111/cogs.13027
Institutions: University of Amsterdam, University of Washington
Language: English
Subfield: Cultural Studies
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,100
OpenAlex ID: W3188901289