Monotone Quantifiers Emerge via Iterated Learning
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Updated: Oct 25, 2025

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
Published on: June 1, 2015
Fausto Carcassi1, Shane Steinert-Threlkeld2, Jakub Szymanik1
1Department of Linguistics, University of Amsterdam.
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.
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