Monotone Quantifiers Emerge via Iterated Learning.

Carcassi, Fausto; Steinert-Threlkeld, Shane; Szymanik, Jakub

Carcassi, Fausto; Steinert-Threlkeld, Shane; Szymanik, Jakub.

Afiliación

Carcassi F; Department of Linguistics, University of Amsterdam.
Steinert-Threlkeld S; Department of Linguistics, University of Washington.
Szymanik J; Department of Linguistics, University of Amsterdam.

Cogn Sci ; 45(8): e13027, 2021 08.

Article en En | MEDLINE | ID: mdl-34379338

RESUMEN

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.

Asunto(s)

Evolución Cultural; Aprendizaje; Humanos; Lenguaje; Redes Neurales de la Computación; Semántica

Palabras clave

Cultural evolution; Generalized quantifiers; Iterated learning; Neural networks; Semantic universals

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Evolución Cultural / Aprendizaje Límite: Humans Idioma: En Revista: Cogn Sci Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos

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