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ACQDIV

Financiado

Cerrado

Acquisition processes in maximally diverse languages Min d ing the ambient lang...

Acquisition processes in maximally diverse languages Min d ing the ambient language "Children learn any language that they grow up with, adapting to any of the ca. 7000 languages of the world, no matter how divergent or complex their structures are. What cognitive processes make this extreme flexibility possible?... ver más

31/08/2019

UZH

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

University of Zurich No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2019-08-31 No tenemos la información de la convocatoria

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UZH

2.00M€ | Lider

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Líder del proyecto

University of Zurich No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "Children learn any language that they grow up with, adapting to any of the ca. 7000 languages of the world, no matter how divergent or complex their structures are. What cognitive processes make this extreme flexibility possible? This is one of the most burning questions in cognitive science and the ACQDIV project aims at answering it by testing and refining the following leading hypothesis: Language acquisition is flexible and adaptive to any kind of language because it relies on a small set of universal cognitive processes that variably target different structures at different times during acquisition in every language. The project aims at establishing the precise set of processes and at determining the conditions of variation across maximally diverse languages. This project focuses on three processes: (i) distributional learning, (ii) generalization-based learning and (iii) interaction-based learning. To investigate these processes I will work with a sample of five clusters of languages including longitudinal data of two languages each. The clusters were determined by a clustering algorithm seeking the structurally most divergent languages in a typological database. The languages are: Cluster 1: Slavey and Cree, Cluster 2: Indonesian and Yucatec, Cluster 3: Inuktitut and Chintang, Cluster 4: Sesotho and Russian, Cluster 5: Japanese and Turkish. For all languages, corpora are available, except for Slavey where fieldwork is planned. The leading hypothesis will be tested against the acquisition of aspect and negation in each language of the sample and also against the two structures in each language that are most salient and challenging in them (e. g. complex morphology in Chintang). The acquisition processes also depend on statistical patterns in the input children receive. I will examine these patterns across the sample with respect to repetitiveness effects, applying data-mining methods and systematically comparing child-directed and child-surrounding speech."

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