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Roete, Ingeborg; Frank, Stefan L.; Fikkert, Paula; Casillas, Marisa – Cognitive Science, 2020
We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child-caregiver interactions in English to test whether one proposed statistical learning mechanism--backward transitional probability--is able to predict children's speech productions with stable accuracy throughout the first few years of development. We…
Descriptors: Statistics, Linguistic Input, Children, Speech Communication
Unger, Layla; Vales, Catarina; Fisher, Anna V. – Cognitive Science, 2020
The organization of our knowledge about the world into an interconnected network of concepts linked by relations profoundly impacts many facets of cognition, including attention, memory retrieval, reasoning, and learning. It is therefore crucial to understand how organized semantic representations are acquired. The present experiment investigated…
Descriptors: Semantics, Role, Schemata (Cognition), Language Processing
Regier, Terry – Cognitive Science, 2005
Children improve at word learning during the 2nd year of life--sometimes dramatically. This fact has suggested a change in mechanism, from associative learning to a more referential form of learning. This article presents an associative exemplar-based model that accounts for the improvement without a change in mechanism. It provides a unified…
Descriptors: Associative Learning, Models, Semantics, Phonology