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  1.  81
    Learn Locally, Act Globally: Learning Language from Variation Set Cues.Luca Onnis,Heidi R. Waterfall &Shimon Edelman -2008 -Cognition 109 (3):423.
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  2.  37
    Social Network Limits Language Complexity.Matthew Lou-Magnuson &Luca Onnis -2018 -Cognitive Science 42 (8):2790-2817.
    Natural languages vary widely in the degree to which they make use of nested compositional structure in their grammars. It has long been noted by linguists that the languages historically spoken in small communities develop much deeper levels of compositional embedding than those spoken by larger groups. Recently, this observation has been confirmed by a robust statistical analysis of the World Atlas of Language Structures. In order to examine this connection mechanistically, we propose an agent‐based model that accounts for key (...) cultural evolutionary features of language transfer and language change. We identify transitivity as a physical parameter of social networks critical for the evolution of compositional structure and the hierarchical patterning of scale‐free distributions as inhibitory. (shrink)
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  3.  39
    Language experience changes subsequent learning.Luca Onnis &Erik Thiessen -2013 -Cognition 126 (2):268-284.
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  4.  24
    Is the Mind Inherently Predicting? Exploring Forward and Backward Looking in Language Processing.Luca Onnis,Alfred Lim,Shirley Cheung &Falk Huettig -2022 -Cognitive Science 46 (10):e13201.
    Cognitive Science, Volume 46, Issue 10, October 2022.
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  5.  38
    Lexical Categories at the Edge of the Word.Luca Onnis &Morten H. Christiansen -2008 -Cognitive Science 32 (1):184-221.
    Language acquisition may be one of the most difficult tasks that children face during development. They have to segment words from fluent speech, figure out the meanings of these words, and discover the syntactic constraints for joining them together into meaningful sentences. Over the past couple of decades, computational modeling has emerged as a new paradigm for gaining insights into the mechanisms by which children may accomplish these feats. Unfortunately, many of these models assume a computational complexity and linguistic knowledge (...) likely to be beyond the abilities of developing young children. This article shows that, using simple statistical procedures, significant correlations exist between the beginnings and endings of a word and its lexical category in English, Dutch, French, and Japanese. Therefore, phonetic information can contribute to individuating higher level structural properties of these languages. This article also presents a simple 2‐layer connectionist model that, once trained with an initial small sample of words labeled for lexical category, can infer the lexical category of a large proportion of novel words using only word‐edge phonological information, namely the first and last phoneme of a word. The results suggest that simple procedures combined with phonetic information perceptually available to children provide solid scaffolding for emerging lexical categories in language development. (shrink)
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  6.  22
    The bottleneck may be the solution, not the problem.Arnon Lotem,Oren Kolodny,Joseph Y. Halpern,Luca Onnis &Shimon Edelman -2016 -Behavioral and Brain Sciences 39:e83.
    As a highly consequential biological trait, a memory “bottleneck” cannot escape selection pressures. It must therefore co-evolve with other cognitive mechanisms rather than act as an independent constraint. Recent theory and an implemented model of language acquisition suggest that a limit on working memory may evolve to help learning. Furthermore, it need not hamper the use of language for communication.
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  7.  14
    Technology-Based Tools for English Literacy Intervention: Examining Intervention Grain Size and Individual Differences.Beth A. O’Brien,Malikka Habib &Luca Onnis -2019 -Frontiers in Psychology 10.
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