L
Luca Onnis
Researcher at University of Genoa
Publications - 55
Citations - 1065
Luca Onnis is an academic researcher from University of Genoa. The author has contributed to research in topics: Language acquisition & Implicit learning. The author has an hindex of 14, co-authored 49 publications receiving 966 citations. Previous affiliations of Luca Onnis include University of California, Merced & Nanyang Technological University.
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Learn locally, act globally: learning language from variation set cues.
TL;DR: The benefits of variation set structure directly are demonstrated directly: in miniature artificial languages, arranging a certain proportion of utterances in a training corpus in variation sets facilitated word and phrase constituent learning in adults.
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Phonology impacts segmentation in online speech processing
TL;DR: In this article, the role of phonology in speech segmentation was investigated in an artificial language where the structure of words was determined by non-adjacent dependencies between syllables, and it was shown that segmentation of continuous speech could proceed on the basis of these dependencies.
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Similar neural correlates for language and sequential learning: Evidence from event-related brain potentials
TL;DR: It is concluded that the same neural mechanisms may be recruited for both syntactic processing of linguistic stimuli and sequential learning of structured sequence patterns more generally.
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General cognitive principles for learning structure in time and space.
Michael H. Goldstein,Heidi Waterfall,Heidi Waterfall,Arnon Lotem,Joseph Y. Halpern,Jennifer A. Schwade,Luca Onnis,Shimon Edelman +7 more
TL;DR: It is proposed that a small set of principles are at work in every situation that involves learning of structure from patterns of experience and outline a general framework that accounts for such learning.
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An empirical generative framework for computational modeling of language acquisition
TL;DR: This paper reports progress in developing a computer model of language acquisition in the form of a generative grammar that is algorithmically learnable from realistic corpus data, viable in its large-scale quantitative performance and psychologically real.