ARCA è l'archivio istituzionale ad accesso aperto della ricerca dell'Università Ca' Foscari Venezia e nasce nel 2014 con lo scopo di raccogliere, diffondere e conservare la produzione scientifica dell'Università. Costituisce di fatto il catalogo della ricerca di Ateneo.Mentre il precedente U-Gov Catalogo era un database chiuso, ARCA è un sistema Open Access: è non solo consultabile da tutti e visibile nei motori di ricerca, ma anche interoperabile con il portale cerCa. In questo modo vengono valorizzate la produzione scientifica dell'Ateneo e la visibilità dei ricercatori.
EnglishTo learn a language, speakers must learn its words and rules from fluent speech; in particular, they must learn dependencies among linguistic classes. We show that when familiarized with a short artificial, subliminally bracketed stream, participants can learn relations about the structure of its words, which specify the classes of syllables occurring in first and last word positions. By studying the effect of familiarization length, we compared the general predictions of associative theories of learning and those of models postulating separate mechanisms for quickly extracting the word structure and for tracking the syllable distribution in the stream. As predicted by the dual-mechanism model, the preference for structurally correct items was negatively correlated with the familiarization length. This result is difficult to explain by purely associative schemes; an extensive set of neural network simulations confirmed this difficulty. Still, we show that powerful statistical computations operating on the stream are available to our participants, as they are sensitive to co-occurrence statistics among non-adjacent syllables. We suggest that different learning mechanisms analyze speech on-line: A rapid mechanism extracting structural information about the stream, and a slower mechanism detecting statistical regularities among the items occurring in it. (C) 2006 Elsevier B.V. All rights reserved.
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