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Similar neural correlates for language and sequential learning: Evidence from event-related brain potentials
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In: http://cnl.psych.cornell.edu/pubs/2012-cco-LCP.pdf (2012)
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| 2 |
Toward a New Scientific Visualization for the Language Sciences
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In: Information ; Volume 3 ; Issue 1 ; Pages 124-150 (2012)
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An empirical generative framework for computational modeling of language acquisition
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In: http://www.wisdom.weizmann.ac.il/%7Eedelman/Waterfall-Sandbank-Onnis-Edelman-JCL10.pdf (2010)
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Lexical categories at the edge of the word
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In: http://www2.hawaii.edu/~lucao/papers/OnnisChristiansen2008.pdf (2008)
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| 5 |
1Variability is an important ingredient in learning
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In: http://bcl.wjh.harvard.edu/images/uploaded/File/Onnisetal-variability.pdf (2006)
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| 6 |
New beginnings and happy endings: Psychological plausibility in computational models of language acquisition
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In: http://csjarchive.cogsci.rpi.edu/Proceedings/2005/docs/p1678.pdf (2005)
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| 7 |
New beginnings and happy endings: Psychological plausibility in computational models of language acquisition
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In: http://www.psych.unito.it/csc/cogsci05/frame/talk/p807-onnis.pdf (2005)
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| 8 |
New beginnings and happy endings: Psychological plausibility in computational models of language acquisition
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In: http://cnl.psych.cornell.edu/pubs/2005-oc-CogSci.pdf (2005)
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| 9 |
Corresponding author:
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In: http://www.cstr.ed.ac.uk/downloads/publications/2005/jml.pdf (2005)
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Abstract:
Peña, Bonatti, Nespor, and Mehler (2002) investigated an artificial language where the structure of words was determined by nonadjacent dependencies between syllables. They found that segmentation of continuous speech could proceed on the basis of these dependencies. However, Peña et al.’s artificial language contained a confound in terms of phonology, in that the dependent syllables began with plosives and the intervening syllables began with continuants. We consider three hypotheses concerning the role of phonology in speech segmentation in this task: (1) participants may recruit probabilistic phonotactic information from their native language to the artificial language learning task; (2) phonetic properties of the stimuli, such as the gaps that precede unvoiced plosives, can influences segmentation; and (3) grouping by phonological similarity between dependent syllables contributes to learning the dependency. In a series of experiments controlling the phonological and statistical structure of the language, we found that segmentation performance is influenced by the three factors in different degrees. Learning of non-adjacent dependencies did not occur when (3) is eliminated. We
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URL: http://www.cstr.ed.ac.uk/downloads/publications/2005/jml.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.2289
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| 10 |
Variability is the spice of learning, and a crucial ingredient for detecting and generalizing in nonadjacent dependencies
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In: http://cnl.psych.cornell.edu/pubs/2004-OMCC-cogsci.pdf (2004)
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| 11 |
Reduction of uncertainty in human sequential learning: evidence from artificial language learning
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In: http://www.dectech.co.uk/publications/LinksNick/Language/Reduction of Uncertainty in Human Sequential Learning Eviden.pdf (2003)
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| 12 |
Using Phoneme Distributions to Discover Words and Lexical Categories in Unsegmented Speech
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In: http://cnl.psych.cornell.edu/pubs/2006-cho-cogsci.pdf
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| 13 |
lexical categories
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In: http://cnl.psych.cornell.edu/pubs/2009-coh-Dev-Science.pdf
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| 14 |
1 Language-induced Biases on Human Sequential Learning
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In: http://mindmodeling.org/cogsci2012/papers/0150/paper0150.pdf
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1 Acquisition and Evolution of Natural Language 134 Acquisition and Evolution of quasi-regular languages: Two puzzles for the price of one. Abstract
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In: http://cnl.psych.cornell.edu/papers/Roberts_Onnis_Chater.pdf
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References words: 843 Total words: 1,949 The Bottleneck May Be the Solution, Not the Problem
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In: http://kybele.psych.cornell.edu/%7Eedelman/Archive/Lotem-et-al-on-Christiansen-and-Chater-resubmitted.pdf
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| 17 |
Variation Sets Facilitate Artificial Language Learning
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In: http://www.wisdom.weizmann.ac.il/~edelman/OnnisWaterfallEdelman-variation-sets-CogSci08.pdf
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| 18 |
Variation Sets Facilitate Artificial Language Learning
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In: http://csjarchive.cogsci.rpi.edu/Proceedings/2008/pdfs/p1011.pdf
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