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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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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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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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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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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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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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Corresponding author:
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In: http://www.cstr.ed.ac.uk/downloads/publications/2005/jml.pdf (2005)
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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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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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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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lexical categories
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In: http://cnl.psych.cornell.edu/pubs/2009-coh-Dev-Science.pdf
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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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Abstract:
What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses. These findings suggest that mechanisms of statistical sequential learning are implicated in language, and experience with language may affect cognitive processes and later learning.
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Keyword:
corpus analyses
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.470.147 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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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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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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