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B. McMurray, Jessica Horst, Joseph Toscano, Larissa Samuelson (2009)
Connectionist learning and dynamic processing:Symbiotic developmental mechanisms
Morten Christiansen, Luca Onnis, S. Hockema (2009)
The secret is in the sound: from unsegmented speech to lexical categories.Developmental science, 12 3
Noam Chomsky (1981)
Lectures on Government and Binding
M.H. Christiansen, L. Onnis, S.A. Hockema (2009)
The secret is in the sound: from unsegmented speech to lexical categoriesPsychological Science, 12
J. Berger, W. Jefferys (1992)
Ockham's Razor and Bayesian Analysis, 80
G. Hollich, C.G. Prince (2009)
Comparing infants’ preference for correlated audiovisual speech with signal‐level computational modelsAmerican Scientist, 12
D. McNamara, J. Trafton (2007)
Proceedings of the 29th Annual Cognitive Science Society
B. McMurray, R. Aslin, Joseph Toscano (2009)
Statistical learning of phonetic categories: insights from a computational approach.Developmental science, 12 3
Jessica Maye, J. Werker, L. Gerken (2002)
Infant sensitivity to distributional information can affect phonetic discriminationCognition, 82
J. Goldberg, G. Schoner (2007)
Understanding the Distribution of Infant Attention: A Dynamical Systems Approach, 29
M. Brent (1999)
An Efficient, Probabilistically Sound Algorithm for Segmentation and Word DiscoveryMachine Learning, 34
G. Hollich, C. Prince (2009)
Comparing infants' preference for correlated audiovisual speech with signal-level computational models.Developmental science, 12 3
J. Spencer, M. Thomas, J. Mcclelland, K. Newell, J. Spencer, Jeff Johnson, J. Lipinski, M. Schlesinger, Y. Munakata, Linda Smith, D. Corbetta, H. Maas, M. Raijmakers, D. Mareschal, R. Leech, Rick Cooper, B. McMurray, Jessica Horst, Larissa Samuelson, G. Orden, H. Kloos, L. Oakes, N. Newcombe, J. Plumert, Tim Johnston, J. Mcclelland, G. Schöner, Fiona Richardson, K. Fischer, James McClelland, Gautam Vallabha (2009)
Toward a New Grand Theory of Development? Connectionism and Dynamic Systems Theory Reconsidered
(2009)
Short arms and talking eggs : the inconvenience of understanding process
R.L. Gómez (2002)
Variability and detection of invariant structureJournal of Child Language, 13
G. Schöner, E. Thelen (2006)
Using dynamic field theory to rethink infant habituation.Psychological review, 113 2
S. Perone, J. Spencer, G. Schoner (2007)
A Dynamic Field Theory of Visual Recognization in Infant Looking Tasks, 29
E. Chemla, Toben Mintz, Savita Bernal, A. Christophe (2009)
Categorizing words using 'frequent frames': what cross-linguistic analyses reveal about distributional acquisition strategies.Developmental science, 12 3
J. Saffran, R. Aslin, E. Newport (1996)
Statistical Learning by 8-Month-Old InfantsScience, 274
Larissa Samuelson, Jessica Horst (2008)
Confronting complexity: insights from the details of behavior over multiple timescales.Developmental science, 11 2
R. Gómez (2002)
Variability and Detection of Invariant StructurePsychological Science, 13
Jan Goodsitt, James Morgan, Patricia Kuhl (1993)
Perceptual strategies in prelingual speech segmentationJournal of Child Language, 20
J. Goodsitt, J.L. Morgan, P.K. Kuhl (1993)
Perceptual strategies in prelingual speech segmentationDevelopmental Science, 20
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Statistical learning is the new paradigm of language acquisition. A perusal of recent conference programs or journal contents reveals much work advocating – or criticizing – statistical learning. Language acquisition will continue to benefit from a variety of theories and methods, but, as the articles in this issue exemplify, statistical learning has progressed from being a minor player towards a central role. To ensure a lasting impact, statistical approaches must now move from piecemeal demonstrations towards a general theory of language learning. Statistical learning stands in contrast to the predominant paradigm that it succeeded. The principles and parameters approach ( Chomsky, 1981 ) assumed rich innate endowment, limited processing abilities and impoverished input, whereas statistical learning assumes that input is rich and that learners possess sufficient computational sophistication to extract relevant linguistic patterns. Statistical learning models are attractive because in principle they recruit powerful, task‐general machinery to solve difficult problems of language acquisition. Furthermore, behavioural findings with both adults and infants suggest that humans use statistical learning in language‐like tasks ( Gómez, 2002 ; Goodsitt, Morgan & Kuhl, 1992 ; Maye, Werker & Gerken, 2002 ; Saffran, Aslin & Newport, 1996 , Saffran, Newport & Aslin, 1996 ,
Developmental Science – Wiley
Published: Jan 1, 2009
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