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Language acquisition is model-based rather than model-free

Published online by Cambridge University Press:  02 June 2016

Felix Hao Wang  and
Toben H. Mintz
Felix Hao Wang
Affiliation:
Department of PsychologyUniversity of Southern California, 3620 McClintock Ave, Los Angeles, CA 90089-1061. [email protected]@usc.eduhttp://dornsife.usc.edu/tobenmintz
Toben H. Mintz
Affiliation:
Department of PsychologyUniversity of Southern California, 3620 McClintock Ave, Los Angeles, CA 90089-1061. [email protected]@usc.eduhttp://dornsife.usc.edu/tobenmintz
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Abstract

Christiansen & Chater (C&C) propose that learning language is learning to process language. However, we believe that the general-purpose prediction mechanism they propose is insufficient to account for many phenomena in language acquisition. We argue from theoretical considerations and empirical evidence that many acquisition tasks are model-based, and that different acquisition tasks require different, specialized models.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 39 , 2016 , e89
Copyright
Copyright © Cambridge University Press 2016 

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References

Kam, X. N. C., Stoyneshka, I., Tornyova, L., Fodor, J. D. & Sakas, W. G. (2008) Bigrams and the richness of the stimulus. Cognitive Science 32(4):771–87.CrossRefGoogle ScholarPubMed
Mintz, T. H., Wang, F. H. & Li, J. (2014) Word categorization from distributional information: Frames confer more than the sum of their (Bigram) parts. Cognitive Psychology 75:1–27.CrossRefGoogle Scholar
Reali, F. & Christiansen, M. H. (2005) Uncovering the richness of the stimulus: Structure dependence and indirect statistical evidence. Cognitive Science 29(6):1007–28.CrossRefGoogle ScholarPubMed
Trueswell, J. C., Medina, T. N., Hafri, A. & Gleitman, L. R. (2013) Propose but verify: Fast mapping meets cross-situational word learning. Cognitive Psychology 66(1):126–56.CrossRefGoogle ScholarPubMed
Wang, F. H. & Mintz, T. H. (under revision) The limits of associative learning in cross-situational word learning.Google Scholar
Waxman, S. R. & Gelman, S. A. (2009) Early word-learning entails reference, not merely associations. Trends in Cognitive Sciences 13(6):258–63.CrossRefGoogle Scholar
Yu, C. & Smith, L. B. (2007) Rapid word learning under uncertainty via cross-situational statistics. Psychological Science 18(5):414–20.CrossRefGoogle ScholarPubMed
Yu, C., Smith, L. B., Klein, K. & Shiffrin, R. M. (2007) Hypothesis testing and associative learning in cross-situational word learning: Are they one and the same? In: Proceedings of the 29th Annual Conference of the Cognitive Science Society, Nashville, TN, August 2007, pp. 737–42, ed. McNamara, D. S. & Trafton, J. G.. Cognitive Science Society.Google Scholar