Hostname: page-component-77f85d65b8-2tv5m Total loading time: 0 Render date: 2026-03-28T00:53:47.787Z Has data issue: false hasContentIssue false
  • English
  • Français

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 Psychology University of Southern California, 3620 McClintock Ave, Los Angeles, CA 90089-1061. wang970@usc.edu tmintz@usc.edu http://dornsife.usc.edu/tobenmintz
Toben H. Mintz
Affiliation:
Department of Psychology University of Southern California, 3620 McClintock Ave, Los Angeles, CA 90089-1061. wang970@usc.edu tmintz@usc.edu http://dornsife.usc.edu/tobenmintz
Get access Rights & Permissions [Opens in a new window]

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.

Information

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

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