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Correlation versus prediction in children's word learning: Cross-linguistic evidence and simulations

Published online by Cambridge University Press:  11 March 2014

Eliana Colunga ,
Linda B. Smith  and
Michael Gasser
Eliana Colunga*
Affiliation:
University of Coloradoat Boulder Indiana University
Linda B. Smith*
Affiliation:
University of Coloradoat Boulder Indiana University
Michael Gasser*
Affiliation:
University of Coloradoat Boulder Indiana University
*
Correspondence addresses: Eliana Colunga, Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado, 80309-0345, USA. E-mail: [email protected].
Correspondence addresses: Linda B. Smith, Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405-7007, USA. E-mail: [email protected].
Correspondence addresses: Michael Gasser, Computer Science Department, Indiana University, Bloomington, Indiana 47405-7104, USA. E-mail: [email protected].
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Abstract

The ontological distinction between discrete individuated objects and continuous substances, and the way this distinction is expressed in different languages has been a fertile area for examining the relation between language and thought. In this paper we combine simulations and a cross-linguistic word learning task as a way to gain insight into the nature of the learning mechanisms involved in word learning. First, we look at the effect of the different correlational structures on novel generalizations with two kinds of learning tasks implemented in neural networks—prediction and correlation. Second, we look at English- and Spanish-speaking 2-3-year-olds' novel noun generalizations, and find that count/mass syntax has a stronger effect on Spanish- than on English-speaking children's novel noun generalizations, consistent with the predicting networks. The results suggest that it is not just the correlational structure of different linguistic cues that will determine how they are learned, but the specific learning mechanism and task in which they are involved.

Keywords

word learningcrosslinguisticmass/count syntaxneural networksprediction/correlation
Type
Research Article
Information
Language and Cognition , Volume 1 , Issue 2 , June 2009 , pp. 197 - 217
Copyright
Copyright © UK Cognitive Linguistics Association 2009

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References

Billman, D. 1989. Systems of correlations in rule and category learning: Use of structured input in learning syntactic categories. Language and Cognitive Processing 4. 127–155.Google Scholar
Billman, D. & Knutson, J.. 1996. Unsupervised concept learning and value systematicity: A complex whole aids learning the parts. Journal of Experimental Psychology: Learning, Memory, and Cognition 22. 458–475.Google Scholar
Bloom, L. & Tinker, E.. 2001. The intentionality model and language acquisition: Engagement, effort, and the essential tension in development. Monographs of the Society for Research in Child Development 66(4). 1–89.CrossRefGoogle ScholarPubMed
Bott, L., Hoffman, A. B. & Murphy, G. L.. 2007. Blocking in category learning. Journal of Experimental Psychology: General 136(4). 685–699.CrossRefGoogle ScholarPubMed
Colunga, E. & Smith, L. B.. 2005. From the lexicon to expectations about kinds: A role for associative learning. Psychological Review 112(2). 347–382.Google Scholar
Gathercole, V. M. C.. 1997. The linguistic mass/count distinction as an indicator of referent categorization in monolingual and bilingual children. Child Development 68(5). 832–842.Google Scholar
Gathercole, V. C. M., Evans, D. & Thomas, E. M.. 2000. What's in a noun? Welsh-, English-, and Spanish-speaking children see it differently. First Language 20(58). 55–90.Google Scholar
Gleitman, L. 1990. The structural sources of verb meanings. Language Acquisition: A Journal of Developmental Linguistics 1(1). 3–55.CrossRefGoogle Scholar
Gordon, P. 1985. Evaluating the semantic categories hypothesis: The case of he count/mass distinction. Cognition 20. 209–242.CrossRefGoogle Scholar
Iannucci, J. E. 1952. Lexical number in Spanish nouns with reference to their English equivalents. Philadelphia: University of Pennsylvania.Google Scholar
Imai, M. & Mazuka, R.. 2007. Language-relative construal of individuation constrained by universal ontology: Revisiting language universals and linguistic relativity. Cognitive Science 31(3). 385–413.Google Scholar
Japkowicz, N. & Fisher, D. H.. 2001. Supervised versus unsupervised binary-learning by feedforward neural networks. Machine Learning 42(1–2). 97–122.Google Scholar
Krushke, J. K. 1993. Human category learning: Implications for backpropagation models. Connection Science 5(1). 3–36.CrossRefGoogle Scholar
Kruschke, J. K. & Blair, N. J.. 2000. Blocking and backward blocking involve learned inattention. Psychonomic Bulletin and Review 7(4). 636–645.CrossRefGoogle ScholarPubMed
Krushke, J.K. 2001. Toward a unified model of attention in associative learning. Journal of Mathematical Psychology 45(6). 812–863.CrossRefGoogle Scholar
Landauer, T. K. & Dumais, S. T.. 1997. A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review 104(2). 211–240.Google Scholar
Li, P., MacWhinney, B. & Zhao, X.. 2007. Dynamic self-organization and early lexical development in children. Cognitive Science 31(4). 581–612.Google Scholar
Lidz, J., Gleitman, H. & Gleitman, L.. 2003. Understanding how input matters: Verb learning and the footprint of universal grammar. Cognition 87(3). 151–178.Google Scholar
Love, B. C. 2002. Comparing supervised and unsupervised category learning. Psychonomic Bulletin and Review 9(4). 829–835.Google Scholar
Love, B. C., Gureckis, T. M. & Medin, D. L.. 2004. SUSTAIN: A network of category learning. Psychological Review 111(2). 309–332.Google Scholar
McClelland, J. & Rumelhart, D.. 1981. An interactive activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review 88. 375–407.CrossRefGoogle Scholar
Miikkulainen, R. 1997. Dyslexic and category-specific aphasic impairments in a self-organizing feature map model of the lexicon. Brain and Language 59(2). 334–366.Google Scholar
Minda, J. P. & Ross, B. H.. 2004. Learning categories by making predictions: An investigation of indirect category learning. Memory & Cognition 32(8).CrossRefGoogle ScholarPubMed
Quine, Q. V. 1969. Onological relativity and other essays. New York: Columbia Univerisity Press.Google Scholar
Regier, T. 2005. The emergence of words: Attentional learning in form and meaning. Cognitive Science 29. 819–865.Google Scholar
Rescorla, R. A. & Wagner, A. R.. 1972. A theory of pavlovian conditioning: Variations in the effectiveness of reinforcement and non-reinforcemnt. In Black, A. H. & Prokasy, W. F. (eds.), Classical conitioning. II. Current research and theory, 64–99. New York: Appleton-Centure-Crofts.Google Scholar
Rogers, T. & McClelland, J.. 2004. Semantic cognition: A parallel distributed processing approach. Bradford Books.CrossRefGoogle Scholar
Rumelhart, D. E., Hinton, G. & Williams, R.. 1986. Learning internal representations by error propagation. In Rumelhart, D. E. & McClelland, J. L. (eds.), Parallel distributed processing vol. 1, 318–364. Cambridge, MA: MIT Press.Google Scholar
Samuelson, L. & Smith, L. B.. 1999. Early noun vocabularies: Do ontology, category structure and syntax correspond?Cognition. 1–33.Google Scholar
Sandhofer, C. M., Luo, J. & Smith, L. B.. 2001. Counting nouns and verbs in the input: Differential frequencies, different kind of learning? Journal of Child Language 27. 561–585.Google Scholar
Soja, N. N. 1992. Inferences about the meaning of nouns; the relationship between perception and syntax. Cognitive Development 7. 29–45.Google Scholar
Soja, N. N., Carey, S. & Spelke, E. S.. 1991. Ontological categories guide young children's inductions of word meanings: Object terms and substance terms. Cognition 38. 179–211.Google Scholar
Smith, L. B. 2000a. Learning how to learn words: An associative crane. In Golinkof, R. M., Akhtar, N., Bloom, L., Hirsh-Pasek, K., Hollich, G., Smith, L. B., Tomasello, M., Woodward, A. L. (eds.), Becoming a word learner, A debate on lexical acquisition. New York, NY: Oxford University Press.Google Scholar
Smith, L. B. 2000b. Avoiding associations when it's behaviorism you really hate. In Golinkof, R. M., Akhtar, N., Bloom, L., Hirsh-Pasek, K., Hollich, G., Smith, L. B., Tomasello, M., Woodward, A. L. (eds.), Becoming a word learner, A debate on lexical acquisition. New York, NY: Oxford University Press.Google Scholar
Smith, L. B., Colunga, E. & Yoshida, H.. 2003. Making an ontology: Cross-linguistic evidence. In Oakes, L. & Rakison, D. (eds.), Early category and concept development making sense of the blooming, buzzing confusion, 275–302. Oxford: Oxford University Press.Google Scholar
Tang, Z., Ishii, M., Tamura, H. & Wang, X.. 2003. An algorithm of supervised learning for multilayer neural networks. Neural Computation 15(5). 97–122.Google Scholar
Yoshida, H. & Smith, L. B.. 2003a. Shifting ontological boundaries: How Japanese- and English-speaking children generalize names for animals and artifacts. Developmental Science 6(1). 1–17.Google Scholar
Yoshida, H. & Smith, L. B.. 2003b. Response: Correlation, concepts and cross-linguistic differences. Developmental Science 6(1). 30–34.Google Scholar