Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-03T13:03:16.920Z Has data issue: false hasContentIssue false
  • English
  • Français

Distributional learning aids linguistic category formation in school-age children

Published online by Cambridge University Press:  10 November 2017

Jessica HALL ,
Amanda OWEN VAN HORNE  and
Thomas FARMER
Jessica HALL*
Affiliation:
University of Iowa, Iowa City, Iowa, USA
Amanda OWEN VAN HORNE
Affiliation:
University of Delaware, Newark, Delaware, USA
Thomas FARMER
Affiliation:
University of Iowa, Iowa City, Iowa, USA
*
Address for correspondence: Jessica Hall, Communication Sciences & Disorders, University of Iowa, 250 Hawkins IA, Iowa City, Iowa 52240, United States. e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The goal of this study was to determine if typically developing children could form grammatical categories from distributional information alone. Twenty-seven children aged six to nine listened to an artificial grammar which contained strategic gaps in its distribution. At test, we compared how children rated novel sentences that fit the grammar to sentences that were ungrammatical. Sentences could be distinguished only through the formation of categories of words with shared distributional properties. Children's ratings revealed that they could discriminate grammatical and ungrammatical sentences. These data lend support to the hypothesis that distributional learning is a potential mechanism for learning grammatical categories in a first language.

Type
Brief Research Reports
Information
Journal of Child Language , Volume 45 , Issue 3 , May 2018 , pp. 717 - 735
Check for updates
Copyright
Copyright © Cambridge University Press 2017 

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.)

Footnotes

We thank Elizabeth Wonnacott for her assistance with Bayesian analyses, and found Wonnacott, Nation, and Brown (2017) particularly helpful for reporting and interpreting Bayes factors. We also thank Tim Arbisi-Kelm, Caitie Hilliard, Sarah O'Neill, and Elissa Newport for their help with stimuli creation. Research reported in this publication was supported by the National Institute On Deafness And Other Communication Disorders of the National Institutes of Health under Award Number F31DC015370. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

References

Ambridge, B., & Lieven, E. V. (2011). Child language acquisition: contrasting theoretical approaches. Cambridge University Press.Google Scholar
Ambridge, B., Pine, J. M., & Rowland, C. F. (2011). Children use verb semantics to retreat from overgeneralization errors: a novel verb grammaticality judgment study. Cognitive Linguistics, 22(2), 303–23.Google Scholar
Ambridge, B., Pine, J. M., & Rowland, C. F. (2012). Semantics versus statistics in the retreat from locative overgeneralization errors. Cognition, 123(2), 260–79.Google Scholar
Ambridge, B., Pine, J. M., Rowland, C. F., & Chang, F. (2012). The roles of verb semantics, entrenchment, and morphophonology in the retreat from dative argument-structure overgeneralization errors. Language, 88(1), 45–81.Google Scholar
Ambridge, B., Pine, J. M., Rowland, C. F., & Young, C. R. (2008). The effect of verb semantic class and verb frequency (entrenchment) on children's and adults’ graded judgements of argument-structure overgeneralization errors. Cognition, 106(1), 87–129.Google Scholar
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: keep it maximal. Journal of Memory and Language, 68, 255–78.Google Scholar
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2016). lme4: linear mixed-effects models using Eigen and S4. R package version 1.1-12. Online: <http://CRAN.R-project.org/package=lme4>..>Google Scholar
Childers, J. B., & Tomasello, M. (2001). The role of pronouns in young children's acquisition of the English transitive construction. Developmental Psychology, 37(6), 739–48.Google Scholar
Dienes, Z. (2008). Understanding psychology as a science: an introduction to scientific and statistical inference. New York: Palgrave Macmillan.Google Scholar
Dienes, Z. (2014). Using Bayes to get the most out of non-significant results. Frontiers in Psychology, 5, 781.Google Scholar
Dienes, Z. (2016). How Bayes factors change scientific practice. Journal of Mathematical Psychology, 72, 78–89.Google Scholar
Dunn, L. M., & Dunn, D. M. (2007). PPVT-4: Peabody picture vocabulary test. Minneapolis, MN: Pearson Assessments.Google Scholar
Ebbels, S. H. (2005). Argument structure in specific language impairment: from theory to therapy. Unpublished doctoral dissertation, University of London.Google Scholar
Farmer, T. A., Christiansen, M. H., & Monaghan, P. (2006). Phonological typicality influences on-line sentence comprehension. Proceedings of the National Academy of Sciences, 103, 12203–08.CrossRefGoogle ScholarPubMed
Frigo, L., & McDonald, J. L. (1998). Properties of phonological markers that affect the acquisition of gender-like subclasses. Journal of Memory and Language, 39(2), 218–45.Google Scholar
Gerken, L., Wilson, R., & Lewis, W. (2005). Infants can use distributional cues to form syntactic categories. Journal of Child Language, 32(2), 249–68.Google Scholar
Gómez, R. L. (2002). Variability and detection of invariant structure. Psychological Science, 13(5), 431–6.CrossRefGoogle ScholarPubMed
Kaufman, A. S., & Kaufman, N. L. (2004). K-BIT-2: Kaufman brief intelligence test, 2nd ed. Circle Pines, MN: American Guidance Service, Inc.Google Scholar
Kidd, E. (2012). Implicit statistical learning is directly associated with the acquisition of syntax. Developmental Psychology, 48(1), 171–84.Google Scholar
Knowlton, B. J., & Squire, L. R. (1994). The information acquired during artificial grammar learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(1), 79–91.Google Scholar
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2016). lmerTest: tests in linear mixed effects models. R package version 2.0-32. Online: <https://cran.r-project.org/web/packages/lmerTest/index.html>..>Google Scholar
Lany, J., & Saffran, J. R. (2011). Interactions between statistical and semantic information in infant language development. Developmental Science, 14(5), 1207–19.Google Scholar
Mintz, T. H. (2003). Frequent frames as a cue for grammatical categories in child directed speech. Cognition, 90(1), 91–117.Google Scholar
Mintz, T. H., Newport, E. L., & Bever, T. G. (2002). The distributional structure of grammatical categories in speech to young children. Cognitive Science, 26(4), 393–424.Google Scholar
Misyak, J. B., & Christiansen, M. H. (2012). Statistical learning and language: an individual differences study. Language Learning, 62(1), 302–31.Google Scholar
Misyak, J. B., Christiansen, M. H., & Tomblin, J. B. (2010). On-line individual differences in statistical learning predict language processing. Frontiers in Language Sciences, 1:31.Google Scholar
Monaghan, P., Chater, N., & Christiansen, M. H. (2005). The differential role of phonological and distributional cues in grammatical categorisation. Cognition, 96(2), 143–82.Google Scholar
Monaghan, P., Christiansen, M. H., & Chater, N. (2007). The phonological-distributional coherence hypothesis: cross-linguistic evidence in language acquisition. Cognitive Psychology, 55(4), 259–305.Google Scholar
Redington, M., Chater, N., & Finch, S. (1998). Distributional information: a powerful cue for acquiring syntactic categories. Cognitive Science, 22(4), 425–69.CrossRefGoogle Scholar
Reeder, P. A., Newport, E. L., & Aslin, R. N. (2013). From shared contexts to syntactic categories: the role of distributional information in learning linguistic form-classes. Cognitive Psychology, 66(1), 30–54.Google Scholar
Rouder, J. N. (2014). Optional stopping: no problem for Bayesians. Psychonomic Bulletin & Review, 21, 301–8.Google Scholar
Siegelman, N., & Frost, R. (2015). Statistical learning as an individual ability: theoretical perspectives and empirical evidence. Journal of Memory and Language, 81, 105–20.Google Scholar
Smith, K. H. (1966). Grammatical intrusions in the free recall of structured letter pairs. Journal of Verbal Learning and Verbal Behavior, 5(5), 447–54.Google Scholar
Smith, K. H. (1969). Learning co-occurrence restrictions: Rule induction or rote learning? Journal of Verbal Learning and Verbal Behavior, 8(2), 319–21.Google Scholar
St. Clair, M. C. S., Monaghan, P., & Christiansen, M. H. (2010). Learning grammatical categories from distributional cues: flexible frames for language acquisition. Cognition, 116(3), 341–60.Google Scholar
Thorpe, K., & Fernald, A. (2006). Knowing what a novel word is not: two-year-olds ‘listen through’ ambiguous adjectives in fluent speech. Cognition, 100(3), 389–433.Google Scholar
Tomasello, M. (2000). The item-based nature of children's early syntactic development. Trends in Cognitive Sciences, 4, 156–63.Google Scholar
Tomasello, M. (2003). Constructing a language: a usage-based theory of language acquisition. Cambridge, MA: Harvard University Press.Google Scholar
Wonnacott, E., Brown, H., & Nation, K. (2017). Skewing the evidence: the effect of input structure on child and adult learning of lexically based patterns in an artificial language. Journal of Memory and Language, 95, 36–48.Google Scholar
Xu, R. (2003). Measuring explained variation in linear mixed effects models. Statistics in Medicine, 22(22), 3527–41.Google Scholar