Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-21T00:10:57.898Z Has data issue: false hasContentIssue false
  • English
  • Français

Prediction plays a key role in language development as well as processing

Published online by Cambridge University Press:  24 June 2013

Matt A. Johnson ,
Nicholas B. Turk-Browne  and
Adele E. Goldberg
Matt A. Johnson
Affiliation:
Department of Psychology, Princeton University, Princeton, NJ 08544. majthree@princeton.eduwww.princeton.edu/ntblabntb@princeton.edu
Nicholas B. Turk-Browne
Affiliation:
Department of Psychology, Princeton University, Princeton, NJ 08544. majthree@princeton.eduwww.princeton.edu/ntblabntb@princeton.edu
Adele E. Goldberg
Affiliation:
Program in Linguistics, Princeton University, Princeton, NJ 08544. adele@princeton.eduwww.princeton.edu/~adele
Get access Rights & Permissions [Opens in a new window]

Abstract

Although the target article emphasizes the important role of prediction in language use, prediction may well also play a key role in the initial formation of linguistic representations, that is, in language development. We outline the role of prediction in three relevant language-learning domains: transitional probabilities, statistical preemption, and construction learning.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 4 , August 2013 , pp. 360 - 361
Copyright
Copyright © Cambridge University Press 2013 

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.)

References

Aronoff, M. (1976) Word formation in generative grammar. Linguistic Inquiry Monograph 1. MIT Press.Google Scholar
Bencini, G. M. & Goldberg, A. E. (2000) The contribution of argument structure constructions to sentence meaning. Journal of Memory & Language 43:640–51.CrossRefGoogle Scholar
Boyd, J. K. & Goldberg, A. E. (2011) Learning what not to say: Categorization and statistical preemption in “a-adjective” production. Language 87:1–29.CrossRefGoogle Scholar
Brooks, P. J. & Tomasello, M. (1999) How children constrain their argument structure constructions. Language 75(4):720–38.Google Scholar
Conway, C. M., Bauernschmidt, A., Huang, S. S. & Pisoni, D. B. (2010) Implicit statistical learning in language processing: Word predictability is the key. Cognition 114:356–71.Google Scholar
Elman, J. L. (1991) Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning 7:195–225.Google Scholar
Elman, J. L. (1993) Learning and development in neural networks: The importance of starting small. Cognition 48:71–99.Google Scholar
French, R. M., Addyman, C. & Mareschal, D. (2011) TRACX: A recognition-based connectionist framework for sequence segmentation and chunk extraction. Psychological Review 118:614–636.Google Scholar
Goldberg, A. E. (1995) Constructions: A construction grammar approach to argument structure. University of Chicago Press.Google Scholar
Goldberg, A. E. (2006) Constructions at work: The nature of generalization in language. Oxford University Press.Google Scholar
Goldberg, A. E. (2011) Corpus evidence of the viability of statistical preemption. Cognitive Linguistics 22:131–54.Google Scholar
Goldberg, A. E., Casenhiser, D. M. & Sethuraman, N. (2005) The role of prediction in construction-learning. Journal of Child Language 32:407–26.Google Scholar
Gomez, R. L. & Gerken, L. (1999) Artificial grammar learning by 1-year-olds leads to specific and abstract knowledge. Cognition 70:109–35.Google Scholar
Graf Estes, K., Evans, J. L., Alibali, M. W. & Saffran, J. R. (2007) Can infants map meaning to newly segmented words? Psychological Science 18:254–60.CrossRefGoogle ScholarPubMed
Hay, J. F., Pelucchi, B., Graf Estes, K. & Saffran, J. R. (2011) Linking sounds to meaning: Infant statistical learning in a natural language. Cognitive Psychology 63:93–106.Google Scholar
Kiparsky, P. (1982) Lexical morphology and phonology. In: Linguistics in the Morning Calm, ed. Yang, I.-S., pp. 3–91. Hanshin.Google Scholar
Lewis, J. D. & Elman, J. L. (2001) Learnability and the statistical structure of language: Poverty of stimulus arguments revisited. Proceedings of the 26th Annual Conference on Language Development.Google Scholar
Marcotte, J. (2005) Causative alternation errors as event-driven construction paradigm completions . Stanford, Ph.D. dissertation.Google Scholar
Mirman, D., Magnuson, J., Graf Estes, K. & Dixon, J. A. (2008) The link between statistical segmentation and word learning in adults. Cognition 108:271–80.Google Scholar
Misyak, J. B., Christiansen, M. H. & Tomblin, J. B. (2010) Sequential expectations: The role of prediction-based learning in language. Topics in Cognitive Science 2:138–53.Google Scholar
Niv, Y. & Montague, P. R. (2008) Theoretical and empirical studies of learning. Neuroeconomics: Decision making and the brain, pp. 329–50. Elsevier.Google Scholar
O'Doherty, J. P., Dayan, P., Schultz, J., Deichmann, R., Friston, K. & Dolan, R. (2004) Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304:452–54.Google Scholar
Pagnoni, G., Zink, C. F., Montague, P. R. & Berns, G. S. (2002) Activity in human ventral striatum locked to errors of reward prediction. Nature Neuroscience 5:97–98.CrossRefGoogle ScholarPubMed
Rescorla, R. A. & Wagner, A. R. (1972) A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In: Classical conditioning II, Black, A. H. & Prokasy, F., pp. 64–99. Appleton-Century-Crofts.Google Scholar
Saffran, J. R. (2002) Constraints on statistical language learning. Journal of Memory and Language 47:172–96.CrossRefGoogle Scholar
Saffran, J. R., Aslin, R. N. & Newport, E. L. (1996) Statistical learning by 8-month-old infants. Science 274:1926–28.CrossRefGoogle ScholarPubMed
Suttle, L. & Goldberg, A. E. (forthcoming) Learning what not to say: Comparing the role of preemption and entrenchment. Princeton University.Google Scholar
Turk-Browne, N. B., Scholl, B. J., Johnson, M. K. & Chun, M. M. (2010) Implicit perceptual anticipation triggered by statistical learning. Journal of Neuroscience 30:11177–87.CrossRefGoogle ScholarPubMed
Wolpert, D. M. (1997) Computational approaches to motor control. Trends in Cognitive Sciences 1:209–16.Google Scholar
Wolpert, D. M., Ghahramani, Z. & Flanagan, J. R. (2001) Perspectives and problems in motor learning. Trends in Cognitive Sciences 5:487–94.Google Scholar