The generative power of human language, our ability to create an infinite variety of new words, phrases and sentences, depend critically on our ability to form implicit linguistic categories, both phonological and syntactic. For example, an adult having heard the sentence """"""""A snerg zugged"""""""" is immediately capable of generating """"""""is the snerg zugging?"""""""" without benefit of understanding the semantic content of either utterance. Creating the new utterance depends on implicitly treating """"""""snerg"""""""" and """"""""zug"""""""" as members of different lexical categories (i.e., noun and verb). The ability to group together into categories superficially distinct acoustic, lexical and phrasal tokens is key to language development in both symbolic and connectionist approaches (e.g., Elman, 1990; Guenther and Gjaja, 1996; Maye and Gerken, 1999; Pinker, 194; Valian and Coulson, 1988). Nevertheless, our understanding of the nature of these categories and how we form them is murky. Focusing, as this project will, on lexical categories, such as noun and verb, there are several questions that recur in the literature. These questions cluster along two dimensions: First, can abstract linguistic categories be induced from the input, or must the learner be born with some expectations about the category structure? Second, is the distribution of words across sentences sufficient for determining their category, or is referential information required in category formation? The proposed research locates itself at the intersection of these two dimensions. The goal of this research is to examine the limits on distributionally based category formation in an artificial language by adults and 12- to 18-month-old infants. The studies all ask the question: Under what conditions will learners generalize between training stimuli and test stimuli on the basis of distributional evidence?

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Research Project (R01)
Project #
5R01HD042170-02
Application #
6536444
Study Section
Special Emphasis Panel (ZRG1-BBBP-6 (04))
Program Officer
Mccardle, Peggy D
Project Start
2001-07-02
Project End
2004-06-30
Budget Start
2002-07-01
Budget End
2003-06-30
Support Year
2
Fiscal Year
2002
Total Cost
$238,613
Indirect Cost
Name
University of Arizona
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
City
Tucson
State
AZ
Country
United States
Zip Code
85721
Gonzales, Kalim; Gerken, LouAnn; Gómez, Rebecca L (2015) Does hearing two dialects at different times help infants learn dialect-specific rules? Cognition 140:60-71
Hawthorne, Kara; Mazuka, Reiko; Gerken, LouAnn (2015) The acoustic salience of prosody trumps infants' acquired knowledge of language-specific prosodic patterns. J Mem Lang 82:105-117
Gerken, LouAnn; Dawson, Colin; Chatila, Razanne et al. (2015) Surprise! Infants consider possible bases of generalization for a single input example. Dev Sci 18:80-9
Hawthorne, Kara; Gerken, LouAnn (2014) From pauses to clauses: prosody facilitates learning of syntactic constituency. Cognition 133:420-8
Lany, Jill; Gómez, Rebecca L (2013) Probabilistically-Cued Patterns Trump Perfect Cues in Statistical Language Learning. Lang Learn Dev 9:66-87
Dawson, Colin R; Gerken, LouAnn (2012) Can rational models be good accounts of developmental change? The case of language development at two time scales. Adv Child Dev Behav 43:95-124
Lindsey, Brittany A; Gerken, Louann (2012) The role of morphophonological regularity in young Spanish-speaking children's production of gendered noun phrases. J Child Lang 39:753-76
Richtsmeier, Peter; Gerken, Louann; Ohala, Diane (2011) Contributions of phonetic token variability and word-type frequency to phonological representations. J Child Lang 38:951-78
Gerken, LouAnn; Balcomb, Frances K; Minton, Juliet L (2011) Infants avoid 'labouring in vain' by attending more to learnable than unlearnable linguistic patterns. Dev Sci 14:972-9
Gerken, LouAnn (2010) Infants use rational decision criteria for choosing among models of their input. Cognition 115:362-6

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