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Phonotactic Probability and Neighborhood Density on Word Learning by Preschool Children (Storkel et al., 2013)

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posted on 01.04.2013, 00:00 by Holly L. Storkel, Daniel E. Bontempo, Andrew J. Aschenbrenner, Junko Maekawa, Su-Yeon Lee
Purpose Phonotactic probability or neighborhood density has predominately been defined through the use of gross distinctions (i.e., low vs. high). In the current studies, the authors examined the influence of finer changes in probability (Experiment 1) and density (Experiment 2) on word learning.
Method The authors examined the full range of probability or density by sampling 5 nonwords from each of 4 quartiles. Three- and 5-year-old children received training on nonword–nonobject pairs. Learning was measured in a picture-naming task immediately following training and 1 week after training. Results were analyzed through the use of multilevel modeling.
Results A linear spline model best captured nonlinearities in phonotactic probability. Specifically, word learning improved as probability increased in the lowest quartile, worsened as probability increased in the mid-low quartile, and then remained stable and poor in the 2 highest quartiles. An ordinary linear model sufficiently described neighborhood density. Here, word learning improved as density increased across all quartiles.
Conclusion Given these different patterns, phonotactic probability and neighborhood density appear to influence different word learning processes. Specifically, phonotactic probability may affect recognition that a sound sequence is an acceptable word in the language and is a novel word for the child, whereas neighborhood density may influence creation of a new representation in long-term memory.

Funding

The project described was supported by National Institute on Deafness and Other Communication Disorders (NIDCD) Grants DC08095 and DC05803 and by National Institute of Child Health and Human Development Grant HD02528. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. The second author was supported by the Analytic Techniques and Technology Core of the Center for Biobehavioral Neurosciences of Communication Disorders ([BNCD] DC05803). We thank the staff of the Participant Recruitment and Management Core of the BNCD (supported by NIDCD Grant DC05803) for assistance with recruitment of preschools and children; the staff of the Word and Sound Learning Lab (supported by NIDCD Grant DC08095) for their contributions to stimulus creation, data collection, data processing, and reliability calculations; and the preschools, parents, and children who participated.

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