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.
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.
Entities:
Keywords:
neighborhood density; phonotactic probability; spline regression; vocabulary; word learning