The influence of contextual variability on word learning

Brendan JohnsQueen's University, Kingston, On, Canada
Melody DyeIndiana University, Bloomington
Michael JonesIndiana University, Bloomington


In a series of analyses over mega datasets, Jones, Johns, & Recchia (2012) and Johns et al. (2012) found that a measure of semantic distinctiveness (SD), which takes into account the semantic variability of a word’s contexts, provides a better fit to both visual and spoken word data than traditional measures, such as word frequency or raw context counts. The present study offers strong empirical support for this account’s extensibility to natural language. In a self-paced reading experiment, subjects were incidentally exposed to novel words as they rated short selections from articles, books, and newspapers. When novel words were encountered across distinct discourse contexts, subjects were both faster and more accurate at recognizing them than when they are seen in redundant contexts. However, learning across redundant contexts promoted the development of more stable semantic representations. These findings are predicted by a model of SD trained on the same materials as our subjects.


The influence of contextual variability on word learning (358 KB)

Back to Table of Contents