Frequency has proved to be a robust predictor of performance across a wide variety of tasks. The notion that word frequency is a principal variable in how words are processed has been recognized in the psychological literature for more than half a century (Broadbent, 1967 Howes & Solomon, 1951). These findings are predicted by a distributional learning model trained on the same materials as our subjects.
However, learning across redundant contexts promoted the development of more stable semantic representations. When novel words were encountered across distinct discourse contexts, subjects were both faster and more accurate at recognizing them than when they were seen in redundant contexts. Subjects were incidentally exposed to novel words as they rated short selections from articles, books, and newspapers.
The present study extends the empirical results with a unique natural language learning paradigm, which allows for an examination of the semantic representations that are acquired as semantic diversity is varied. This measure was empirically validated with an artificial language experiment (Jones et al.). (Journal of the Acoustical Society of America, 132:2, EL74-EL80, 2012) found that a measure of contextual diversity that takes into account the semantic variability of a word’s contexts provided a better fit to both visual and spoken word recognition data than traditional measures, such as word frequency or raw context counts. In a series of analyses over mega datasets, Jones, Johns, and Recchia (Canadian Journal of Experimental Psychology, 66(2), 115–124, 2012) and Johns et al.
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