Abstract
A number of speech perception studies have been carried out to investigate how we process audio signals containing real words. However, comparatively fewer studies have been conducted looking at how we process audio signals containing phonotactically valid pseudo-words. Many traditional metrics, such as lexical frequency, used as predictors in this kind of analysis are difficult or impossible to calculate for pseudo-words, but other metrics like phonotactic probability can be calculated for such pseudo-words. Phonotactic probability is the likelihood that a particular sequence of phones occurs in a particular language, and it can be easily calculated using existing pronunciation and lexical frequency data. The present study uses the CMU Pronouncing Dictionary and the Google Ngram datasets to calculate phonotactic probability measures for each stimulus in an existing set of English auditory lexical decision responses and then statistically models the influence of phonotactic probability on the participants’ reaction times to the stimuli. This modeling shows a general trend of response times decreasing as phonotactic probability increases. The results are then framed in the greater picture of speech perception and word recognition overall.
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