Using acoustic distance and acoustic absement to quantify lexical competition.

Using phonological neighborhood density has been a common method to quantify lexical competition. It is useful and convenient but has shortcomings that are worth reconsidering. The present study quantifies the effects of lexical competition during spoken word recognition using acoustic distance and acoustic absement rather than phonological neighborhood density. The indication of a word's lexical competition is given by what is termed to be its acoustic distinctiveness, which is taken as its average acoustic absement to all words in the lexicon. A variety of acoustic representations for items in the lexicon are analyzed. Statistical modeling shows that acoustic distinctiveness has a similar effect trend as that of phonological neighborhood density. Additionally, acoustic distinctiveness consistently increases model fitness more than phonological neighborhood density regardless of which kind of acoustic representation is used. However, acoustic distinctiveness does not seem to explain all of the same things as phonological neighborhood density. The different areas that these two predictors explain are discussed in addition to the potential theoretical implications of the usefulness of acoustic distinctiveness in the models. The present paper concludes with some reasons why a researcher may want to use acoustic distinctiveness over phonological neighborhood density in future experiments.