Deconstructing transitional probabilities: Bigram frequency and diversity in lexical decision

Abstract

Statistical learning paradigms traditionally use transitional probabilities as a measure of statistical distribution within a language. The current study suggests that alternative metrics may exist that can account for differences in language processing ability. Two primed lexical decision tasks are used to examine the effects of bigram frequency and diversity on speed and accuracy of word recognition. It is demonstrated that both frequency and diversity contribute to word recognition performance; findings and theoretical implications are discussed.


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