Abstract
Previous studies have reported facilitatory effects of semantic richness on word recognition (e.g., Yap et al., 2012). These effects suggest that word meaning is an important contributor to lexical decision task (LDT) performance, but what are the effects of repeated LDT practice on these semantic contributions? The current study utilized data from the British Lexicon Project (BLP) in which 78 participants made lexical decision judgments for 28,730 words over 16 h. We used linear mixed effects to detect practice-driven changes in the explanatory variance accounted for by a set of lexical predictors that included numerous indices of relative semantic richness, including imageability, the number of senses and average radius of co-occurrence (ARC). Results showed that practice was associated with decreasing effects of predictors such as word frequency and imageability. In contrast, ARC effects were only slightly diminished with repeated practice, and effects of the number of senses and the age of acquisition were unaffected by practice. We interpret our results within a framework in which variables may dynamically influence lexical processing and the post-lexical decision making mechanisms that also contribute to LDT performance.
Highlights
Over the past several decades, considerable research attention has been devoted to the study of visual word recognition
Consistent behavioral effects of different word characteristics, such as word length effects, frequency effects, and semantic richness effects have fuelled assumptions about contributions made by these kinds of information to the process of recognizing words
If the non-words are made more similar to words, for example by using pseudohomophones, several changes in lexical decision task (LDT) performance can be observed: latencies are slower for both word and non-word responses, and certain behavioral effects are reliably larger (e.g., Stone and Van Orden, 1993; Lupker and Pexman, 2010)
Summary
Over the past several decades, considerable research attention has been devoted to the study of visual word recognition. Consistent behavioral effects of different word characteristics, such as word length effects (faster lexical decisions for shorter words, e.g., New et al, 2006), frequency effects (faster lexical decisions for words that appear more frequently in language, e.g., Balota et al, 2004), and semantic richness effects (faster lexical decisions to words associated with more semantic information, e.g., Pexman et al, 2008) have fuelled assumptions about contributions made by these kinds of information to the process of recognizing words These are among the standard word recognition effects that all models of word recognition are designed to explain. If the non-words are made more similar to words, for example by using pseudohomophones (non-words that do sound like real words if pronounced, e.g., BRANE), several changes in LDT performance can be observed: latencies are slower for both word and non-word responses, and certain behavioral effects (e.g., the word frequency effect) are reliably larger (e.g., Stone and Van Orden, 1993; Lupker and Pexman, 2010)
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