Evidence from large-scale studies (Pexman, Hargreaves, Siakaluk, Bodner, & Pope, 2008) suggests that semantic richness, a multidimensional construct reflecting the extent of variability in the information associated with a word's meaning, facilitates visual word recognition. Specifically, recognition is better for words that (1) have more semantic neighbors, (2) possess referents with more features, and (3) are associated with more contexts. The present study extends Pexman et al. (2008) by examining how two additional measures of semantic richness, number of senses and number of associates (Pexman, Hargreaves, Edwards, Henry, & Goodyear, 2007), influence lexical decision, speeded pronunciation, and semantic classification performance, after controlling for an array of lexical and semantic variables. We found that number of features and contexts consistently facilitated word recognition but that the effects of semantic neighborhood density and number of associates were less robust. Words with more senses also elicited faster lexical decisions but less accurate semantic classifications. These findings point to how the effects of different semantic dimensions are selectively and adaptively modulated by task-specific demands.The majority of visual word recognition research has examined how lexical-level properties such as word frequency and number of letters influence performance,using tasks such as lexical decision (word/nonword discrimination), speeded pronunciation (naming words aloud), and semantic classification (e.g., classifying a word as animate or inanimate). However, there is substantial evidence that meaning-level characteristics such as imageability also affect word recognition, even after correlated lexical variables are controlled for

The present study sheds light on the interplay between lexical and decision processes in the lexical decision task by exploring the effects of lexical decision difficulty on semantic priming effects. In 2 experiments, we increased lexical decision difficulty by either using transposed letter wordlike nonword distracters (e.g., JUGDE; Experiment 1) or by visually degrading targets (Experiment 2). Although target latencies were considerably slowed by both difficulty manipulations, stimulus quality-but not nonword type-moderated priming effects, consistent with recent work by Lupker and Pexman (2010). To characterize these results in a more fine-grained manner, data were also analyzed at the level of response time (RT) distributions, using a combination of ex-Gaussian, quantile, and diffusion model analyses. The results indicate that for clear targets, priming was reflected by distributional shifting of comparable magnitude across different nonword types. In contrast, priming of degraded targets was reflected by shifting and an increase in the tail of the distribution. We discuss how these findings, along with others, can be accommodated by an embellished multistage activation model that incorporates retrospective prime retrieval and decision-based mechanisms.