An abundance of riches: cross-task comparisons of semantic richness effects in visual word recognition - PubMed (original) (raw)

An abundance of riches: cross-task comparisons of semantic richness effects in visual word recognition

Melvin J Yap et al. Front Hum Neurosci. 2012.

Abstract

There is considerable evidence (e.g., Pexman et al., 2008) that semantically rich words, which are associated with relatively more semantic information, are recognized faster across different lexical processing tasks. The present study extends this earlier work by providing the most comprehensive evaluation to date of semantic richness effects on visual word recognition performance. Specifically, using mixed effects analyses to control for the influence of correlated lexical variables, we considered the impact of number of features, number of senses, semantic neighborhood density, imageability, and body-object interaction across five visual word recognition tasks: standard lexical decision, go/no-go lexical decision, speeded pronunciation, progressive demasking, and semantic classification. Semantic richness effects could be reliably detected in all tasks of lexical processing, indicating that semantic representations, particularly their imaginal and featural aspects, play a fundamental role in visual word recognition. However, there was also evidence that the strength of certain richness effects could be flexibly and adaptively modulated by task demands, consistent with an intriguing interplay between task-specific mechanisms and differentiated semantic processing.

Keywords: body-object interaction; imageability; lexical decision; progressive demasking; semantic classification; semantic neighborhood density; semantic richness; visual word recognition.

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Figures

Figure 1

Figure 1

Partial effects plots of semantic richness effects, adjusted for the median value of the other numerical predictors in the model, as a function of task. 95% highest posterior density intervals are provided. Note. BOI, body–object interaction; Senses, log number of senses (Miller, 1990); SND, semantic neighborhood density (Shaoul and Westbury, 2010); Features, number of features (McRae et al., 2005); LDT, lexical decision task; G/NG LDT, go/no-go lexical decision task; PDT, progressive demasking task; SCT, semantic classification task.

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