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

被引:109
|
作者
Yap, Melvin J. [1 ]
Pexman, Penny M. [2 ]
Wellsby, Michele [2 ]
Hargreaves, Ian S. [2 ]
Huff, Mark J. [2 ]
机构
[1] Natl Univ Singapore, Fac Arts & Social Sci, Dept Psychol, Singapore 117570, Singapore
[2] Univ Calgary, Dept Psychol, Calgary, AB T2N 1N4, Canada
来源
基金
加拿大自然科学与工程研究理事会;
关键词
semantic richness; visual word recognition; imageability; semantic neighborhood density; body-object interaction; semantic classification; lexical decision; progressive demasking; OBJECT INTERACTION RATINGS; LEXICAL DECISION; AMBIGUITY; FREQUENCY; MODEL; IMAGEABILITY; NEIGHBORHOOD; ACCESS; INFORMATION; ORTHOGRAPHY;
D O I
10.3389/fnhum.2012.00072
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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 cross 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.
引用
收藏
页数:10
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