Department of Psychology, Faculty of Arts and Social Sciences, National University of Singapore, Block AS4, #02-07, Singapore, 117570, Republic of Singapore.
Psychon Bull Rev. 2011 Aug;18(4):742-50. doi: 10.3758/s13423-011-0092-y.
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.
来自大规模研究的证据（Pexman、Hargreaves、Siakaluk、Bodner 和 Pope，2008）表明，语义丰富度是一个多维结构，反映了与单词意义相关的信息的可变性程度，有助于视觉单词识别。具体来说，对于具有以下特征的单词，识别效果更好：(1) 具有更多语义邻居，(2) 具有更多特征的指称对象，以及 (3) 与更多上下文相关联。本研究通过检查另外两个语义丰富度的度量标准（意义数和联想数）（Pexman、Hargreaves、Edwards、Henry 和 Goodyear，2007），在控制了一系列词汇和语义变量后，如何影响词汇决策、快速发音和语义分类表现，扩展了 Pexman 等人的研究（2008）。我们发现，特征和上下文的数量一致地促进了单词识别，但语义邻居密度和联想数量的影响不太稳定。具有更多意义的单词也会引发更快的词汇决策，但语义分类的准确性较低。这些发现表明，不同语义维度的影响如何根据特定任务的需求进行有选择和适应性的调节。
