Moving beyond Coltheart's N: A new measure of orthographic similarity
Moving beyond Coltheart's N: A new measure of orthographic similarity
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DOI：
10.3758/pbr.15.5.971
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发表时间：
2008-10-01
影响因子：
3.5
通讯作者：
Yap, Melvin
中科院分区：
文献类型：
--
作者：
Yarkoni, Tal;Balota, David;Yap, Melvin
视觉词识别研究通常使用Colheart的正字法邻域大小度量（ON）来测量词的正字法相似性。尽管ON可靠地预测许多词汇任务中的行为变异性，但其实用性本质上受到其相对限制性定义的限制。在这篇文章中，我们介绍了一个新的标准的计算机科学度量字符串相似性（Levenshtein距离）生成的正交相似性的措施。与ON不同的是，新的度量标准--名为正交Levenshtein距离20（OLD 20）--包含了词典中所有单词对之间的比较，包括不同长度的单词。我们证明，OLD 20提供了显着的优势，在预测词汇决策和发音性能在三个大的数据集。此外，OLD 20与词频的相互作用比ON更强，并且表现出更强的邻近频率影响。讨论部分重点讨论这些结果对视觉词识别模型的影响。
Visual word recognition studies commonly measure the orthographic similarity of words using Coltheart's orthographic neighborhood size metric (ON). Although ON reliably predicts behavioral variability in many lexical tasks, its utility is inherently limited by its relatively restrictive definition. In the present article, we introduce a new measure of orthographic similarity generated using a standard computer science metric of string similarity (Levenshtein distance). Unlike ON, the new measure-named orthographic Levenshtein distance 20 (OLD20)-incorporates comparisons between all pairs of words in the lexicon, including words of different lengths. We demonstrate that OLD20 provides significant advantages over ON in predicting both lexical decision and pronunciation performance in three large data sets. Moreover, OLD20 interacts more strongly with word frequency and shows stronger effects of neighborhood frequency than does ON. The discussion section focuses on the implications of these results for models of visual word recognition.