Moving beyond the monosyllable in models of skilled reading: Mega-study of disyllabic nonword reading
Moving beyond the monosyllable in models of skilled reading: Mega-study of disyllabic nonword reading
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DOI：
10.1016/j.jml.2016.09.003
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发表时间：
2017-04-01
影响因子：
4.3
通讯作者：
Rastle, Kathleen
中科院分区：
文献类型：
--
作者：
Mousikou, Petroula;Sadat, Jasmin;Rastle, Kathleen
大多数英语单词都是多音节的，然而对阅读的研究通常集中在单音节上。41名熟练的成年读者大声朗读915个双音节非词，这些非词与英语单词具有重要特征。分析了重音、发音和命名词，并将其与来自三个双音节阅读计算账户的数据进行了比较，包括基于规则的算法（Rastle & Colheart，2000）和连接主义方法（佩里、齐格勒和Zorzi的CDP++模型，2010年，以及Seva、Monaghan和Arciuli的打印重音网络，2009年）。基于项目的回归分析显示，正字法和语音的影响，模态人类的压力分配，发音的变化，命名lasting，而人类和模型的数据比较显示出重要的优势和劣势的对立帐户。我们的数据集提供了第一个规范的非词语料库为英国英语和最大的数据库，其同类的任何语言，因此，它将是至关重要的评估泛化性能的计算模型的阅读的未来发展。(C)2016作者（S）爱思唯尔公司出版
Most English words are polysyllabic, yet research on reading aloud typically focuses on monosyllables. Forty-one skilled adult readers read aloud 915 disyllabic nonwords that shared important characteristics with English words. Stress, pronunciation, and naming latencies were analyzed and compared to data from three computational accounts of disyllabic reading, including a rule-based algorithm (Rastle & Coltheart, 2000) and connectionist approaches (the CDP++ model of Perry, Ziegler, & Zorzi, 2010, and the print-to-stress network of Seva, Monaghan, & Arciuli, 2009). Item-based regression analyses revealed orthographic and phonological influences on modal human stress assignment, pronunciation variability, and naming latencies, while human and model data comparisons revealed important strengths and weaknesses of the opposing accounts. Our dataset provides the first normative nonword corpus for British English and the largest database of its kind for any language; hence, it will be critical for assessing generalization performance in future developments of computational models of reading. (C) 2016 The Author(s). Published by Elsevier Inc.