Faculty of Life and Social Sciences, Swinburne University of Technology, Australia.
Cogn Sci. 2013 Jul;37(5):800-28. doi: 10.1111/cogs.12030. Epub 2013 Mar 14.
It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full-blown model of multi-syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading.
通常认为，在字母之上，字符是一种至关重要的正字法表示层次。然而，当前的阅读连接主义模型并未解决从字母到字符的映射是如何学习的问题。因此，计算建模的一个主要挑战是开发一种能够学习这种映射并将字符分配到语言学上有意义的类别（如音节的起音、元音和韵尾）的模型。在这里，我们提出了一种模型，该模型可以学习在任何字母长度和任意数量音节的字符串上执行此操作。该模型通过错误率进行评估，并通过旨在检查字符处理中的歧义的行为实验的结果进一步验证。结果表明，该模型 (a) 可以高度准确地从字母串中选择字符，即使仅在英语词汇的一小部分上进行训练；(b) 在可能选择不同字符的情况下，选择与人们相似的字符集；(c) 预测了人类数据中发现的对分段的正字法影响；(d) 可以轻松集成到完整的多音节朗读模型（如 CDP++（Perry、Ziegler 和 Zorzi，2010））中。总而言之，这些结果表明，该模型为在熟练阅读中使用字符的计算提供了一个合理的假设。
