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Modelling word recognition and reading aloud

机译：建模单词识别和朗读

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Computational modelling has tremendously advanced our understanding of the processes involved in normal and impaired reading. The present Special Issue highlights some new directions in the field of word recognition and reading aloud. These new lines of research include the learning of orthographic and phonological representations in both supervised and unsupervised networks, the extension of existing models to multisyllabic word processing both in English and in other languages, such as Italian, French, and German, and the confrontation of these models with data from masked priming. Some of the contributors to the Special Issue also address hotly debated issues concerning the front-end of the reading process, the viability of Bayesian approaches to understanding masked and unmasked priming, as well as the longstanding debate about the role of rules versus statistics in language processing. Thus, the present Special Issue provides a critical analysis and synthesis of current computational models of reading and cutting edge research concerning the next generation of computational models of word recognition and reading aloud.View full textDownload full textRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/09541446.2010.496263
机译：计算建模极大地提高了我们对正常阅读和阅读障碍过程的理解。本期特刊突出了单词识别和朗读领域中的一些新方向。这些新的研究方向包括在有监督和无监督的网络中学习拼字法和语音表示法，将现有模型扩展到英语和意大利语，法语和德语等其他语言的多音节词处理，以及这些模型具有来自屏蔽启动的数据。 《特刊》的一些撰稿人还解决了有关阅读过程前端，贝叶斯理解掩盖和未掩盖启动方式的可行性以及关于规则与语言中统计与统计作用的长期争论的激烈辩论的问题。处理。因此，本特刊提供了有关下一代单词识别和朗读计算模型的当前阅读和前沿研究计算模型的批判性分析和综合。查看全文下载全文相关的var addthis_config = {ui_cobrand：“ Taylor＆弗朗西斯在线”，services_compact：“ citeulike，netvibes，twitter，technorati，美味，linkedin，facebook，stumbleupon，digg，google，更多”，发布号：“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/09541446.2010.496263

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