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Lexical Predictability During Natural Reading: Effects of Surprisal and Entropy Reduction

机译:自然阅读中的词汇可预测性:惊奇和熵减少的影响

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What are the effects of word-by-word predictability on sentence processing times during the natural reading of a text? Although information complexity metrics such as surprisal and entropy reduction have been useful in addressing this question, these metrics tend to be estimated using computational language models, which require some degree of commitment to a particular theory of language processing. Taking a different approach, this study implemented a large-scale cumulative cloze task to collect word-by-word predictability data for 40 passages and compute surprisal and entropy reduction values in a theory-neutral manner. A separate group of participants read the same texts while their eye movements were recorded. Results showed that increases in surprisal and entropy reduction were both associated with increases in reading times. Furthermore, these effects did not depend on the global difficulty of the text. The findings suggest that surprisal and entropy reduction independently contribute to variation in reading times, as these metrics seem to capture different aspects of lexical predictability.
机译:在自然阅读文本期间,逐字可预测性对句子处理时间有什么影响?尽管信息复杂性度量(例如惊喜和熵减少)在解决此问题方面很有用,但这些度量倾向于使用计算语言模型进行估算,这需要对特定语言处理理论做出一定程度的投入。本研究采用另一种方法,实施了一项大规模的累积完形填空任务,以收集40个段落的逐字可预测性数据,并以一种与理论无关的方式计算出意外和熵减少值。记录参与者的眼动时,另一组参与者阅读相同的文本。结果表明,惊喜和熵减少的增加均与阅读时间的增加有关。此外,这些影响不取决于文本的整体难度。研究结果表明,惊喜和熵的减少独立地导致阅读时间的变化,因为这些指标似乎捕获了词汇可预测性的不同方面。

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