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Can Entropy Explain Successor Surprisal Effects in Reading?

机译:熵会解释继任者的惊喜效果在阅读中吗?

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Human reading behavior is sensitive to surprisal: more predictable words tend to be read faster. Unexpectedly, this applies not only to the surprisal of the word that is currently being read, but also to the surprisal of upcoming (successor) words that have not been fixated yet. This finding has been interpreted as evidence that readers can extract lexical information parafoveally. Calling this interpretation into question, Angele et al. (2015) showed that successor effects appear even in contexts in which those successor words are not yet visible. They hypothesized that successor surprisal predicts reading time because it approximates the reader's uncertainty about upcoming words. We test this hypothesis on a reading time corpus using an LSTM language model, and find that successor surprisal and entropy are independent predictors of reading time. This independence suggests that entropy alone is unlikely to be the full explanation for successor surprisal effects.
机译:人类阅读行为对惊喜敏感:更加可预测的词语往往会更快地阅读。出乎意料的是,这不仅适用于目前正在读取的词的惊喜,而且还适用于尚未固定的即将到来的(继任者)单词的惊喜。这一发现被解释为读者可以解释读者可以提取涉及词汇信息的证据。将此解释称为疑问,Angele等人。 (2015)表明,即使在那些继任语言尚未看到的情况下,也会出现后继效果。他们假设继承人的惊喜预测阅读时间,因为它近似读者对即将到来的话语的不确定性。我们使用LSTM语言模型在阅读时间语料库上测试这个假设,并发现继承人的惊喜和熵是阅读时间的独立预测因子。这种独立性表明,单独的熵不太可能是继任者的惊喜效果的完整解释。

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