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Can Entropy Explain Successor Surprisal Effects in Reading?

机译:熵能解释阅读过程中的后继惊喜吗?

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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:人类的阅读行为对意外感很敏感:更多可预测的单词倾向于更快地阅读。出乎意料的是,这不仅适用于当前正在读取的单词的副词,而且还适用于尚未确定的即将到来的(继任者)单词的副词。这一发现被解释为读者可以从副词提取词汇信息的证据。对此解释提出质疑,Angele等人。 (2015)表明,即使在那些继任词还不可见的情况下,继任效应也会出现。他们假设后继意外能够预测阅读时间,因为它可以使读者对即将到来的单词产生不确定性。我们使用LSTM语言模型在阅读时间语料库上检验了这一假设,发现后继惊喜和熵是阅读时间的独立预测因子。这种独立性表明,仅靠熵不可能完全解释后继意外效应。

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