A fundamental result in psycholinguistics is that less predictable words take
a longer time to process. One theoretical explanation for this finding is
Surprisal Theory (Hale, 2001; Levy, 2008), which quantifies a word's
predictability as its surprisal, i.e. its negative log-probability given a
context. While evidence supporting the predictions of Surprisal Theory have
been replicated widely, most have focused on a very narrow slice of data:
native English speakers reading English texts. Indeed, no comprehensive
multilingual analysis exists. We address this gap in the current literature by
investigating the relationship between surprisal and reading times in eleven
different languages, distributed across five language families. Deriving
estimates from language models trained on monolingual and multilingual corpora,
we test three predictions associated with surprisal theory: (i) whether
surprisal is predictive of reading times; (ii) whether expected surprisal, i.e.
contextual entropy, is predictive of reading times; (iii) and whether the
linking function between surprisal and reading times is linear. We find that
all three predictions are borne out crosslinguistically. By focusing on a more
diverse set of languages, we argue that these results offer the most robust
link to-date between information theory and incremental language processing
across languages.

通过对 11 种不同语言进行研究，我们发现惊奇理论与阅读时间之间存在跨语言的联系，验证了三个预测：惊奇度是否预测阅读时间、预期惊奇度是否预测阅读时间、以及惊奇度与阅读时间之间的联系是否是线性的，从而在更多语言上提供了信息论与递增语言处理之间至今最强大的连接。

在 11 种语言中测试惊奇理论的预测

Testing the Predictions of Surprisal Theory in 11 Languages

Over the past two decades, numerous studies have demonstrated how less
predictable (i.e. higher surprisal) words take more time to read. In general,
these previous studies implicitly assumed the reading process to be purely
responsive: readers observe a new word and allocate time to read it as
required. These results, however, are also compatible with a reading time that
is anticipatory: readers could, e.g., allocate time to a future word based on
their expectation about it. In this work, we examine the anticipatory nature of
reading by looking at how people's predictions about upcoming material
influence reading times. Specifically, we test anticipation by looking at the
effects of surprisal and contextual entropy on four reading-time datasets: two
self-paced and two eye-tracking. In three of four datasets tested, we find that
the entropy predicts reading times as well as (or better than) the surprisal.
We then hypothesise four cognitive mechanisms through which the contextual
entropy could impact RTs -- three of which we design experiments to analyse.
Overall, our results support a view of reading that is both anticipatory and
responsive.

本篇研究检验了阅读的预测性质，发现上下文熵对阅读时间的预测能力高于或等于惊奇程度，在四个阅读数据中三个能够预测阅读时间。此外，作者提出了四个认知机制来支持上下文熵对阅读时间的影响，从而支持阅读具有先见性和响应性的观点。