Past studies have provided broad support for that words with lower
predictability (i.e., higher surprisal) require more time for comprehension by
using large language models (LLMs) to simulate humans' cognitive load. In
general, these studies have implicitly assumed that the probability scores from
LLMs are accurate, ignoring the discrepancies between human cognition and LLMs
from this standpoint. Inspired by the concept of probability calibration, we
are the first work to focus on the probability distribution for human reading
simulation. We propose to use temperature-scaled surprisal, a surprisal
calculated by shaped probability, to be the predictor of human reading times.
Our results across three corpora consistently revealed that such a surprisal
can drastically improve the prediction of reading times. Setting the
temperature to be approximately 2.5 across all models and datasets can yield up
to an 89% of increase in delta log-likelihood in our setting. We also propose a
calibration metric to quantify the possible human-likeness bias. Further
analysis was done and provided insights into this phenomenon.

通过概率校准的概念，本文首次关注人类阅读模拟的概率分布，提出使用温度调整的意外度作为预测人类阅读时间的指标，通过三个数据集的实验证明这种意外度可以显著提高阅读时间的预测，在本文的设置中，将温度设置为大约 2.5 可使对数似然度增加 89％。同时，本文还提出了一个校准度量指标来量化可能的人类偏好偏差，并进行了进一步分析以提供相关洞察。