Readers intentionally do not fixate some words, thought to be those they have already identified. In a rational model of reading, these word skipping decisions should be complex functions of the particular word, linguistic context, and visual information available. In contrast, heuristic models of reading only predict additive effects of word and context features. Here we test these predictions by implementing a rational model with Bayesian inference and predicting human skipping with the entropy of this model's posterior distribution. Results showed a significant effect of the entropy in predicting skipping above a strong baseline model including word and context features. This pattern held for entropy measures from rational models with a frequency prior but not from models with a 5‐gram prior. These results suggest complex interactions between visual input and linguistic knowledge as predicted by the rational model of reading, and a dominant role of frequency in making skipping decisions.

中文翻译：

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阅读中跳过单词的合理模型：视觉和语言信息的理想整合

读者故意不固定某些单词，认为这是他们已经确定的单词。在一个合理的阅读模型中，这些跳过单词的决定应该是特定单词，语言环境和可用视觉信息的复杂功能。相反，阅读的启发式模型只能预测单词和上下文特征的加和效应。在这里，我们通过使用贝叶斯推理实现一个理性模型并使用该模型的后验分布的熵来预测人类的跳跃来测试这些预测。结果表明，在预测包括单词和上下文特征的强大基线模型上方的跳跃时，熵具有显着影响。这种模式适用于先验频率有理模型的熵测度，但不适用于先验5克模型的熵测度。