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Abstract 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.