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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. 

Reading is a highly complex learned skill in which humans move their eyes three to four times every second in response to visual and cognitive processing. The consensus view is that the details of these rapid eye-movement decisions—which part of a word to target with a saccade—are determined solely by low-level oculomotor heuristics. But maximally efficient saccade targeting would be sensitive to ongoing word identification, sending the eyes farther into a word the farther its identification has already progressed. Here, using a covert text-shifting paradigm, we showed just such a statistical relationship between saccade targeting in reading and trial-to-trial variability in cognitive processing. This result suggests that, rather than relying purely on heuristics, the human brain has learned to optimize eye movements in reading even at the fine-grained level of character-position targeting, reflecting efficiency-based sensitivity to ongoing cognitive processing. 

To ascertain the importance of phonetic information in the form of phonological distinctive features for the purpose of segment-level phonotactic acquisition, we compare the performance of two recurrent neural network models of phonotactic learning: one that has access to distinctive features at the start of the learning process, and one that does not. Though the predictions of both models are significantly correlated with human judgments of non-words, the feature-naive model significantly outperforms the feature-aware one in terms of probability assigned to a held-out test set of English words, suggesting that distinctive features are not obligatory for learning phonotactic patterns at the segment level.