Abstract
In a typical text, readers look much longer at some words than at others, even skipping many altogether. Historically, researchers explained this variation via low-level visual or oculomotor factors, but today it is primarily explained via factors determining a word's lexical processing ease, such as how well word identity can be predicted from context or discerned from parafoveal preview. While the existence of these effects is well established in controlled experiments, the relative importance of prediction, preview and low-level factors in natural reading remains unclear. Here, we address this question in three large naturalistic reading corpora (n = 104, 1.5 million words), using deep neural networks and Bayesian ideal observers to model linguistic prediction and parafoveal preview from moment to moment in natural reading. Strikingly, neither prediction nor preview was important for explaining word skipping-the vast majority of explained variation was explained by a simple oculomotor model, using just fixation position and word length. For reading times, by contrast, we found strong but independent contributions of prediction and preview, with effect sizes matching those from controlled experiments. Together, these results challenge dominant models of eye movements in reading, and instead support alternative models that describe skipping (but not reading times) as largely autonomous from word identification, and mostly determined by low-level oculomotor information.