Word predictability and frequency effects in a rational model of reading

This paper presents results from the first rational model of eye movement control in reading to make predictions for the full range of the eye movement record. The model identifies the text through Bayesian inference and makes eye movement decisions to maximize the efficiency of text identification, going beyond leading approaches which select model parameters to maximize the fit to human data. Two simulations with the model demonstrate that it can produce effects of word predictability and frequency on eye movements in reading similar to those produced by humans, providing evidence that many properties of human reading behavior may be understood as following from the nature of efficient text identification.