Readers Use Bayesian Estimation for Eye Movement Control

During reading, saccadic landing positions within words show a pronounced peak close to the word center, with an additional systematic error that is modulated by the distance from the launch site and the length of the target word. Here we show that the systematic variation of fixation positions within words, the saccadic range error, can be derived from Bayesian decision theory. We present the first mathematical model for the saccadic range error; this model makes explicit assumptions regarding underlying visual and oculomotor processes. Analyzing a corpus of eye movement recordings, we obtained results that are consistent with the view that readers use Bayesian estimation for saccade planning. Furthermore, we show that alternative models fail to reproduce the experimental data.