In three experimental tasks Stephen and Mirman (2010) measured gaze steps, the distance in pixels between gaze positions on successive samples from an eyetracker. They argued that the distribution of gaze steps is best fit by the lognormal distribution, and based on this analysis they concluded that interactive cognitive processes underlie eye movement control in these tasks. The present comment argues that the gaze step distribution is predictable based on the fact that the eyes alternate between a fixation state in which gaze is steady and a saccade state in which gaze position changes rapidly. By fitting a simple mixture model to Stephen and Mirman's gaze step data we reveal a fixation distribution and a saccade distribution. This mixture model captures the shape of the gaze step distribution in detail, unlike the lognormal model, and provides a better quantitative fit to the data. We conclude that the gaze step distribution does not directly suggest processing interaction, and we emphasize some important limits on the utility of fitting theoretical distributions to data. (Contains 4 figures and 3 Tables.)