Mapping eye movements to cognitive processes
Dario D. Salvucci, John R. Anderson · 1999
Eye movements provide a rich and informative window into a personÕs thoughts and intentions. In recent years researchers have increasingly employed eye movements to study cognition in psychological experiments, to understand behavior in user interfaces, and even to control computers through eye-based input devices. Unfortunately, like speech and handwriting, eye movements generate vast amounts of data with significant individual variability and equipment noise. Thus, the analysis of eye-movement dataÑthat is, determining what people are thinking based on where they are lookingÑcan be extremely tedious and time-consuming. Typical eyemovement data sets are simply too large and complex to be analyzed by hand or by naive automated methods. This thesis formalizes a new class of algorithms that provide fast and robust automated analysis of eye-movement data. Specifically, the thesis describes three novel algorithms for tracing eye movementsÑmapping eye-movement protocols to the sequential predictions of a cognitive process model. Two algorithms, fixation tracing