Prediction of human eye movements in facial discrimination tasks

Under natural viewing conditions, human observers selectively allocate their attention to subsets of the visual input. Since overt allocation of attention appears as eye movements, the mechanism of selective attention can be uncovered through computational studies of eyemovement predictions. Since top-down attentional control in a task is expected to modulate eye movements significantly, the models that take a bottom-up approach based on low-level local properties are not expected to suffice for prediction. In this study, we introduce two representative models, apply them to a facial discrimination task with morphed face images, and evaluate their performance by comparing them with the human eye-movement data. The result shows that they are not good at predicting eye movements in this task.