RESUMO
Saliency models seek to predict fixation locations in (human) gaze behaviour. These are typically created to generalize across a wide range of visual scenes but validated using only a few participants. Generalizations across individuals are generally implied. We tested this implied generalization across people, not images, with gaze data of 1600 participants. Using a single, feature-rich image, we found shortcomings in the prediction of fixations across this diverse sample. Models performed optimally for women and participants aged 18-29. Furthermore, model predictions differed in performance from earlier to later fixations. Our findings show that gaze behavior towards low-level visual input varies across participants and reflects dynamic underlying processes. We conclude that modeling and understanding gaze behavior will require an approach which incorporates differences in gaze behavior across participants and fixations; validates generalizability; and has a critical eye to potential biases in training- and testing data.