Automatic selection of eye tracking variables in visual categorization for adults and infants

Visual categorization and learning of visual categories exhibit early onset, however the underlying mechanisms of early cat-egorization are not well understood. The main limiting factor for examining these mechanisms is the limited duration of in-fant cooperation (10-15 minutes), which leaves little room for multiple test trials. With its tight link to visual attention, eye tracking is a promising method for getting access to the mecha-nisms of category learning. But how should researchers decide which aspects of the rich eye tracking data to focus on? To date, eye tracking variables are generally handpicked, often re-sulting in biases of the eye tracking data. Here, we propose an automated method for selecting eye tracking variables based on their usefulness to discriminate learners from non-learners of visual categories. We presented infants and adults with a category learning task and tracked their eye movements. We then extracted an over-complete set of eye tracking variables encompassing durations, probabilities, latencies, and the order of fixations and saccadic eye movements. We applied the sta-tistical technique of ANOVA ranking to identifying those vari-ables among this large set that covaried with the learner/non-learner label. We were able to identify learners and non-learners above chance level using linear SVM and the top eye tracking variables