  Visual categorization and learning of visual categories exhibit early onset,
however the underlying mechanisms of early categorization are not well
understood. The main limiting factor for examining these mechanisms is the
limited duration of infant 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 mechanisms 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, which may lead to biases in the eye tracking data. Here, we propose
an automated method for selecting eye tracking variables based on analyses of
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 compared three statistical
techniques for identifying those variables among this large set that are useful
for discriminating learners form non-learners: ANOVA ranking, Bayes ranking,
and L1 regularized logistic regression. We found remarkable agreement between
these methods in identifying a small set of discriminant variables. Moreover,
the same eye tracking variables allow us to classify category learners from
non-learners among adults and 6- to 8-month-old infants with accuracies above
71%.
