Abstract
Anticipatory eye movements (AEMs) are a natural and implicit measure of cognitive processing and have been successfully used to document such important cognitive capacities as learning, categorization, and generalization, especially in infancy (McMurray & Aslin, Infancy, 6, 203-229, 2004). Here, we describe an improved AEM paradigm to automatically assess online learning on a trial-by-trial basis, by analyzing eye gaze data in each intertrial interval of a training phase. Different measures of learning can be evaluated simultaneously. We describe the implementation of a system for designing and running a variety of such AEM paradigms. Additionally, this system is capable of a wider variety of gaze-contingent paradigms, as well as implementations of standard noncontingent paradigms. Our system, Smart-T (System for Monitoring Anticipations in Real Time with the Tobii), is a set of MATLAB scripts with a graphical front end, written using the Psychophysics Toolbox. The system gathers eye gaze data using the commercially available Tobii eye-trackers via a MATLAB module, Talk2Tobii. We report a pilot study showing that Smart-T can detect 6-month-old infants' learning of simple predictive patterns involving the disappearance and reappearance of multimodal stimuli.