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

Automatic selection of eye tracking variables in visual categorization for adults and infants Samuel Rivera 1 , Catherine A. Best 2 , Hyungwook Yim 2 , Aleix M. Martinez 1 , Vladimir M. Sloutsky 2 , Dirk B. Walther 2 1 Department of Electrical and Computer Engineering, 205 Dreese Lab, 2015 Neil Avenue, Columbus, OH 43210 of Psychology, 225 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210 2 Department Abstract 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. Keywords: eye tracking; category learning. Introduction Categorization is the process of forming an equivalence class, such that discriminable entities elicit a common representa- tion and/or a common response. This task has two compo- nents (a) category learning (i.e., forming a common repre- sentation for a set of items and/or learning a common re- sponse) and (b) categorization or classification (i.e., partition- ing a stimulus set using this common representation). While category learning exhibits an early onset (Quinn, Eimas, & Rosenkrantz, 1993), relatively little is known about the un- derlying mechanism and the development of early categoriza- tion. One of the reasons is the limited duration of typical experimental paradigms with infants, yielding only a small number of data points per participant. These limitations have restricted researchers ability to answer fundamental questions about categorization in infants: How do infants learn a cate- gory? And what changes in the course of development? Given that eye movements are tightly linked to visual atten- tion (see Rayner, 1998, for a review), eye movements can pro- vide critical information of how attention allocation changes during category learning. However, eye tracking yields a large amount of data, and it is usually not clear from the out- set how to make sense of these data. These data could be converted into numerous variables, and there is no principled way of deciding which of these variables should be selected and why. The key idea of our approach is to develop algo- (a) Category object (b) Identical category pair Figure 1: Image (a) is an example category object used in the eye tracking study, with the Areas of interest (AOI)s enumerated. Image (b) defines the AOIs for a pair of objects. The numbers were not shown to participants. rithms that automatically select the eye tracking variables that are systematically linked to category learning. Our approach was as follows. We extracted a large set of possible variables from the adult or infant gaze sequence dur- ing a categorization task (e.g. fixations, saccades, gaze se- quences, etc). Some of the variables have been used in an- alyzing categorization experiments, whereas others are new. We used ANOVA to identify the eye tracking variables that best predict category learning in adults and subsequently in infants, and validated the variables using linear SVM. The significant contribution of this work is that it provides a methodology for identifying eye-tracking variables that are linked to category learning, thus allowing researchers to bet- ter understand categorization. Method Category Object The category members were flower-like objects with six petals. An example category object is shown in Fig. 1(a), with the petals enumerated for clarity. The four different cat- egories used were defined by a single petal having a distin- guishing color and shape. Specifically, the category defining features were A (pink triangle at AOI 4), B (blue semi-circle at AOI 4), C (orange square at AOI 6), and D (yellow pen- tagon at AOI 6). Adult Experiment To validate the efficacy of the approach before applying it to infants, adult participants were tested. Adult partici- pants from an introductory psychology course at The Ohio State University underwent a series of category learning tasks while their eye gaze was tracked by a Tobii T60 eye tracker at 60Hz. Adults had normal or corrected-to-normal vision, and sat approximately 60 cm away from the display. Adult participants were assigned to either a supervised or an unsupervised condition. In the supervised condition par- ticipant were advised to look for a single distinguishing fea-