Using eye-tracking and computational modeling to understanding the dynamic allocation of attention during category learning

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People are good at learning to make distinctions. They naturally come, without any special conscious effort, to distinguish between dogs and cats, anger and sadness, and tables and chairs. But, not everything is easy to identify. Radiologists, for example, need years of training to detect tumors in x-ray films. The research in this proposal is aimed at improving our understanding of how humans learn to classify things. The purpose of our experiments and models is to understand how selective attention, the ability to pay attention to important features and ignore irrelevant ones, enables categorization and interacts with our memory and perceptual systems. Theories in this area of research are formal theories, mathematical formulas that precisely specify what category a person is likely to think something belongs to, given what the object looks like and what the person knows. Experiments are used to record how people's responses improve when learning categories, and these data are used to quantitatively test the predictions of formal theories. The primary goal of the proposed research is to produce an accurate formal theory of selective attention. By studying eye-movements one trades the difficulties in studying attention directly for more manageable problems that can be dealt with in the lab. Eye-movements are controlled by the brain, and their positioning is learned from experience; when an object or location is useful to look at, our visual attention is drawn to that location, and with attention goes our gaze. Scientists have traditionally focused on either the biology of the eyes, the study of attention or the problem of learning but rarely do the three intersect in a single research program. What I am doing is bringing together expertise in mathematics and the psychology of learning to examine learned attention by studying eye-movements. I am doing this using sophisticated eye-tracking equipment, data analysis and computer simulations made possible by Canada's high-performance computing clusters. As basic research in cognitive science, the present work is focused on improving our understanding of human cognition generally. This knowledge can then be used by applied scientists to design automatic computer-based classification systems or to improve training procedures for expert classifiers, such as airport security personnel who need to interpret the output of luggage scanners.