Selective processing of features, dimensions & feedback in human category learning

点击翻译按钮获取中文摘要

People are good at learning to make distinctions. They can distinguish between dogs and cats, they can tell when someone is angry, and they know a chair when they see one.  But not everything is easy to identify. Radiologists need years of training to detect tumors in x-ray films, for instance. The research in this proposal is aimed at improving our understanding how humans are able to learn to classify things, accurately placing them into categories. The focus of the experiments is on understanding how selective attention, the ability to pay attention to important features and ignore irrelevant ones, supports 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 the theories are quantitatively tested to see how well they are able to predict people's responses. One of the primary goals of the proposed research is to produce an accurate formal theory of selective attention, that will predict how and when people will shift their attention when learning categories, and to what effect. As basic research, the present work is focused on improving our understanding of human cognition generally. This knowledge can then be used by applied scientists for many practical purposes, such as designing automatic computer-based classification systems, or improving training procedures for airport security personnel who need to detect weapons in the images on their luggage scanners.