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

人们善于学习区分。它们能分辨狗和猫，能分辨出别人什么时候生气，能认出椅子。但并不是所有的东西都很容易识别。例如，放射科医生需要多年的培训才能在x光片上发现肿瘤。这项提案中的研究旨在提高我们对人类如何能够学习对事物进行分类，准确地将它们分类的理解。实验的重点是了解选择性注意，即关注重要特征而忽略无关特征的能力，如何支持分类，并与我们的记忆和感知系统相互作用。这一研究领域的理论是形式理论，数学公式，精确地说明一个人可能认为某物属于哪个类别，给定物体的样子和这个人所知道的。实验用来记录人们在学习类别时的反应是如何提高的，并对这些理论进行定量测试，看看它们能在多大程度上预测人们的反应。这项研究的主要目标之一是提出一种准确的选择性注意的形式理论，该理论将预测人们在学习类别时如何以及何时转移注意力，以及会产生什么影响。作为基础研究，目前的工作重点是提高我们对人类认知的总体理解。然后，应用科学家可以将这些知识用于许多实际目的，例如设计基于计算机的自动分类系统，或改进机场安全人员的培训程序，这些人员需要在行李扫描仪上的图像中发现武器。

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.