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

人们善于学习区分。他们自然来，没有任何特殊的有意识的努力，区分狗和猫，愤怒和悲伤，桌子和椅子。但是，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光片中检测肿瘤。这项研究旨在提高我们对人类如何学习分类事物的理解。我们的实验和模型的目的是了解选择性注意力，即关注重要特征而忽略不相关特征的能力，如何使分类成为可能，并与我们的记忆和感知系统相互作用。

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.