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

人们善于学习区分。它们自然而然地出现，没有任何特别的刻意努力，来区分狗和猫，愤怒和悲伤，以及桌子和椅子。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来检测X光胶片中的肿瘤。这项建议中的研究旨在提高我们对人类如何学习对事物进行分类的理解。我们的实验和模型的目的是了解选择性注意，即注意重要特征和忽略无关特征的能力，如何实现分类，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.