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。 这一研究领域的理论是正式的理论，数学公式，精确地指定一个人可能认为某个物体属于什么类别，给定物体的外观和人的知识。实验被用来记录人们在学习类别时的反应如何改善，这些数据被用来定量测试正式理论的预测。本研究的主要目标是提出一个准确的选择性注意的形式化理论。 通过研究眼球运动，人们可以将直接研究注意力的困难转化为可以在实验室中处理的更易于处理的问题。眼球运动是由大脑控制的，它们的位置是从经验中学习的;当一个物体或位置对我们有用时，我们的视觉注意力就会被吸引到那个位置，随着注意力的转移，我们的目光也会随之转移。传统上，科学家们关注的是眼睛的生物学、注意力的研究或学习问题，但这三个问题很少在一个单一的研究项目中交叉。我正在做的是将数学和学习心理学的专业知识结合起来，通过研究眼球运动来检查学习的注意力。我正在使用复杂的眼球跟踪设备、数据分析和计算机模拟来做这件事，这些都是加拿大高性能计算集群所做的。 作为认知科学的基础研究，本研究的重点是从总体上提高我们对人类认知的理解。应用科学家可以利用这些知识来设计基于计算机的自动分类系统，或改进专家分类器的培训程序，例如需要解释行李扫描仪输出的机场安全人员。

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.