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

使用眼动追踪和计算模型来理解类别学习期间注意力的动态分配

• 批准号: 327301-2013
• 负责人: Blair, Mark
• 金额: $ 1.82万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=571977
• 关键词: Using; eye; tracking; computational; modeling

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.

人们善于学会辨别。他们自然而然地就能区分狗和猫、愤怒和悲伤、桌子和椅子。但是，并非所有事情都容易识别。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。该提案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，即关注重要特征并忽略不相关特征的能力，如何实现分类并与我们的记忆和感知系统相互作用。 该研究领域的理论是形式理论和数学公式，根据物体的外观和人的知识，精确地指定一个人可能认为某物属于哪个类别。实验用于记录人们在学习类别时的反应如何改善，这些数据用于定量检验正式理论的预测。拟议研究的主要目标是产生准确的选择性注意的形式理论。 通过研究眼球运动，人们可以将直接研究注意力的困难换成可以在实验室中处理的更易于管理的问题。眼睛的运动是由大脑控制的，它们的位置是从经验中习得的；当某个物体或位置值得一看时，我们的视觉注意力就会被吸引到该位置，并且注意力也会随之转移。传统上，科学家们要么关注眼睛的生物学，要么关注注意力的研究，要么关注学习的问题，但很少将这三者交叉在一个研究项目中。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习得的注意力。我正在使用加拿大高性能计算集群提供的先进的眼动追踪设备、数据分析和计算机模拟来完成这项工作。 作为认知科学的基础研究，目前的工作重点是提高我们对人类认知的总体理解。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，或改进专家分类员的培训程序，例如需要解释行李扫描仪输出的机场安检人员。