Classifying Categorization Using State Trace Analysis and Hierarchical Bayesian Modeling

为了理解世界，人类必须对世界进行分类，比如健康与有毒，有价值与高价，甚至苹果与橘子，这些分类协调了我们的思维，指导了我们在世界上的行动。与其在认知中的中心地位相一致，当前的分类和类别学习理论数量众多且多种多样。这个研究项目的目标是收集这些不同的理论，并使用新的和创新的统计理论，计算机实现和实验设计进行直接比较。这个项目旨在通过找出为什么不同的人，在不同的时间，以不同的方式学习不同种类的类别，来服务于苏格拉底在其关节处雕刻自然的目标。该项目具有许多潜在的科学和实际影响。特别是，要开发的技术与记忆，判断和决策的心理学研究直接相关。更广泛地说，如果知道不同的类别类型在学习方式上是否可靠地不同，或者人们在学习类别的方式上是否可靠地不同，将允许开发程序来帮助人们更快更准确地学习困难的类别（如癌症与良性射线照片）。

Humans must categorize the world in order to understand it. Categories like healthy versus poisonous, valuable versus overpriced, or even apples versus oranges coordinate our thinking and guide our actions in the world. In line with their centrality in cognition, current theories of categorization and category learning are numerous and diverse. The goal of this research project is to gather together these diverse theories and subject them to direct comparison using new and innovative statistical theories, computer implementations, and experimental designs. This project aims to serve Socrate's goal of carving nature at its joints by finding out why different people, at different times, learn different kinds of categories in different ways. This project has many potential scientific and practical ramifications. In particular, the techniques to be developed are directly relevant to research in the psychology of memory, judgment, and decision-making. More broadly, knowing if different category types are reliably different in the way they are learned, or if people are reliably different in the way they learn categories, will allow development of programs to help people learn difficult categories (like cancerous vs benign radiographs) more quickly and accurately.