INSTANCE-BASED MODELS OF LEARNING MEMORY AND JUDGMENT

该项目旨在提高我们对基本流程的理解 人类通过开发基于实例的记忆模型进行学习和记忆。 这些模型已经被用来通过假设来解释分类任务 我们对类别的心理表征是由类别集合组成的 成员或模范。这里对实例模型进行了扩展以解释更多内容 来自学习、记忆和判断领域的复杂现象。这些 现象包括从例证链、概念性推理 多变量之间的组合、学习关系和判断 事件频率。在这些扩展模型的实验测试中，受试者 会研究一些例子，比如对一组人的人格特征描述 然后，人们对这些特征做出概率或分类判断。 该研究的长期目标是开发基于实例的 将模型转化为一个框架，帮助心理学家理解广泛的 认知能力。

This project aims to improve our understanding of the basic processes of human learning and memory by developing instance-based models of memory. These models have been used to explain categorization tasks by assuming that our mental representations of categories consist of sets of category members or exemplars. Instance models are extended here to explain more complex phenomena from the areas of learning, memory, and judgment. These phenomena include reasoning from chains of examples, conceptual combination, learning relations between multiple variables, and judgment of event frequency. In experimental tests of these extended models, subjects will study examples, such as personality trait descriptions for a group of people, then make probability or classification judgments about the traits. The long-term objective of this research is to develop instance-based models into a framework that helps psychologists understand a wide range of cognitive abilities.