The Cognitive Neuroscience of Human Category Learning

人类类别学习的认知神经科学

• 批准号: 6789975
• 负责人: F. Gregory Ashby
• 金额: $ 21.2万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA BARBARA
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2005-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6789975
• 关键词: biological models; clinical research; cognition; human subject; learning; memory; model design /development; neural information processing; neurons; neuropsychological tests; neuropsychology; perception; single cell analysis

DESCRIPTION (provided by applicant): Categorization is the process of assigning objects and events to separate classes or categories. It is a vitally important skill that makes it possible, for example, to respond differently to nutrients and poisons, and to predators and prey. Human category learning is incredibly diverse, as are the categories that people must learn, and there is growing evidence that different neural circuits might mediate category learning in different special circumstances. The research proposed here has two major goals. The first it to test more fully the hypothesis that human category learning is mediated by multiple systems, and in so doing, to explore the properties of the putative component systems and the conditions under which they may contribute to normal category learning. The second major goal is to develop a biologically plausible computational model of one important possible subsystem - namely, one in which people use an explicit rule-based reasoning process to learn new categories. The components of this model will be models of single cells that are joined in simple circuits that have been implicated in rule-based categorization. To calibrate the model and to establish its biological plausibility, the component models will be fit to relevant available single-cell recording data. After calibrating the components in this way, the overall model will be tested against human behavioral category learning data. Thus, the model that will be developed in this project represents a new generation of computational models in cognitive psychology - its architecture will be patterned after real neural circuits that are known to exist, its components will be models of single neurons whose behavior is consistent with the firing properties of real cells, and it will attempt to account for human category' learning data as well as the best existing (cognitive) models.

描述（由申请人提供）：分类是将对象和事件分配到单独的类或类别的过程。这是一项至关重要的技能，例如，它可以对营养和毒药以及捕食者和猎物做出不同的反应。人类的类别学习是非常多样化的，人们必须学习的类别也是如此，越来越多的证据表明，不同的神经回路可能在不同的特殊情况下介导类别学习。这里提出的研究有两个主要目标。第一，它更充分地测试的假设，人类的类别学习是由多个系统介导的，并在这样做，探索假定的组件系统的属性和条件下，他们可能有助于正常的类别学习。第二个主要目标是为一个重要的可能的子系统开发一个生物学上合理的计算模型--也就是说，人们使用一个明确的基于规则的推理过程来学习新的类别。这个模型的组成部分将是单个细胞的模型，这些细胞连接在简单的电路中，这些电路与基于规则的分类有关。为了校准模型并确定其生物相容性，将组件模型拟合到相关的可用单细胞记录数据。在以这种方式校准组件之后，将针对人类行为类别学习数据对整个模型进行测试。因此，将在该项目中开发的模型代表了认知心理学中的新一代计算模型-其架构将以已知存在的真实的神经回路为模式，其组件将是单个神经元的模型，其行为与真实的细胞的放电特性一致，它将试图解释人类类别的学习数据以及现有的最佳（认知）模型。