The Cognitive Neuroscience of Human Category Learning

描述(由申请人提供)：分类是将对象和事件分配到不同的类别或类别的过程。例如，这是一项极其重要的技能，使人们能够对营养物质和毒物以及捕食者和猎物做出不同的反应。人类的类别学习是令人难以置信的多样化，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调节类别学习。这里提出的研究有两个主要目标。它首先检验了人类类别学习是由多个系统调节的假设，并在此过程中探索了假定的成分系统的性质以及在什么情况下它们可能有助于正常的类别学习。第二个主要目标是开发一个重要的可能子系统的生物学上可信的计算模型--即，人们使用显式的基于规则的推理过程来学习新的类别。这个模型的组件将是连接在简单电路中的单个细胞的模型，这些简单电路已经被牵连到基于规则的分类中。为了校准模型并建立其生物学可信性，组件模型将与相关的可用单细胞记录数据进行拟合。在以这种方式校准组件后，将对照人类行为类别学习数据来测试整体模型。因此，在这个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-它的架构将模仿已知存在的真实神经电路，其组件将是单个神经元的模型，其行为与真实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最好的现有(认知)模型。

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.