The Cognitive Neuroscience of Human Category Learning

描述（由申请人提供）：分类是人类拥有的最重要的认知技能之一。它让我们在一个危险的世界中航行，找到食物、住所和朋友。现在有压倒性的证据表明，人类有多个类别学习系统，这些系统在神经解剖学上很大程度上是分开的，通过不同的规则进行学习，并且已经适应了学习不同类型的类别结构。下一个自然要研究的问题是这些不同的系统是如何相互作用的。这是一个重要的问题，因为在日常生活中，我们必须经常在不同的分类系统之间切换（例如，明确的和程序的）。例如，驾驶的许多组成部分是程序性的，但同时也需要一些明确的决策。在做出明确的决定后，如果不能迅速切换回程序策略，可能会大大增加事故的风险。本研究主要研究显性和程序性类别学习是如何协调的，以及控制是如何在这两个系统之间传递的。我们将行为学、神经心理学（帕金森病患者）、功能性磁共振成像和经颅磁刺激研究结合起来，采用综合、跨学科、融合的操作方法，目的是建立和测试调节分类和系统切换行为的大脑回路的生物学详细计算模型。这个建议是继续一个程序，(a)提供了许多现有的证据，证明人类有多个类别学习系统，(b)绘制了调节每个系统的神经网络，(c)发现了这些系统的许多独特属性。在前一阶段（2R01 MH3760），我们在几个领域取得了重大进展。一是了解不同系统中的学习是如何协调的。为此，我们报告的证据表明，在显性和程序性分类策略之间的逐个试验切换是有效的

DESCRIPTION (provided by applicant): Categorization is among the most important cognitive skills that humans possess. It allows us to navigate in a dangerous world, and to find food, shelter, and friends. The evidence is now overwhelming that humans have multiple category-learning systems, which are largely neuroanatomically separate, learn by qualitatively different rules, and have adapted to learning different types of category structures. A natural next question to investigate is how these various systems interact. This is an important problem because during daily life we must often switch between different categorization systems (e.g., explicit and procedural). For example, many components of driving are procedural, but at the same time some explicit decisions are required. Following an explicit decision, a failure to quickly switch back to a procedural strategy could greatly increase the risk of an accident. The proposed research studies how explicit and procedural category learning are coordinated and how control is transferred between these systems. We take an integrative, cross-disciplinary, converging operations approach that combines behavioral, neuropsychological (with Parkinson's disease patients), functional magnetic resonance imaging, and transcranial magnetic stimulation studies with the goal of building and testing biologically detailed computational models of the brain circuits that mediate categorization and system-switching behavior. This proposal is to continue a program that (a) provided much of the existing evidence that humans have multiple category-learning systems, (b) mapped out the neural networks that mediate each system, and (c) discovered many unique properties of these systems. During the previous period (2R01 MH3760), we made significant progress in several areas. One was to understand how learning in the various systems is coordinated. Toward this end, we reported evidence that trial-by-trial switching between explicit and procedural categorization strategies is extremely difficult. The proposed research, which continues our investigations of system interactions, has three aims. Aim 1 is to identify the cognitive components of system switching. Aim 2 is to identify the neural basis of system switching, and Aim 3 is to develop and test a biologically detailed computational model of system switching. The model we develop should be able to provide accurate accounts of all data from Aims 1 and 2, as well as data from various published single-unit recording studies. In addition, the model will make specific predictions about how drugs, genes, and focal lesions should affect behavior and it will make novel predictions about behavioral and pharmacological interventions that might improve system switching in category learning.