The Cognitive Neuroscience of Human Category Learning

人类类别学习的认知神经科学

• 批准号: 8818610
• 负责人: F. Gregory Ashby
• 金额: $ 40.94万
• 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 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8818610
• 关键词: Accidents; Accounting; Address; Affect; Alzheimer' s Disease; Area; Attention; Autistic Disorder; Automobile Driving; Back; Behavior; Behavior Control; Behavioral; Biological Models; Biological Neural Networks; Brain; Brain region; Categories; Cognitive; Computer Simulation; Corpus striatum structure; Data; Deep Brain Stimulation; Development; Disease; Dopamine; Failure; Food; Friends; Functional Magnetic Resonance Imaging; Genes; Goals; Health; Human; Huntington Disease; Intervention; Investigation; Lateral; Lead; Learning; Lesion; Life; Maps; Mediating; Memory; Modeling; Motor; Names; Neuroanatomy; Neurons; Neurosciences; Parkinson Disease; Participant; Pathway interactions; Patients; Pharmaceutical Preparations; Probability; Property; Publishing; Reaction Time; Reporting; Research; Research Personnel; Risk; Role; Schizophrenia; Series; Shelter facility; Short-Term Memory; Structure; Structure of subthalamic nucleus; System; Testing; Time; Training; Transcranial magnetic stimulation; Work; base; cognitive neuroscience; improved; neurophysiology; neuropsychological; normal aging; novel; operation; procedural memory; programs; public health relevance; relating to nervous system; research study; skills

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.

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。它使我们能够在危险的世界中航行，并找到食物，住所和朋友。现在有大量证据表明，人类有多个类别学习系统，这些系统在很大程度上是神经解剖学上独立的，通过不同的规则学习，并适应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如何相互作用。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，明确的和程序性的）。例如，驾驶的许多组成部分是程序性的，但同时需要一些明确的决定。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。本研究旨在探讨外显式和程序式类别学习是如何协调的，以及控制如何在这些系统之间转移。我们采取一种综合的，跨学科的，融合的操作方法，结合行为，神经心理学（与帕金森氏病患者），功能性磁共振成像和经颅磁刺激研究，目的是建立和测试生物详细的计算模型的大脑回路，介导分类和系统切换行为。这个提议是为了继续一个项目，（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）绘制出介导每个系统的神经网络，（c）发现这些系统的许多独特属性。在上一个时期（2R01 MH3760），我们在几个领域取得了重大进展。一个是了解如何协调不同系统中的学习。为此，我们报告了证据表明，在明确和程序分类策略之间的逐个试验转换是 非常困难。拟议的研究，继续我们的调查系统的相互作用，有三个目标。目的1是识别系统转换的认知成分。目的2是确定系统切换的神经基础，目的3是开发和测试系统切换的生物详细的计算模型。我们开发的模型应该能够提供来自目标1和2的所有数据的准确说明，以及来自各种已发表的单单位记录研究的数据。此外，该模型将对药物、基因和局灶性病变如何影响行为做出具体预测，并对可能改善类别学习中系统转换的行为和药理干预做出新的预测。