The Cognitive Neuroscience of Human Category Learning

人类类别学习的认知神经科学

• 批准号: 9263771
• 负责人: F. Gregory Ashby
• 金额: $ 30.82万
• 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 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9263771
• 关键词: Accidents; Address; Affect; Alzheimer' s Disease; Area; Attention; Autistic Disorder; Automobile Driving; Back; Behavior; Behavioral; Biological; Biological Models; Biological Neural Networks; Biological Testing; Brain; Brain region; Categories; Cognitive; Computer Simulation; Corpus striatum structure; Dangerousness; 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; Neuropsychology; 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; behavioral pharmacology; cognitive neuroscience; cognitive skill; experimental study; improved; network models; neurophysiology; 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，人类拥有多种类别学习系统，这些系统在神经解剖学上基本上是分开的，通过性质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（例如，显式分类系统和程序分类系统）之间切换。例如，驾驶的许多组成部分都是程序性的，但同时需要一些明确的决策。在做出明确决定后，如果未能迅速切换回程序策略，可能会大大增加发生事故的风险。拟议的研究研究了显式和程序类别学习如何协调以及控制如何在这些系统之间转移。我们采用综合、跨学科、融合的操作方法，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相结合，目标是建立和测试介导分类和系统切换行为的大脑回路的生物学详细计算模型。该提案旨在继续执行一项计划，该计划（a）提供了人类拥有多个类别学习系统的大部分现有证据，（b）绘制了调解每个系统的神经网络，以及（c）发现了这些系统的许多独特属性。在上一时期（2R01 MH3760），我们在多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，我们报告了证据，表明显性分类策略和程序性分类策略之间的逐次试验切换是 极其困难。拟议的研究继续我们对系统交互的研究，有三个目标。目标 1 是识别系统切换的认知成分。目标 2 是确定系统切换的神经基础，目标 3 是开发和测试系统切换的生物学详细计算模型。我们开发的模型应该能够准确描述目标 1 和 2 的所有数据，以及各种已发表的单单元记录研究的数据。此外，该模型还将对药物、基因和病灶如何影响行为做出具体预测，并对行为和药理学干预措施做出新颖的预测，从而可能改善类别学习中的系统切换。