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Brain Network Mechanisms of Instructed Learning

指导学习的脑网络机制

基本信息

批准号：
9977801
负责人：
Michael William Cole
金额：
$ 38.98万
依托单位：
RUTGERS THE STATE UNIV OF NJ NEWARK
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-13 至 2022-06-30
项目状态：
已结题

项目摘要

Project Summary/Abstract: The tools of network science have enabled substantial progress in understanding the intrinsic organization of the human brain. Yet, the predominant focus on resting-state functional connectivity (FC) has become a critical barrier to progress in cognitive neuroscience, given that rest FC does not account for task-specific network changes likely essential for adaptive cognition. We offer a complementary approach – cognitive network neuroscience – which applies dynamic network analysis tools and theories to task manipulations of FC to offer insights into human cognitive function. The goal of this proposal is to utilize this network-based approach with human neuroimaging to understand how instructed learning is implemented in the human brain, from initial learning to automaticity after extensive practice. Most neuroscientific research has focused on non-instructed (e.g., exploratory or feedback-based) learning. Yet, instructed learning is highly relevant to mental health for several reasons. First, empirically supported psychotherapies (e.g., cognitive behavioral therapy) utilize the human ability for rapid instructed task learning (RITL; “rittle”) to convert instructions into cognitive strategies that improve outcomes across nearly every major mental disease. Second, RITL is impaired in a variety of mental diseases, given that RITL relies on flexible cognitive control – a general capacity supporting adaptive, goal-directed behavior important in daily life. Thus, in addition to adding difficulties to everyday life (e.g., learning new skills at work), the disruption of RITL abilities likely limits the effectiveness of psychotherapy in improving mental health. Finally, instructed learning provides an especially powerful means of experimental control over behavior change, which underlies mental health improvements even outside the context of psychotherapy. Advancing understanding of the neural basis of RITL and its transition to practiced automatized behaviors parallels the transition from instructions in the clinic to ingrained habits that can foster successful mental health change. In prior work, we built a large-scale brain network theory for how instructed learning occurs by drawing on the concept of “flexible hubs” – brain regions that coordinate goal-directed cognition (flexible control) by dynamically updating connectivity throughout the brain. The flexible hub theory strongly links the methods and theories of network science to the cognitive neuroscience of learning, and as such has the power to offer insights into the large-scale network processes underlying instructed learning. We propose to use large-scale brain network theory to understand the domain generality of flexible hubs during instructed learning (Aim 1), to determine the role of flexible hubs in the transition from novel instructed task training to practiced performance (Aims 2.1 & 2.2), and to develop RITL cognitive training that maximizes the utility of flexible hubs for performance of novel tasks (Aim 2.3). Our network-based approach to understanding instructed learning along with RITL cognitive training may lead to improved outcomes for a variety of mental disorders, given the central role instructed learning plays in empirically supported psychotherapies.

项目摘要/摘要：网络科学的工具在理解人类大脑的内在组织方面取得了实质性进展。然而，对静息状态功能连接（FC）的主要关注已经成为认知神经科学进展的一个关键障碍，因为静息状态功能连接并不能解释可能对适应性认知至关重要的任务特异性网络变化。我们提供了一种互补的方法-认知网络神经科学-它将动态网络分析工具和理论应用于FC的任务操作，以提供对人类认知功能的见解。本提案的目标是利用这种基于网络的人类神经成像方法来了解指导学习是如何在人类大脑中实现的，从最初的学习到广泛实践后的自动化。大多数神经科学研究都集中在非指导（例如，探索性或基于反馈的）学习上。然而，指导学习与心理健康高度相关的原因有几个。首先，经验支持的心理疗法（例如，认知行为疗法）利用人类快速指示任务学习（RITL；“一点点”）的能力，将指令转化为认知策略，从而改善几乎所有主要精神疾病的治疗结果。其次，RITL在多种精神疾病中受损，因为RITL依赖于灵活的认知控制——一种支持日常生活中重要的适应性、目标导向行为的一般能力。因此，除了给日常生活增加困难（例如，在工作中学习新技能）之外，RITL能力的中断可能会限制心理治疗在改善心理健康方面的有效性。最后，指导学习提供了一种对行为改变的实验控制的特别有力的手段，这是心理健康改善的基础，甚至在心理治疗的背景之外。深入了解RITL的神经基础及其向可实践的自动化行为的转变，与从临床指导到能够促进成功的心理健康改变的根深蒂固的习惯的转变是平行的。在之前的工作中，我们利用“灵活中枢”的概念建立了一个大规模的大脑网络理论，通过动态更新整个大脑的连接来协调目标导向的认知（灵活控制）。灵活中枢理论将网络科学的方法和理论与学习的认知神经科学紧密地联系在一起，因此有能力为指导学习背后的大规模网络过程提供见解。我们建议使用大规模脑网络理论来理解指导性学习期间柔性枢纽的域一般性（目标1），确定柔性枢纽在从新的指导性任务训练过渡到实践表现中的作用（目标2.1和2.2），并开发RITL认知训练，最大限度地发挥柔性枢纽在新任务表现中的效用（目标2.3）。我们基于网络的方法来理解指导性学习和RITL认知训练可能会改善各种精神障碍的结果，因为指导性学习在经验支持的心理治疗中起着核心作用。