A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior.
A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior.
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DOI：
10.1016/j.tics.2017.01.010
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发表时间：
2017-04
影响因子：
19.9
通讯作者：
Mattar MG
中科院分区：
文献类型：
--
作者：
Bassett DS;Mattar MG
人类在一个通常由学习促进的过程中使自己的行为适应外部环境。经验性地描述学习的努力可以通过将神经生理学的变化映射到行为变化的定量理论来补充。在这篇综述中，我们重点介绍了网络科学的最新进展，这些进展提供了一套工具和一个一般的视角，在理解分布式神经回路支持的学习类型时可能特别有用。我们描述了最近的应用这些工具的神经影像数据，提供独特的见解，适应性神经过程，知识的获得，并获得新的技能，形成一个网络神经科学的人类学习。虽然很有前途，但这些工具还没有与认知心理学中常用的良好行为模型联系起来。我们认为，持续的进展将需要明确的婚姻的网络方法的神经影像数据和行为的定量模型。
Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior.
脊柱问题：寻找树突状刺的功能。
影响因子：
2.9
作者：
Malanowski S;Craver CF
通讯作者：
Craver CF