Local Patterns to Global Architectures: Influences of Network Topology on Human Learning.
Local Patterns to Global Architectures: Influences of Network Topology on Human Learning.
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DOI：
10.1016/j.tics.2016.06.003
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发表时间：
2016-08
影响因子：
19.9
通讯作者：
Bassett DS
中科院分区：
文献类型：
--
作者：
Karuza EA;Thompson-Schill SL;Bassett DS
认知科学的一个核心问题是人类如何获取和表征有关其环境的知识。为此，学习过程的定量理论已经被正式化，试图解释和预测大脑和行为的变化。在这里，我们将认知科学中的统计学习方法与网络科学方法联系起来，认知科学中的统计学习方法植根于学习者对局部分布的敏感性，网络科学方法用于表征全局模式及其涌现特性。我们专注于创新的工作，描述了学习是如何影响的拓扑特性的感觉输入。这些理论方法和最近的经验证据的汇合激发了在行为和神经水平上扩大定量学习方法的重要性。
A core question in cognitive science is how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. Here we connect statistical learning approaches in cognitive science, which are rooted in learners’ sensitivity to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling up quantitative approaches to learning at both behavioral and neural levels.