Complex Systems Group, Department of Physics, University of California, Santa Barbara, CA 93106, USA.
Proc Natl Acad Sci U S A. 2011 May 3;108(18):7641-6. doi: 10.1073/pnas.1018985108. Epub 2011 Apr 18.
Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes--flexibility and selection--must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.
人类学习是一种复杂的现象，需要灵活性来适应现有的大脑功能，并精确选择新的神经生理活动来驱动所需的行为。这两个属性——灵活性和选择性——必须在多个时间尺度上运作，因为技能的表现从缓慢和具有挑战性转变为快速和自动。这种选择性适应性自然是由模块化结构提供的，模块化结构在进化、发展和最佳网络功能中起着关键作用。我们使用从初始训练到掌握简单运动技能过程中获得的大脑活动的功能连接测量来研究模块化结构在人类学习中的作用，通过识别跨越多个时间尺度的模块化组织的动态变化来实现这一目标。我们的结果表明，在一个实验会话中，我们通过节点对模块的忠诚度来衡量的灵活性，预测了在未来会话中相对的学习量。我们还开发了一个用于识别动态系统中模块化结构的一般统计框架，该框架广泛适用于网络适应性对系统性能理解至关重要的学科。
