Graduate Center, City University of New York, 365 5(th) Avenue, New York, NY 10016, United States.
Neurosci Biobehav Rev. 2019 Dec;107:279-295. doi: 10.1016/j.neubiorev.2019.09.017. Epub 2019 Sep 18.
Animals engage in intricately woven and choreographed action sequences that are constructed from trial-and-error learning. The mechanisms by which the brain links together individual actions which are later recalled as fluid chains of behavior are not fully understood, but there is broad consensus that the basal ganglia play a crucial role in this process. This paper presents a comprehensive review of the role of the basal ganglia in action sequencing, with a focus on whether the computational framework of reinforcement learning can capture key behavioral features of sequencing and the neural mechanisms that underlie them. While a simple neurocomputational model of reinforcement learning can capture key features of action sequence learning, this model is not sufficient to capture goal-directed control of sequences or their hierarchical representation. The hierarchical structure of action sequences, in particular, poses a challenge for building better models of action sequencing, and it is in this regard that further investigations into basal ganglia information processing may be informative.
动物会进行错综复杂且精心编排的动作序列,这些序列是通过试错学习构建的。大脑将单个动作联系在一起形成流畅的行为链的机制尚不完全清楚,但人们普遍认为基底神经节在这个过程中起着至关重要的作用。本文全面回顾了基底神经节在动作序列中的作用,重点探讨了强化学习的计算框架是否可以捕捉到序列的关键行为特征及其潜在的神经机制。虽然强化学习的简单神经计算模型可以捕捉到动作序列学习的关键特征,但该模型不足以捕捉序列的目标导向控制或其分层表示。动作序列的层次结构特别给构建更好的动作序列模型带来了挑战,在这方面,进一步研究基底神经节的信息处理可能会提供有价值的信息。