Service de Neurologie, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne 1011, Switzerland; Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK.
School of Psychology, University of Birmingham, Birmingham B15 2TT, UK.
Trends Cogn Sci. 2017 May;21(5):313-332. doi: 10.1016/j.tics.2017.02.005. Epub 2017 Apr 3.
Over the past 30 years, cumulative evidence has indicated that cerebellar function extends beyond sensorimotor control. This view has emerged from studies of neuroanatomy, neuroimaging, neuropsychology, and brain stimulation, with the results implicating the cerebellum in domains as diverse as attention, language, executive function, and social cognition. Although the literature provides sophisticated models of how the cerebellum helps refine movements, it remains unclear how the core mechanisms of these models can be applied when considering a broader conceptualization of cerebellar function. In light of recent multidisciplinary findings, we examine how two key concepts that have been suggested as general computational principles of cerebellar function- prediction and error-based learning- might be relevant in the operation of cognitive cerebro-cerebellar loops.
在过去的 30 年中,累积的证据表明小脑的功能不仅限于感觉运动控制。这种观点源自神经解剖学、神经影像学、神经心理学和脑刺激的研究,结果表明小脑参与了注意力、语言、执行功能和社会认知等不同领域。尽管文献提供了关于小脑如何帮助完善运动的复杂模型,但当考虑更广泛的小脑功能概念时,这些模型的核心机制如何应用仍不清楚。鉴于最近的多学科发现,我们研究了两个被认为是小脑功能的一般计算原则的关键概念——预测和基于错误的学习——如何在认知脑-小脑循环的运作中相关。