Department of Psychology, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
Department of Psychology, University of California, Davis, CA, USA.
Psychon Bull Rev. 2024 Oct;31(5):2022-2035. doi: 10.3758/s13423-024-02489-1. Epub 2024 Mar 26.
While many theories assume that sleep is critical in stabilizing and strengthening memories, our recent behavioral study (Liu & Ranganath, 2021, Psychonomic Bulletin & Review, 28[6], 2035-2044) suggests that sleep does not simply stabilize memories. Instead, it plays a more complex role, integrating information across two temporally distinct learning episodes. In the current study, we simulated the results of Liu and Ranganath (2021) using our biologically plausible computational model, TEACH, developed based on the complementary learning systems (CLS) framework. Our model suggests that when memories are activated during sleep, the reduced influence of temporal context establishes connections across temporally separated events through mutual training between the hippocampus and neocortex. In addition to providing a compelling mechanistic explanation for the selective effect of sleep, this model offers new examples of the diverse ways in which the cortex and hippocampus can interact during learning.
虽然许多理论假设睡眠在稳定和加强记忆方面至关重要,但我们最近的行为研究(Liu & Ranganath, 2021, Psychonomic Bulletin & Review, 28[6], 2035-2044)表明,睡眠并不仅仅是稳定记忆。相反,它起着更复杂的作用,整合了两个时间上不同的学习事件的信息。在当前的研究中,我们使用基于互补学习系统 (CLS) 框架开发的具有生物学意义的计算模型 TEACH 模拟了 Liu 和 Ranganath (2021) 的结果。我们的模型表明,当记忆在睡眠中被激活时,时间背景的影响降低会通过海马体和新皮层之间的相互训练在时间上分离的事件之间建立联系。除了为睡眠的选择性影响提供一个引人注目的机械解释外,该模型还为皮层和海马体在学习过程中相互作用的多种方式提供了新的范例。