亚稳态思维：通过事件分割与亚稳态神经状态的综合理解自然认知的神经基础
The Metastable Mind: Neural Underpinnings of Naturalistic Cognition Through the Synthesis of Event Segmentation and Metastable Neural States
AI总结：
本文综述了事件分割的认知理论与亚稳态神经活动的机制方法，指出两者从不同角度研究相同的亚稳态神经状态，并阐述了这些状态作为认知基本计算单元的核心原则。
摘要：
来自认知、行为与计算神经科学的众多发现和理论表明，神经活动以多种有意义的时域单位展开。关于事件分割（ES）的行为研究表明，连续体验被分割为离散事件和子事件，这有助于实时理解、记忆和决策。计算神经科学的研究将持续的大脑活动观察并建模为一系列在广泛时空尺度上发生的稳定群体活动，称为亚稳态神经活动（MNA）。通过本综述，我们表明这些孤立的文献分支——事件分割（ES）的认知理论和亚稳态（MNA）的机制方法——实际上从不同角度研究了相同的亚稳态神经状态。行为分支提供了分割的认知和行为效用的理论，而亚稳态文献则在实现层面提供了机制解释。我们描述了亚稳态神经状态如何作为认知的基本计算单元，并确定了它们运作的一些核心原则。其一是在时空上嵌套的状态层级，其中高级区域中持续时间较长的状态既约束又受快速运作区域的状态塑造。其二是神经状态反映了潜在的预测模型，这些模型塑造感知、决策、记忆编码和回忆。最后，神经状态是更模块化处理的时期，其间穿插着连接重组发生的边界。理解神经状态如何涌现、相互作用并塑造认知，使我们更接近理解大脑在其自然运作模式下的状态。
英文摘要：
A multitude of findings and theories from cognitive, behavioural and computational neuroscience show that neural activity unfolds in a variety of meaningful temporal units. Behavioural research on event segmentation (ES) has shown that continuous experience is segmented into discrete events and sub-events, which aid real-time comprehension, memory, and decision-making. Computational neuroscience research observes and models ongoing brain activity as a series of stable population activity that occur across wide spatial and temporal scales, referred to as metastable neural activity (MNA). Through this review, we show that these isolated branches of literature, the cognitive theory of Event Segmentation (ES) and the mechanistic approach of metastability (MNA), actually study the same metastable neural states from different perspectives. While the behavioural branch offers a theory for the cognitive and behavioural utility of segmentation, the metastability literature provides the mechanistic account at the implementational level. We describe how metastable neural states act as the fundamental computational units of cognition and identify a number of core principles of how they operate. One is the spatio-temporally nested hierarchy of states, where longer-duration states in higher-order regions both constrain and are shaped by states in faster-operating regions. Another is that neural states are a reflection of underlying predictive models which shape perception, decision making, memory encoding and recall. And finally that neural states are periods of more modular processing, which are interspersed by boundaries where there is a reconfiguration of connectivity. Understanding how neural states emerge, interact, and shape cognition brings us closer to understanding the brain in its natural mode of operation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
