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Internally directed cognition and mindfulness: an integrative perspective derived from predictive and reactive control systems theory

机译:内部定向的认知和正念:从预测和反应控制系统理论得出的综合观点

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In the present paper, we will apply the predictive and reactive control systems (PARCS) theory as a framework that integrates competing theories of neural substrates of awareness by describing the “default mode network” (DMN) and anterior insula (AI) as parts of two different behavioral and homeostatic control systems. The DMN, a network that becomes active at rest when there is no external stimulation or task to perform, has been implicated in self-reflective awareness and prospection. By contrast, the AI is associated with awareness and task-related attention. This has led to competing theories stressing the role of the DMN in self-awareness vs. the role of interoceptive and emotional information integration in the AI in awareness of the emotional moment. In PARCS, the respective functions of the DMN and AI in a specific control system explains their association with different qualities of awareness, and how mental states can shift from one state (e.g., prospective self-reflection) to the other (e.g., awareness of the emotional moment) depending on the relative dominance of control systems. These shifts between reactive and predictive control are part of processes that enable the intake of novel information, integration of this novel information within existing knowledge structures, and the creation of a continuous personal context in which novel information can be integrated and understood. As such, PARCS can explain key characteristics of mental states, such as their temporal and spatial focus (e.g., a focus on the here and now vs. the future; a first person vs. a third person perspective). PARCS further relates mental states to brain states and functions, such as activation of the DMN or hemispheric asymmetry in frontal cortical functions. Together, PARCS deepens the understanding of a broad range of mental states, including mindfulness, mind wandering, rumination, autobiographical memory, imagery, and the experience of self.
机译:在本文中,我们将预测和反应控制系统(PARCS)理论作为框架,通过将“默认模式网络”(DMN)和前绝缘(AI)描述为神经网络的一部分来整合意识的神经底物的竞争理论。两种不同的行为和稳态控制系统。 DMN是一种在没有外部刺激或任务执行时会处于静止状态的活跃网络,它与自我反省意识和前景有关。相比之下,人工智能与意识和与任务相关的注意力相关。这导致了相互竞争的理论,它们强调了DMN在自我意识中的作用与AI在感知情感时刻时的感知和情感信息整合的作用。在PARCS中,DMN和AI在特定控制系统中的各自功能解释了它们与不同意识质量之间的关联,以及心理状态如何从一种状态(例如预期的自我反思)转变为另一种状态(例如,对自我的意识)情感时刻)取决于控制系统的相对优势。反应性和预测性控制之间的这些转换是过程的一部分,该过程使得能够获取新颖信息,将该新颖信息整合到现有的知识结构中,以及创建一个可以整合和理解新颖信息的连续个人环境。这样,PARCS可以解释精神状态的关键特征,例如它们的时间和空间重点(例如,重点关注当前和现在与未来;第一人称视角与第三人称视角)。 PARCS进一步将精神状态与脑部状态和功能相关,例如DMN的激活或额叶皮层功能中的半球不对称。 PARCS共同加深了对广泛心理状态的理解,包括正念,思维游荡,沉思,自传式记忆,意象和自我体验。

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