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宏观能战胜微观吗?跨时空尺度的整合信息。

Can the macro beat the micro? Integrated information across spatiotemporal scales.

作者信息

Hoel Erik P, Albantakis Larissa, Marshall William, Tononi Giulio

机构信息

Department of Psychiatry, University of Wisconsin, Madison, 6001 Research Park Blvd, WI 53703, USA.

出版信息

Neurosci Conscious. 2016 Aug 31;2016(1):niw012. doi: 10.1093/nc/niw012. eCollection 2016.

Abstract

Causal interactions within complex systems such as the brain can be analyzed at multiple spatiotemporal levels. It is widely assumed that the micro level is causally complete, thus excluding causation at the macro level. However, by measuring effective information-how much a system's mechanisms constrain its past and future states-we recently showed that causal power can be stronger at macro rather than micro levels. In this work, we go beyond effective information and consider additional requirements of a proper measure of causal power from the intrinsic perspective of a system: composition (the cause-effect power of the parts), state-dependency (the cause-effect power of the system in a specific state); integration (the causal irreducibility of the whole to its parts), and exclusion (the causal borders of the system). A measure satisfying these requirements, called , was developed in the context of integrated information theory. Here, we evaluate systematically at micro and macro levels in space and time using simplified neuronal-like systems. We show that for systems characterized by indeterminism and/or degeneracy, can indeed peak at a macro level. This happens if coarse-graining micro elements produces macro mechanisms with high irreducible causal selectivity. These results are relevant to a theoretical account of consciousness, because for integrated information theory the spatiotemporal maximum of integrated information fixes the spatiotemporal scale of consciousness. More generally, these results show that the notions of macro causal emergence and micro causal exclusion hold when causal power is assessed in full and from the intrinsic perspective of a system.

摘要

诸如大脑这样的复杂系统中的因果相互作用可以在多个时空层面进行分析。人们普遍认为微观层面在因果关系上是完备的,从而排除了宏观层面的因果关系。然而,通过测量有效信息——一个系统的机制对其过去和未来状态的约束程度——我们最近发现因果力在宏观层面可能比微观层面更强。在这项工作中,我们超越了有效信息,从系统的内在视角考虑对因果力进行恰当度量的其他要求:组成(各部分的因果效力)、状态依赖性(系统在特定状态下的因果效力);整合(整体相对于其各部分的因果不可约性),以及排他性(系统的因果边界)。在整合信息理论的背景下,开发了一种满足这些要求的度量,称为 。在这里,我们使用简化的类神经元系统在空间和时间的微观和宏观层面系统地评估 。我们表明,对于以不确定性和/或简并性为特征的系统, 确实可能在宏观层面达到峰值。如果对微观元素进行粗粒化产生具有高不可约因果选择性的宏观机制,就会出现这种情况。这些结果与意识的理论解释相关,因为对于整合信息理论来说,整合信息的时空最大值确定了意识的时空尺度。更一般地说,这些结果表明,当从系统的内在视角全面评估因果力时,宏观因果涌现和微观因果排他的概念是成立的。

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本文引用的文献

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PLoS Comput Biol. 2018 Apr 23;14(4):e1006114. doi: 10.1371/journal.pcbi.1006114. eCollection 2018 Apr.
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Integrated Information and State Differentiation.综合信息与状态分化
Front Psychol. 2016 Jun 28;7:926. doi: 10.3389/fpsyg.2016.00926. eCollection 2016.
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Measuring Integrated Information from the Decoding Perspective.从解码视角测量整合信息。
PLoS Comput Biol. 2016 Jan 21;12(1):e1004654. doi: 10.1371/journal.pcbi.1004654. eCollection 2016 Jan.
8
Quantifying causal emergence shows that macro can beat micro.量化因果涌现表明宏观可以胜过微观。
Proc Natl Acad Sci U S A. 2013 Dec 3;110(49):19790-5. doi: 10.1073/pnas.1314922110. Epub 2013 Nov 18.

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