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意识、串扰和整体论谬误:一种进化视角。

Consciousness, crosstalk, and the mereological fallacy: an evolutionary perspective.

机构信息

Division of Epidemiology, The New York State Psychiatric Institute at Columbia University, Box 47, 1051 Riverside Drive, New York, NY 10032, USA.

出版信息

Phys Life Rev. 2012 Dec;9(4):426-53. doi: 10.1016/j.plrev.2012.08.002. Epub 2012 Aug 21.

Abstract

The cross-sectional decontextualization afflicting contemporary neuroscience - attributing to 'the brain' what is the province of the whole organism - is mirrored by an evolutionary decontextualization exceptionalizing consciousness. The living state is characterized by cognitive processes at all scales and levels of organization. Many can be associated with dual information sources that 'speak' a 'language' of behavior-in-context. Shifting global broadcasts analogous to consciousness, albeit far slower - wound healing, tumor control, immune function, gene expression, etc. - have emerged through repeated evolutionary exaptation of the crosstalk and noise inherent to all information transmission. These recruit 'unconscious' cognitive modules into tunable arrays as needed to meet threats and opportunities across multiple frames of reference. The development is straightforward, based on the powerful necessary conditions imposed by the asymptotic limit theorems of communication theory, in the same sense that the Central Limit Theorem constrains sums of stochastic variates. Recognition of information as a form of free energy instantiated by physical processes that consume free energy permits analogs to phase transition and nonequilibrium thermodynamic arguments, leading to 'dynamic regression models' useful for data analysis.

摘要

当代神经科学中存在的横向去语境化问题——将属于整个生物体范畴的属性归因于‘大脑’——反映了一种进化去语境化的特殊意识。生命状态的特点是在所有尺度和组织层次上都存在认知过程。许多认知过程可以与双重信息源相关联,这些信息源“以行为语境中的语言”进行交流。类似于意识的全球性广播虽然速度要慢得多——伤口愈合、肿瘤控制、免疫功能、基因表达等——但它们是通过对所有信息传输中固有的串扰和噪声的反复进化适应而出现的。这些信息会根据需要招募“无意识”认知模块,以应对多个参照系中的威胁和机会。这种发展是基于通信理论渐近极限定理所施加的强大必要条件,这与中心极限定理限制随机变量和的方式相同。将信息识别为一种由消耗自由能的物理过程实现的自由能形式,允许对相变和非平衡热力学论点进行类比,从而导致有用的“动态回归模型”进行数据分析。

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