Causal reductionism and causal structures
Causal reductionism and causal structures
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DOI：
10.1038/s41593-021-00911-8
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发表时间：
2021-09-23
影响因子：
25
通讯作者：
Tononi, Giulio
中科院分区：
文献类型：
--
作者：
Grasso, Matteo;Albantakis, Larissa;Tononi, Giulio
在这个观点中，托诺尼和他的同事认为，虽然基本机制的知识足以预测系统动力学的一切，但只有对因果结构的分析才能提供“因果关系”的连贯解释。因果还原论是一种普遍的假设，即一旦我们解释了系统内所有的基本机制，就没有额外原因的空间了。由于其直观的吸引力，因果还原论在神经科学中很流行：一旦所有神经元都被引起放电或不放电，似乎就没有什么可以解释的了。在这里，我们认为这些还原论的直觉是基于一种隐含的，未经检验的因果关系概念，将因果关系与预测混为一谈。通过一个简单的模式生物，我们证明因果还原论不能提供一个完整和连贯的“什么导致了什么”的解释。为此，我们概述了一种明确的、可操作的方法来分析因果结构。
In this Perspective, Tononi and colleagues argue that while knowledge of elementary mechanisms is enough to predict everything about the dynamics of a system, only the analysis of causal structures can provide a coherent account of 'what caused what'.Causal reductionism is the widespread assumption that there is no room for additional causes once we have accounted for all elementary mechanisms within a system. Due to its intuitive appeal, causal reductionism is prevalent in neuroscience: once all neurons have been caused to fire or not to fire, it seems that causally there is nothing left to be accounted for. Here, we argue that these reductionist intuitions are based on an implicit, unexamined notion of causation that conflates causation with prediction. By means of a simple model organism, we demonstrate that causal reductionism cannot provide a complete and coherent account of 'what caused what'. To that end, we outline an explicit, operational approach to analyzing causal structures.