Aguilera Miguel, Di Paolo Ezequiel A
IAS-Research Center for Life, Mind and Society, Department of Logic and Philosophy of Science, University of the Basque Country, Donostia, Spain; Department of Informatics & Sussex Neuroscience, University of Sussex, Falmer, Brighton, UK; ISAAC Lab, Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
IAS-Research Center for Life, Mind and Society, Department of Logic and Philosophy of Science, University of the Basque Country, Donostia, Spain; Ikerbasque, Basque Foundation for Science, Bizkaia, Spain; Centre for Computational Neuroscience and Robotics, Department of Informatics, University of Sussex, Falmer, Brighton, UK.
Neurosci Biobehav Rev. 2021 Apr;123:230-237. doi: 10.1016/j.neubiorev.2021.01.009. Epub 2021 Jan 22.
Inspired by models of self-organized criticality, a family of measures quantifies long-range correlations in neural and behavioral activity in the form of self-similar (e.g., power-law scaled) patterns across a range of scales. Long-range correlations are often taken as evidence that a system is near a critical transition, suggesting interaction-dominant, softly assembled relations between its parts. Psychologists and neuroscientists frequently use power-law scaling as evidence of critical regimes and soft assembly in neural and cognitive activity. Critics, however, argue that this methodology operates at most at the level of an analogy between cognitive and other natural phenomena. This is because power-laws do not provide information about a particular system's organization or what makes it specifically cognitive. We respond to this criticism using recent work in Integrated Information Theory. We propose a more principled understanding of criticality as a system's susceptibility to changes in its own integration, a property cognitive agents are expected to manifest. We contrast critical integration with power-law measures and find the former more informative about the underlying processes.
受自组织临界模型的启发，一系列度量以跨尺度范围的自相似（例如，幂律缩放）模式的形式量化神经和行为活动中的长程相关性。长程相关性通常被视为系统接近临界转变的证据，这表明其各部分之间存在以相互作用为主导的、松散组装的关系。心理学家和神经科学家经常使用幂律缩放作为神经和认知活动中临界状态和松散组装的证据。然而，批评者认为，这种方法最多只是在认知与其他自然现象之间的类比层面上起作用。这是因为幂律并不能提供关于特定系统组织的信息，也不能说明使其具有特定认知性的因素。我们利用综合信息理论的最新研究来回应这一批评。我们提出了一种对临界性更具原则性的理解，即临界性是系统对自身整合变化的敏感性，这是认知主体预期会表现出的一种特性。我们将临界整合与幂律度量进行对比，发现前者能提供更多关于潜在过程的信息。
