Mediano Pedro A M, Seth Anil K, Barrett Adam B
Department of Computing, Imperial College, London SW7 2RH, UK.
Sackler Centre for Consciousness Science and Department of Informatics, University of Sussex, Brighton BN1 9RH, UK.
Entropy (Basel). 2018 Dec 25;21(1):17. doi: 10.3390/e21010017.
Integrated Information Theory (IIT) is a prominent theory of consciousness that has at its centre measures that quantify the extent to which a system generates more information than the sum of its parts. While several candidate measures of integrated information (" Φ ") now exist, little is known about how they compare, especially in terms of their behaviour on non-trivial network models. In this article, we provide clear and intuitive descriptions of six distinct candidate measures. We then explore the properties of each of these measures in simulation on networks consisting of eight interacting nodes, animated with Gaussian linear autoregressive dynamics. We find a striking diversity in the behaviour of these measures-no two measures show consistent agreement across all analyses. A subset of the measures appears to reflect some form of dynamical complexity, in the sense of simultaneous segregation and integration between system components. Our results help guide the operationalisation of IIT and advance the development of measures of integrated information and dynamical complexity that may have more general applicability.
整合信息理论（IIT）是一种著名的意识理论，其核心是量化一个系统产生的信息比其各部分之和更多的程度的度量。虽然现在存在几种整合信息的候选度量（“Φ”），但对于它们如何比较知之甚少，尤其是在非平凡网络模型上的表现。在本文中，我们对六种不同的候选度量进行了清晰直观的描述。然后，我们在由八个相互作用节点组成的网络上进行模拟，用高斯线性自回归动力学进行动画演示，探索这些度量各自的特性。我们发现这些度量的行为存在显著差异——没有两种度量在所有分析中都表现出一致的一致性。从系统组件之间同时进行分离和整合的意义上讲，一部分度量似乎反映了某种形式的动态复杂性。我们的结果有助于指导IIT的操作化，并推动可能具有更广泛适用性的整合信息和动态复杂性度量的发展。
