The debate on how to describe consciousness is lively in the research community. A recent paper in Neuroscience of Consciousness develops an analytical comparison between a clinical tool for assessing residual conscious activity, the so-called Perturbational Complexity Index, and a theoretical model of consciousness that is different from the one that inspired it, the Global Neuronal Workspace Theory, showing that they are compatible for a number of theoretical and empirical reasons.

The Perturbational Complexity Index (PCI) is a clinical tool that was introduced within the framework of a specific theory of consciousness, known as the Integrated Information Theory. This is currently one of the most popular theories to explain consciousness. The paper compares PCI to another very popular theory: the Global Neuronal Workspace Theory (GNWT), that was introduced by Jean-Pierre Changeux together with Stanislas Dehaene and Michel Kerszberg in 1998.

The paper is written by Michele Farisco from Uppsala University, together with Jean-Pierre Changeux, from the Institut Pasteur. According to Michele Farsisco, the paper provides a first ever direct analytical comparison between PCI and GNWT, something that has long been considered an important task to develop.

In addition, the authors have also developed an indirect comparison between IIT and GNWT, which will serve as an important contribution to an ongoing discussion about suitable theoretical frameworks and models in research on consciousness.

According to Michele Farisco & Jean-Pierre Changeux, the GNWT and PCI are indeed compatible on a fundamental level. The paper takes its starting point in a description of brain complexity, a notion that is crucial for PCI. This, they argue, is compatible with the main principle behind GNWT: a conscious process that depends on a long-range connection between different cortical regions, and more specifically on the amplification, global propagation, and integration of brain signals.

Want to read the paper?

Michele Farisco , Jean-Pierre Changeux, About the compatibility between the perturbational complexity index and the global neuronal workspace theory of consciousness, Neuroscience of Consciousness, Volume 2023, Issue 1, 2023, niad016, https://doi.org/10.1093/nc/niad016

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One thought on “Measuring & describing consciousness: clinical tool and theoretical model prove compatible”

  1. It’s becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman’s Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with primary consciousness will probably have to come first.

    What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990’s and 2000’s. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I’ve encountered is anywhere near as convincing.

    I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there’s lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.

    My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar’s lab at UC Irvine, possibly. Dr. Edelman’s roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461

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