Lifeboat Foundation

Philosopher Wilfrid Sellars had a term for the world as it appears, the “manifest image.” This is the world as we perceive it. In it, an apple is an apple, something red or green with a certain shape, a range of sizes, a thing that we can eat, or throw.

The manifest image can be contrasted with the scientific image of the world. Where the manifest image has colors, the scientific one has electromagnetic radiation of certain wavelengths. Where the manifest image has solid objects, like apples, the scientific image has mostly empty space, with clusters of elementary particles, held together in configurations due to a small number of fundamental interactions.

The scientific image is often radically different from the manifest image, although how different it is depends on what level of organization is being examined. For many purposes, including scientific ones, the manifest image, which is itself a predictive theory of the world at a certain level or organization, works just fine. For example, an ethologist, someone who studies animal behavior, can generally do so without having to concern themselves about quantum fields and their interactions.

Summary: Researchers made a significant discovery in the study of human brain evolution, identifying epiregulin as a key factor in the expansion of the human neocortex. By comparing brain development between mice and humans and utilizing 3D brain organoids, the team found that epiregulin promotes the division and expansion of stem cells, crucial for neocortex development.

This study, which utilized cutting-edge 3D culture technology, suggests that the quantity of epiregulin, rather than its presence or absence, distinguishes human brain development from that of other species, including primates like gorillas. The research offers new insights into what makes the human brain unique and underscores the value of innovative methodologies in understanding complex evolutionary processes.

Neurotech startup Paradromics is set to commence human trials of its brain implant in 2025, intensifying the competition in the emerging brain-computer interface (BCI) market.

This move positions Paradromics against Elon Musk’s Neuralink, which has been at the forefront of public attention in this domain.

Paradromics’ CEO and founder, Matt Angle, in an interview with CNBC Tech, expressed his enthusiasm about the potential of brain-computer interfaces.

Development of a cognitive enhancer based on preclinical findings has translational potential (Stepan et al., in 30 April 2024 issue).

The Journal of Consciousness Studies has an issue out on the meta-problem of consciousness. (Unfortunately, it’s paywalled, so you’ll need a subscription, or access to a school network that has one.)

As a reminder, there’s the hard problem of consciousness, coined by David Chalmers in 1995, which is the question of why or how we have conscious experience, or as described by others, how conscious experience “arises” from physical systems.

Then there’s the meta-problem, also more recently coined by Chalmers, on why we think there is a hard problem. The meta-problem is an issue long identified by people in the illusionist camp, those who see phenomenal consciousness as an illusion, a mistaken concept.

Michael Shermer has an article up at Scientific American asking if science will ever understand consciousness, free will, or God.

I contend that not only consciousness but also free will and God are mysterian problems—not because we are not yet smart enough to solve them but because they can never be solved, not even in principle, relating to how the concepts are conceived in language.

On consciousness in particular, I did a post a few years ago which, on the face of it, seems to take the opposite position. However, in that post, I made clear that I wasn’t talking about the hard problem of consciousness, which is what Shermer addresses in his article. Just to recap, the “hard problem of consciousness” was a phrase originally coined by philosopher David Chalmers, although it expressed a sentiment that has troubled philosophers for centuries.

This essay addresses Cartesian duality and how its implicit dialectic might be repaired using physics and information theory. Our agenda is to describe a key distinction in the physical sciences that may provide a foundation for the distinction between mind and matter, and between sentient and intentional systems. From this perspective, it becomes tenable to talk about the physics of sentience and ‘forces’ that underwrite our beliefs (in the sense of probability distributions represented by our internal states), which may ground our mental states and consciousness. We will refer to this view as Markovian monism, which entails two claims: fundamentally, there is only one type of thing and only one type of irreducible property (hence monism). All systems possessing a Markov blanket have properties that are relevant for understanding the mind and consciousness: if such systems have mental properties, then they have them partly by virtue of possessing a Markov blanket (hence Markovian). Markovian monism rests upon the information geometry of random dynamic systems. In brief, the information geometry induced in any system—whose internal states can be distinguished from external states—must acquire a dual aspect. This dual aspect concerns the (intrinsic) information geometry of the probabilistic evolution of internal states and a separate (extrinsic) information geometry of probabilistic beliefs about external states that are parameterised by internal states. We call these intrinsic (i.e., mechanical, or state-based) and extrinsic (i.e., Markovian, or belief-based) information geometries, respectively. Although these mathematical notions may sound complicated, they are fairly straightforward to handle, and may offer a means through which to frame the origins of consciousness.

Keywords: consciousness, information geometry, Markovian monism.

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This paper investigates the compatibility between the theoretical framework of the global neuronal workspace theory (GNWT) of conscious processing and the perturbational complexity index (PCI). Even if it has been introduced within the framework of a concurrent theory (i.e. Integrated Information Theory), PCI appears, in principle, compatible with the main tenet of GNWT, which is a conscious process that depends on a long-range connection between different cortical regions, more specifically on the amplification, global propagation, and integration of brain signals. Notwithstanding this basic compatibility, a number of limited compatibilities and apparent differences emerge. This paper starts from the description of brain complexity, a notion that is crucial for PCI, to then summary of the main features of PCI and the main tenets of GNWT. Against this background, the text explores the compatibility between PCI and GNWT. It concludes that GNWT and PCI are fundamentally compatible, even though there are some partial disagreements and some points to further examine.

Keywords: brain complexity; global neuronal worskpace theory; measurement of consciousness; perturbational complexity index; theory of consciousness.

© The Author(s) 2023. Published by Oxford University Press.

This study introduces a novel methodology for consciousness science. Consciousness as we understand it pretheoretically is inherently subjective, yet the data available to science are irreducibly intersubjective. This poses a unique challenge for attempts to investigate consciousness empirically. We meet this challenge by combining two insights. First, we emphasize the role that computational models play in integrating results relevant to consciousness from across the cognitive sciences. This move echoes Alan Newell’s call that the language and concepts of computer science serve as a lingua franca for integrative cognitive science. Second, our central contribution is a new method for validating computational models that treats them as providing negative data on consciousness: data about what consciousness is not. This method is designed to support a quantitative science of consciousness while avoiding metaphysical commitments. We discuss how this methodology applies to current and future research and address questions that others have raised.

Keywords: computationalism; consciousness; evidence; functionalism; methodology; modeling.

© The Author(s) 2021. Published by Oxford University Press.