Metaversal Mining

I’ve written about Newtonian science and the simple cause and effect interpretation of the physical universe that it embodies, and how the mathematics of complexity and statistical interpretations of the physical universe, such as quantum mechanics, have superseded that mechanistic view. What I would like to suggest is that neuroscience is treading the same path in its interpretation of the mind as a mechanistic system demonstrable by a physical understanding of brain function. This is not a new idea as far as I know, but I need to ‘say it out loud’ to show myself that I know where the limits lie.

I’ve been thinking about whether I believe that the mind could be replaced by the internet, and I think ‘no, but there could be functions that could be farmed out such as memory‘. Here I’m going to explore that idea with specific reference to the blossoming fields of neuroscience, neuromarketing, neuroethics and neuro-anything-else-they-can-think-of.

The mathematics of complexity and uncertainty, chaos theory, complex systems, or however else we wish to express the concept, all share the same fundamental tenet; that simple mathematical relationships can give in unpredictable results. This is shown by the Lorentz’s Butterfly Effect, but it it is also embodied in Godel’s incompleteness theorem and the Schrodinger’s Cat thought experiment. To me these are all similar aspects of the same idea; that we can never measure all variables sufficiently well to be able to have a 100% reliable model.

If we transpose this notion to the pursuit of neuroscience, where claims are being made almost to the point of being able to read the minds of experimental subjects, we should at least consider the nature of the systems involved before we accept the validity or even applicability these kind of claims.

Brain structure is primarily a function of the expression of DNA of the individual and DNA as a replicator a very mechanistic and ultimately predictable system. I say this because genes are quite simple. Their complexity is in their size, not their building blocks. That the Human Genome Project was able to sequence our genes using automated techniques suggests that a mechanistic approach to reading that material is appropriate. However once the brain has started to develop neuronal connectivity in response to stimuli (memories start to be formed in response to experience), that mechanistic interpretation is no longer applicable without having a set of meta-data that shows the context under which those connections are made.

What neuroscientists are doing using fMRI is establishing a work-book of that contextual meta-data under experimental conditions, and its very impressive that they are managing to exclude enough of the outside world to be able to see human responses such as lying and trust and jealousy in the data that they collect. I’m sure that, on average, they are seeing some functions of mind being expressed physically. BUT, what cannot, and indeed must not, be inferred from this work is that the responses from one individual’s brain can be directly equated to the responses of another individual’s brain.

We could go though a significant proportion of the human race, taking subjects from all walks of life and every corner of the globe and find average response curves for each chunk of the brain, but we would never be able to replicate the contextual meta-data to a fine enough resolution to be able to counter Godel’s incompleteness theorem as it applies to basic information or the individual’s brain development in response to experiences from its own unique viewpoint. The mechanistic interpretation of mind, that equates brain activity to mind function, breaks down under existing mathematical interpretations of the physical universe. We would need a whole new mathematics to be able to do what is currently being claimed for neuroscience. To be fair to the neuroscientists, many of them shy away from the grand claims, but enough are not that we see fMRI being cited in legal cases. Far from free will being dead and neuroscience proving a deterministic worldview, it is showing just how poor our quantitative understanding of mind really is.

This is not a new experience. Psychoanalysis promised an understanding of mind and motivation at the beginning and middle of the 20th century and arguably was the basis for the construction of the consumerist global economy. I wonder how far neuroscience will be pushed outside the lab.

Proponents see recent fMRI science as analogous to genetic fingerprinting; as a quantitative diagnostic tool. I would argue that it is more analogous to a form of psychoanalysis where interpretation is automated. In many of the new institutes and companies working with fMRI we see the objections to the wider application of fMRI-centered neuroscience being characterised as philosophical and relating to ideas of free will and determinism. I don’t see that as a valid or even relevant conflation. My counter-claim is that what is being claimed for neuroscience is not mathematically possible and that in ignoring the role of mathematical complexity scientists, lawmakers, economists and others are acting unethically. What is being seen is the brain and not the mind. That the brains responses are linked to the mind shouldn’t be a surprise but the simple Newtonian idea of cause and effect is not applicable where 100 billion neurons each have around 7,000 synapses many of which have been influenced by memory formation or physical conditions since, or even before, birth. Simply put, just because a specific cubic centimeter of grey matter demands extra blood flow in response to the same stimuli, it doesn’t mean its for the same reason.

If it is possible to mathematically model the mind, then it should be considered as a complex system inhabiting another complex system (the brain) and informed by a set of contextual meta-data (memories and experiences) as well as environmental stimuli. Divining motivation from brain activity is a step too far mathematically, but an approximation could be possible with a sufficiently large database to populate response curves with experimental data. Whether those response curves could provide useful predictive data can’t be known at this point, but what we can say with a good degree of certainty is that you’d need a large n-value to compensate for the free variables in two complex systems and the contextual meta-data.