Dr. Natalia Janson, senior lecturer in applied mathematics, discusses: Can We Resolve the Mind-Body Problem with Mathematics? at TEDxLoughboroughU Conference (Transcript)
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Dr. Natalia Janson – Senior Lecturer in Applied Mathematics
In the 1600s, a philosopher and mathematician, René Descartes, suggested that the mind was an immaterial entity, isolated from the body and — notably from the body and from the brain.
On the contrary, in the middle of the 20th century, a philosopher, Gilbert Ryle, proposed that the mind is simply the physical processes occurring in the brain, and there is no separate entity which needs to be called the mind. So, in fact, he proposed that the mind was material.
In between these two extreme standpoints, there was a theory of a 19th century philosopher, Arthur Schopenhauer, who admitted that the mind and the body were distinct entities, but the properties of the mind were largely determined by the properties of the body to which it belonged.
There has been a lot of philosophical dispute about whether the mind is totally unobservable, or perhaps observable. In the 20th century, it has been widely recognized that we need to start handling the mind with the methods of natural sciences. The ancient mind-body problem about the relationship between mental and physical processes has been reformulated with account of the achievements of modern science.
Nowadays, the scientific hypothesis is that the mind is the product of the brain, and it emerges from interactions between the brain components. So the mind-body problem now reads: What is the exact relationship between the brain and cognitive functions, between the brain and behavior? Several people hypothesized that the mind could be some field produced by the brain, possibly an electrical field. A psychologist, Benjamin Libet, proposed that the mind is not a physical force field, not like an electrical or a gravitational one, and is rather some immaterial field which is not directly observable.
What else do we assume about the mind? It develops throughout our life. Indeed, we are not the kids we used to be: we learn, we change every day, we develop. It is largely shaped by the environment, by nurture, for example. It governs our behavior. And, importantly, it is assumed that all minds are unique.
There seems to be some contradiction between what different theories attribute to the mind. If only one of these theories is correct, does it mean that all others are wrong? Or is there any chance that maybe all of these theories are correct? Well, in science, it happens from time to time, when conflicting theories are reconciled by the appearance of a more general theory. And a very famous example is the wave-particle duality of light, which was resolved when quantum mechanics appeared.
To study the mind with the methods of natural sciences, we first of all need its rigorous definition. But it should be such a definition, which connects the mind with brain data. And there are two kinds of brain data: Brain activity data, which tell us what the brain does; and the brain connectivity data, which inform us how the brain is made.
There is one standard way to define an object: to list all of its features. Can’t we simply define the mind as an object having all the features listed here? No, because it will not connect the mind with the brain, and it will not explain how features which seem to contradict each other, to exclude each other, can coexist. So, basically, we need to resolve the mind-body problem first. In mathematics, there happens to be an entity, which is called into being by the brain, and which combines all of the mind features listed here in a non-conflicting manner.
So let us start, from the brain. Thousands of papers are published on neuroscience every year, and our knowledge of brain mechanisms is vast and growing. However, it did not resolve the mind-body problem so far. To get to the core of this problem, I am going to ignore most of this knowledge and I will focus only on the most important, significant brain facts.
The brain consists of a large number of neuron cells, which are connected to each other. These neurons produce repetitive splashes of electrical activity, called firings, which never stop. And importantly, the neural activity occurs spontaneously, in the sense that nobody tells the neuron when to fire. The neuron analyzes signals at its input and decides what to do next entirely on its own. So, overall the brain is a large system which demonstrates spontaneous activity.
To describe spontaneously behaving devices of any origin, from a mechanical pendulum clock to a beating heart, in mathematics there is a special theory, called theory of dynamical systems. ‘Dynamics’ means ‘change of time’. To construct a model for a spontaneously behaving device, we make two steps. First of all, we identify all quantities which change in time and call them variables. In neurons, such variables are usually ionic currents, the membrane voltages, concentrations of various chemicals inside the cell, and so on. The full collection of such variables is called a state. We assume that these variables are coordinates of a certain point in a special space, called state space.
State space is not like the physical 3D space in which we live. It is a non-physical space. And going from the physical 3D space to a non-physical, multi-dimensional state space was a huge mathematical breakthrough. When the neuron demonstrates activity, its variables change in time and the state point moves along a certain path.
To describe the whole brain, we need to introduce state variables for all of its neurons — and there are about 100 billions of them — and to form the joint state space, because the neurons are connected. So the dimension of the state space of the brain is really large, much larger than 3.
So the second step in constructing a model is to specify the rules which govern the behavior of the system. And in our terms these are the rules which govern the motion of the state point in the state space. We do it as follows: At every point in the state space we put an arrow which has a certain length and a certain direction. This arrow is called the velocity vector. The full collection of such arrows is called the velocity vector field. It works as follows: Imagine that our state point is represented as a football moving in the state space. And imagine that at every point in the state space we put a footballer who does two things: He accepts the ball from where it arrives and kicks it in a given direction with a given speed. In between the two kicks, the football travels for just a tiny amount of time before it is caught by the next footballer. And the next footballer kicks it in a different direction with a different speed. That is why the football moves.
So this metaphor explains why our field of arrows is called the velocity vector field: it is because speed with direction is called ‘velocity’, and in mathematics all quantities with direction are called ‘vectors’. So we know that if we consider a car moving along a curved village road, the velocity of the car tells us how quickly and in what direction the car moves in the physical 3D space. But here the velocity vectors tell us how quickly and in what direction the state point moves in the non-physical state space.
Interestingly, when all these arrows are permanently fixed in time, do not change, they still prompt the system to evolve, to behave, and they fully determine all properties of this behavior. So the velocity vector field of the device plays the role of the ruling force which dictates the system what to do in every possible situation.
