The mathematics of mind-time by Karl Friston

"Consciousness is not a thing but a process of inference'

Complex systems are self-organising because they possess attractors. These are cycles of mutually reinforcing states that allow processes to achieve a point of stability, not by losing energy until they stop, but through what’s known as dynamic equilibrium. An intuitive example is homeostasis. If you’re startled by a predator, your heartbeat and breathing will speed up, but you’ll automatically do something to restore your cardiovascular system to a calmer state (following the so-called ‘fight or flight’ response). Any time there’s a deviation from the attractor, this triggers flows of thoughts, feelings and movements that eventually take you back to your cycle of attracting, familiar states. In humans, all the excitations of our body and brain can be described as moving towards our attractors, that is, towards our most probable states.

On this view, humans are little more than ‘strange loops’, as the philosopher Douglas Hofstadter puts it. We all flow through an enormous, high-dimensional state-space of manifold possibilities, but are forced by our attractors to move around in confined circles. We are like an autumn leaf; tracing out a never-ending trajectory in the turbulent eddies of a stream, thinking our little track is the whole world. This description of ourselves as playful loops might sound teleologically barren – but it has profound implications for the nature of any complex system with a set of attracting states, such as you or me.

....we can talk about inference, the process of figuring out the best principle or hypothesis that explains the observed states of that system we call ‘the world’. Technically, inference entails maximising the evidence for a model of the world. Because we are obliged to maximise evidence, we are – effectively – making inferences about the world using ourselves as a model. That’s why every time you have a new experience, you engage in some kind of inference to try to fit what’s happening into a familiar pattern, or to revise your internal states so as to take account of this new fact. This is just the kind of process a statistician goes through in trying to decide whether she needs new rules to account for the spread of a disease, or whether the collapse of a bank ought to affect the way she models the economy.

Now we can see why attractors are so crucial. An attracting state has a low surprise and high evidence. Complex systems therefore fall into familiar, reliable cycles because these processes are necessarily engaged in validating the principle that underpins their own existence. Attractors push systems to fall into predictable states and thereby reinforce the model that the system has generated of its world. A failure of this surprise minimising, self-evidencing, inferential behaviour means the system will decay into surprising, unfamiliar states – until it no longer exists in any meaningful way. Attractors are the product of processes engaging in inference to summon themselves into being. In other words, attractors are the foundation of what it means to be alive. The Mathematics of Time Aeon Magazine