Cognition Without Computation

Just because a theory is old doesn't mean it's correct

3 min read
A human head with a color overlay of the Earth and a series of 1’s and 0’s.

I'm just going to come out and say it: Human cognition might have nothing whatsoever to do with computation.

Yes, I am well aware that the computational theory of mind is a deeply entrenched one, starting with the work in the early 1940s of Warren McCulloch and Walter Pitts in Chicago, and then later at MIT, where they were joined by Jerome Lettvin and Humberto Maturana. But over the course of human history, lots of theories have been widely but wrongly held, sometimes for decades.

Consider the phlogiston theory of combustion. For over a century, starting in 1667, most scientists believed that what made disparate things burn was a common substance, later named phlogiston, that dissipated into the air via fire. Air had only a finite capacity to absorb it, which explained why fire was extinguished if there was only a small amount of air available.

By the end of the 19th century, Newtonian physics was pretty good at explaining the behavior of physical objects in the universe that could be directly observed. But right at the beginning of the 20th century, Einstein came up with two revolutions almost at once, the ideas of both relativity and that energy was quantized. The fallout from the second of these is quantum mechanics. Over 100 years later, its consequences are still being discovered and often still disbelieved. Quantum entanglement, which is leading to new forms of secure communications, is still routinely described as "spooky." Measuring a property at one place can cause something to change at another, and with a reaction time that is faster than the speed of light.

Today we all assume that intelligence, thought, cognition—whatever you want to call it—is a product of computation.

For the last 30 years it had been thought that the mechanism for Alzheimer's disease was the accumulation of amyloid protein plaques in the brain. A person with Alzheimer's always has clumps of plaque in the brain. However, recent trials with drugs that reduce these clumps failed to give relief from the disease. The theory has come under fire, and now it is postulated that the lumps of plaque are a side effect of the disease, not the causal mechanism. There have been accusations that alternate approaches to understanding the disease were underfunded, as the peer reviews of such proposals were viewed as out of the mainstream.

The point is that long-held theories get superseded pretty frequently, and then everyone gets to claim that they thought they were a bit kooky all along. And over time the heroes of any particular science sometimes get knocked out of the pantheon for being fundamentally wrong.

Today we all assume that intelligence, thought, cognition—whatever you want to call it—is a product of computation. Computational neuroscience is the respectable way to approach the understanding of these phenomena in all animals, including humans. And artificial intelligence, the engineering counterpart to neuro-"science," likewise assumes that to build an intelligent system we should write computer programs. In John McCarthy's proposal for the famous 1956 Dartmouth Workshop on AI, the field's foundational event, he argued precisely this position on the very first page.

Maybe conscious experiences come from some kind of self-organization.

Computationalism is not at all central to other aspects of our technology. New satellite-launch companies don't set out to write computer programs as the primary mechanism for getting people into orbit. Yes, there are inevitably a lot of computer programs involved, but the central mechanism is burning phlogiston-free rocket fuel with oxygen in a booster. Rocket engines turn that combustion into thrust, which conforms to classical Newtonian physics to escape the clutches of Newtonian gravity. A Python script by itself just can't get the job done. Likewise, crystals do not arise because there's something computing where atoms should go. The atoms self-organize as the result of the interplay of fundamental forces.

Inside your brain are two-dimensional sheets of neurons called maps. Researchers long ago established that neural activity in these maps corresponds very precisely to sensory stimulation, for example in the retina. Many of these researchers describe these neural actions as having been computed, and they contend that these computations are what give us conscious experiences of the world. But perhaps that is all wrong. Maybe instead these conscious experiences come from some kind of self-organization. The computation we associate with these sensations could be simply an invention of our own to explain the mechanism of sentience, not the primary cause of it.

Now that sounds both kooky and spooky to our current way of thinking. To me it is exciting. I like being a kook.

The Conversation (11)
Connor McCormick
Connor McCormick27 Oct, 2021

Difficult to imagine how any self-organization process would be indescribable with computation. Even if it is quantum mechanical, that too is simuable.

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Lynn Rasmussen
Lynn Rasmussen08 Nov, 2021

Neurons are either off or on. Bits. So at the level of neurons, the brain computes. Then neurons organize and emerge into neural populations and neural populations organize to emerge to whole-brain activity using the systems structures/processes of input across boundaries, information (the literal forming within), self-organization, hierarchy, anticipation/feedforward, active inference, attractors, chaos, emergence, and more. As Walter J. Freeman said in answer to a question from a physicist about the neural correlates of consciousness, "Trying to explain consciousness by looking at neurons is like trying to explain a hurricane by looking at molecules." In the 90s, his team demonstrated how micro (neural), meso (neural populations), and macro (whole brain) levels of brain activity operate at different scales, and each emerges to the next. EEGs with 110 leads on the surface of the scalp showed that the whole brain breaks down into chaos and organizes again 5 to 7 times a second. Karl Friston is on to this, using Bayesian statistics, active inference/anticipatory/feedforward processes, information theory, and more. So the brain computes, just in a much more interesting way using many more processes than self-organization.

Wolfgang Stegemann
Wolfgang Stegemann27 Oct, 2021

Neuroscience is naive in two ways. She believes in WYSIWYG and she thinks that life is about the same linear relationships as in inanimate nature: a → b and reducible b → a. This does not apply to all living things, because life forms emergences that are irreducible. Between a and b there are reaction cycles that cannot be reduced to one element, but only to principles.

The central principle on which life is based is 'self-organization', i.e. self-reproduction and self-preservation. Of course, this also applies to the way the brain works. Thus, the brain does not work according to the rules of Boolean algebra, but according to those of self-organization. Which are they? The core of self-organization is growth, i.e. agglomeration of the compatible, in the sense of expansion and reduction (see metabolism). For thinking, this is associating compatible patterns. So there is no YES, NO, OR, but rather superimpositions of states in decisions, which one could at best describe as MAYBE. These constant superimpositions as an assimilation of new things finally lead to accommodation (Piaget) in the sense of a summary according to the example 1 + 1 + 1 + 1 + 1 to 5x1.

However, if the edges are superimposed, there is blurring and the result is a blurred virtual hologram. Only when the sharpness is great enough is there a reduction. This reduces complexity, albeit not logically, but topologically.

Every psychological process is a physical one. Mental processes cannot, however, be mapped 1: 1 to physical ones, because they operate according to a different logic.

If one assumes that thinking proceeds in the manner of a pattern formation and comparison process, then learning can be understood as the superposition of similar, compatible patterns and their reduction. The best way to imagine it is this: A pattern, e.g. that of a tree, corresponds to a rasterized sheet of paper with different numbers between 1 and 2 in the boxes. A slightly different tree has the numbers between 1.5 and 2.5 and so on. If you put the sheets on top of each other and add the numbers vertically in the boxes, you get different sums. The points of the patterns of all trees are reflected in these summed numbers. Topologically, there are mountains and valleys. Mountains and valleys are thus reductions of many superimposed points. All mountains and valleys are connected to one another, but only the mountains communicate with one another. The communication of the mountains is different from that of all points. Of course, this communication runs through the valleys (how else could it work), but it is impulse patterns that respond to external and internal impulses. The impulse patterns are used to compare coarse-grained properties, i.e. the mountain peaks and, if necessary, a little 'down the mountain'. So we have two topologies, the physiological and one that is also physiological, but is in a superposition [not quantum mechanically] or forms a supersystem. Both topologies are physiological, the upper one appears psychological.

Consciousness arises from the difference between the two topologies and is an integral part of the neural system.

Patterns of the same class of objects (e.g. tree) are superimposed and form a topology in which the 'mountains' contain the typical features of all patterns and which make up that coarseness, as a difference to mere perception that makes consciousness possible in the first place. If required, each individual pattern can be activated in detail. Without this property of the neural system, every life situation would have to be saved for itself. This applies to non-central nervous systems, which represent the outside world via stimulus-response patterns.

This is exactly the point at which thinking, i.e. consciousness, becomes out of mere perception, and ordered structures out of chaotic stimuli. Possibly the 'mountains' form attractors that create a micro-readiness potential that reacts to corresponding impulse patterns. The 'mountains' could also be called neural micro-hotspots that communicate with each other.

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