Matthew Cobb (right) is a British neuroscientist who blogs on occasion on Darwinian evolutionary biologist Jerry Coynes Why Evolution is True blog. Despite this inauspicious hobby, he has written a good essay in The Guardian, Why your brain is not a computer on the shortcomings of the computational model of the brain:

And yet there is a growing conviction among some neuroscientists that our future path [to understanding how the brain works] is not clear. It is hard to see where we should be going, apart from simply collecting more data or counting on the latest exciting experimental approach. As the German neuroscientist Olaf Sporns has put it: Neuroscience still largely lacks organising principles or a theoretical framework for converting brain data into fundamental knowledge and understanding. Despite the vast number of facts being accumulated, our understanding of the brain appears to be approaching an impasse.

So true. Philosopher Roger Scruton (19442020) said it best (I paraphrase): neuroscience is a vast trove of answers with no memory of the questions. Cobb continues:

For more than half a century, [neuroscience has been] framed by thinking that brain processes involve something like those carried out in a computer. But that does not mean this metaphor will continue to be useful in the future. At the very beginning of the digital age, in 1951, the pioneer neuroscientist Karl Lashley argued against the use of any machine-based metaphor.

Descartes was impressed by the hydraulic figures in the royal gardens, and developed a hydraulic theory of the action of the brain, Lashley wrote. We have since had telephone theories, electrical field theories and now theories based on computing machines and automatic rudders. I suggest we are more likely to find out about how the brain works by studying the brain itself, and the phenomena of behaviour, than by indulging in far-fetched physical analogies.

Cobb is rightmodels of the brain tend to track with the latest technology. To some ancient philosophers, the brain worked by making heat, like a fire. To Descartes the brain was hydraulic. To 19th century materialists, writing amid the Industrial Revolution, the brain was a machine. To 21st century materialists, the brain is a computer. Our tools at hand become our metaphors.

But metaphors are not metaphysics. Often, metaphors lead us astray. In some sense, the atom is like a little solar system, with electrons orbiting the nucleus like planets orbiting the sun. But quantum mechanics revealed dynamics utterly unlike the solar system model of the atom pictured by early twentieth century pioneers in the field like Ernest Rutherford and Niels Bohr.

Similarly, the eye is in some ways, like a camera. But if you understood only cameras and did not understand ocular physiology and neurophysiology, you will understand pitifully little about the eye.

So, what is the brain? How does it work? As Scruton noted, we need to make the questions more clear. Several questions are embedded in the issues Cobb raises.

One at a time:

1.Is the brain a kind of computer? First and foremost, the brain is an organ and it does organ thingsit metabolizes, secretes, generates action potentials and neurotransmitters, etc. But is it also a computer?

The answer depends on how you define computation. If computation is the mapping of an input to an output according to a set of rules (which is the usual broad definition of computation), then some aspects of brain function are computation. There are inputs (sensory inputs, electrical and chemical stimuli, etc.) and these inputs are in some situations mapped to outputs (transmission of action potentials, secretion of neurotransmitters, reflexes, etc.) according to rules (neurophysiological principles). Perhaps this application of computation to brain function is trivial, perhaps not, but in this sense some aspects of brain function are computational.

As well see below, however, not all aspects of brain function are computational, so the brain cannot be described entirely as a computer. And I would point out to Cobb (who is a materialist and atheist) that computation intrinsically entails teleology which (by Aquinas Fifth Way) demonstrates the existence of God. Atheists should be careful about computational models in biology, because computation is the product of intelligent design. Computers and software dont just happen by themselves.

2.What is the relation between the brain and the mind? Cobb is a materialist so I presume he discounts dualism. However, abstract thought (as classical philosophers pointed out) is inherently an immaterial ability and thus it cannot arise from the brain or from any material organ. Concrete thought can be material in origin but that view presupposes a metaphysical understanding of matter that is considerably more sophisticated than Cobbs materialism. Hylemorphism is the best metaphysical perspective from which to understand the material and immaterial powers of the mind.

3. Is the mind a kind of computation? No. In fact, the mind is the antithesis of computation. The reason is obvious when you think about it. Mental activity always has meaningevery thought is about something. Computation always lacks meaning in itself. A word processing program doesnt care about the opinion that youre expressing when you use it. A digital camera doesnt care what youre taking a picture of. In fact, the great utility of computation is that it doesnt have its own meaning so you can use it as a substrate to express any meaning you choose. Because the mind always has meaning and computation never has meaning, the mind is not computation. In fact, the mind is the opposite of computation.

Succinctly, the brain is an organ and some of its functions can be described as computation. The mind is obviously related to the brain but the relationship is complex and is best understood from the perspective of hylemorphic metaphysics. Concrete thought arises from brain function but abstract thought is inherently immaterial. Although abstract thought is influenced by brain function, it does not arise from it. The mind itself (as distinct from the brain) is no form of computation, and in fact the mind is the antithesis of computation.

Cobb is in the right track in critiquing the computational model of the brain and the mind. His materialism prevents him from following his genuine insights to their logical conclusion: Human beings have souls with material and immaterial powers, and some of the material powers are caused by the brain.

Cobb is shortly publishing a book, The Idea of the Brain, of which his essay in The Guardian is an edited excerpt. It looks like a worthwhile read. He understands the limitations that plague the philosophical basis of modern neuroscience but he needs to think more clearly about the source of the metaphysical errors that plague neuroscience, which is materialism.

See also: Did consciousness evolve? A Darwinist responds. Jerry Coyne argues that consciousness is a mere byproduct of useful traits that are naturally selected. But wait (Michael Egnor)

Further reading on the brain as a computer (or probably not)

We will never solve the brain. A science historian offers a look at some of the difficulties we face in understanding the brain. In a forthcoming book, science historian Matthew Cobb suggests that we may need to be content with different explanations for different brain parts. And that the image of the brain as a computer is definitely on the way out.

Why the brain is not at all like a computer. Seeing the brain as a computer is an easy misconception rather than an informative image, says neuroscientist Yuri Danilov.

Brains are not billions of little computers. Despite the hype. Also, life forms are not machines and neurons are not neural networks.

The brain is not a meat computer. Dramatic recoveries from brain injury highlight the difference. (Michael Egnor)

The brain exceeds the most powerful computers in efficiency.

and

Some people think and speak with only half a brain. A new study sheds light on how they do it.

Originally posted here:
The Mind Is the Opposite of a Computer - Walter Bradley Center for Natural and Artificial Intelligence