Neuroscience raises a question about the possible distortions of the models we use to organise and shape our knowledge. With neuroscience it is quickly necessary to think about the relationship between observable brain functions and consciousness.
It’s become possible to make a direct correlation between specific states of mind and visible activity in the brain, which has led some effectively to equate brain activity and consciousness. But this ignores the fundamental philosophical challenge that consciousness offers. We are not aware of our consciousness as we are of other things, because that consciousness is the starting point for our awareness of other things. It’s hard to talk of our consciousness as if it was actually oneself rather than something we own, something separate, and yet we know that it’s not something separate. It is not like a hand or a leg which we can possess in the sense that we could also live without our limbs. It is, fundamentally, what we think of as our self (so loss of certain brain functions can equate literally to a loss of self).
When we seek to understand brain concepts we tend to use a metaphorical model. In the case of recent neuroscience, that model overwhelmingly is the computer. The computer is the readiest external analogy we have for the brain, and indeed a large chunk of computer science is dedicated to making computers more brain-like, to create effective artificial intelligence capable of understanding context and eventually programming itself (there is an AI concept of The Singularity, the point where computer intelligence starts to function independently and beyond the capabilities of human intelligence).
The irony here, as writers like Iain McGilchrist and Bryan Appleyard have pointed out, is that just as we want to make our computer machines more brain-like, in the way we conceive of the brain/mind we want to make the brain more machine-like, and this is a blinding metaphor, which is already pervasive (think how people will speak of humans being “hard wired” to behave in certain ways, or indeed the probably specious notion of neurolinguistic programming).
We’ve come to think of scientific analysis as a process of breaking things down to their constituent parts, understanding how those parts work together. Though this can be useful it is not the only means we have of pursuing an enquiry and when it comes to understanding consciousness it may obscure as much as it illuminates.
This kind of reductive analysis may be appropriate if the questions you want to answer are about the essence of something (at some level). So if you wanted to understand how colour exists in nature (or perhaps cure colour blindness) you would want to do some essential work around the chemistry of pigments, about light and optics and so on.
But if you wanted to understand why a painting moved you, discussing its physical chemistry would not get you very far. This is not a perfect analogy: consciousness doesn’t exactly present itself like a work of art (though actually the consciousness of other humans does). But if, as seems likely, the way consciousness arises in the brain is a complex phenomenon, then reductive analysis is unlikely to get you to the answers you want.
Conceiving of the brain as a computer is a reductive analogy. There is no reason to believe that the fundamental building blocks of brain activity are like binary code. Equally there is no reason to believe that increasing processing power will somehow help you leap from the world of binary processing to the world of the human brain. As Appleyard among others has pointed out, it’s hard to imagine computers becoming conscious like brains, because we are nowhere near being able to describe what consciousness is, or even what it is like (the philosopher Thomas Nagel has argued that one of the distinguishing features of consciousness is that we cannot say it is like anything else).
Dealing with complexity
In The Brain is Wider than the Sky Bryan Appleyard argues that the world is mostly complex, and unless we’re very careful, attempts to understand it through (reductive) simplification are bound to produce distortions. In this view he is explicitly influenced by McGilchrist, who in The Master and His Emissary moves from a review of the current state of neuroscience to a cultural history of the West. McGilchrist is unusual in that he’s a working psychiatrist, but has been a professional (academic) literary critic, and philosopher.
McGilchrist suggests that the left and right brain hemispheres, though always interdependent (in a healthy brain), process our experiences in very different ways. To summarise a usually complicated argument, he suggests that only our right hemisphere has direct experience of the world outside ourselves, and that it handles this experience holistically, being the part of ourselves capable of understanding metaphors, analogies and likenesses (it connects things). The left hemisphere, in contrast, takes the information flowing from the right hemisphere, and seeks to make analytic (reductive) sense of it, so that it can maintain close control of the situation associated with that information. To do this it seeks to exclude further information flowing from the right brain’s continued experience of the world.
McGilchrist is very aware that this way of talking is itself shaped by metaphor, but his claims are rooted in the empirical evidence of hemispherical dysfunction (the capabilities we lose when part of a hemisphere is damaged): our brains really are divided and there are differences between the two hemispheres. He goes on to suggest that our cultural history reflects a vying for superiority between the world views of our respective brain hemispheres. He argues that in the modern world, the left brain, analytic, reductive, fixated on control, has come to dominate thinking about experience, and what we should be doing. He argues that this isn’t a necessary way of looking at what we have, and in many respects is a misleading way.
But it is left brain thinking which sees the computer (machine) as a good model for mind. The left brain can relate to the way machines do things. We can see the left brain at work too in the routine attempts to oversimplify how organisations can be managed.