Chapter 3

Understanding how the brain works

I've always been attracted to scientific articles on neurology and any theories that throw new light on the way the brain works. However, the brain is such a vast, complicated organ that all this information is fragmented and the theories that try to put all the bits together are widely diverse and highly controversial.

This results in a very hazy, incomplete picture that is acceptable only because it seems impossible that anyone could ever be able to explain in physical terms the complexity that provides us with consciousness, thinking and emotions.

Then, in March 2004, I was pointed to a Web page that contained a prepublication paper written by Professor Marc Lewis, of the Department of Human Development and Applied Psychology at the University of Toronto entitled "Bridging emotion theory and neurobiology through dynamic systems modeling".

My first impression was that it would be a formidable document to read. Over thirty thousand words long, it was packed full of technical names for various brain functions and structures , citing over 200 other papers for reference. Normally, I'd rapidly scan through such a document, looking to pick out the main points. However, in this case, I was interested to see how the author was including the concept of complexity in his explanations and printed out the paper for a more intensive study.

As I worked through this paper, I felt a growing excitement. Here, for the first time ever, I was getting a sense of the overall working of the brain. I began to understand how the various structures in the brain interacted to bring about the phenomenon of perception; how they could arrange for us to think, plan and make decisions. I began to understand how neurological circuits can hold memories and induce emotions. I was able to see how information carried in our genes could influence our thinking and behavior.

It produced on of those rare Eureka moments, when a haze lifts from understanding and everything suddenly becomes crystal clear.

My first reaction was to share this knowledge with members of an Internet discussion forum I belonged to. This was a group of technical specialists, where the phenomenon of human perception had been the center of interest for over fifteen years. I briefly explained the essence of the paper and gave them the address of the Web page for them to read it for themselves.

To my surprise and dismay, all the comments were negative and dismissive. Some claimed the paper was theoretical gobbledegook, others claimed it was all old hat and the theories had been disproved long ago. Not one single person had experienced anything like the breakthrough in understanding that I'd experienced.

As a writer, I deal in concepts and explanations. As a consequence, I'm always interested in how people think and understand things. I become intrigued whenever other people come to conclusions differing from my own. The first question I ask myself is "Are they missing something or am I missing something?". This can usually be answered by asking a few probing questions and prompting a discussion.

Soon it became clear that everyone who was commenting negatively about the paper weren't picking up on the fact that the paper was based upon dynamic systems modeling - even though this was in the title. Discussion soon revealed that it wasn't being picked up because nobody had been able to conceptualization this idea. Any paragraphs that referred to dynamic systems had simply been passed over, much the same way as an unfamiliar word might be passed over. It was no wonder nobody had been impressed with the paper, the key element was missing.

My attempts to explain to the discussion forum the nature of dynamic complex systems and the importance of attractors were a complete failure. The concept was so foreign to anything they had encountered before that they simply turned off. Those that did try to make the effort to read my explanations were trying understand complexity in terms of the concepts they were already familiar with and were coming up with nonsensical conlusions.