Have you ever watched from your window as your neighbor’s dog dumps in your garden. Perhaps you thought to yourself of your neighbor, “Ugh, I could kill that guy!” No doubt, we’ve all had moments where we used the word “kill” or “murder” without even really picturing the act itself. Most of us living in large cities experience this frequently with other drivers on the road.
To say, “I could kill that guy!” is an example of a conceptual blend, described in The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities by Gilles Fauconnier and Mark Turner. In a conceptual blend, we take two unrelated concepts and merge certain aspects. When we blurt out our murderous intentions, we’re not really stating that we want to kill someone, and listeners do not immediately run to the police. Our true intentions are usually obvious because we’re simply blending one aspect of the concept of murder with the current situation.
From murder’s concept, we take the level of anger we believe necessary to commit murder — not the act itself, not the inevitable consequences. To express our anger, we blend that part of the murder concept with the current situation, and listeners’ brains are wired to pay attention only to that salient part. The act, from our point of view, is effortless but it is this ability that separates us from other species and that challenges artificial intelligence researchers. How can we teach computers about concepts like murder when humans use these concepts in a variety of circumstances?
Crucial to understanding the central argument of The Way We Think is the notion of form. We humans have devised numerous ways of representing ideas and perceptions. Sheet music, for example, represents sounds you can make with an instrument. By creating forms, we give ourselves means for manipulating and understanding concepts, but it’s important to understand that we’re not manipulating the thing itself. Trained musicians do not look at a sheet of notation and see a jumble of notes. They hear the song inside their head. What gives the notation (the form) meaning is the object behind it.
Fauconnier and Turner use the analogy of Achilles’ armor: we recognize the warrior inside the armor by what he’s wearing, but the armor is nothing without the warrior inside. Frankly, the analogy didn’t do it for me, and it was only when I started thinking about real-world examples of form, of ways we have to represent ideas, that I understood the idea.
I’m still only two chapters into the book, and I haven’t hit upon anything that directly relates to my thinking about content management but the leading quote of the first chapter did strike a chord:
It is far more useful to view computational science as part of the problem, rather than the solution. The problem is understanding how humans can have invented explicit, algorithmically drive machine when our brains do not operate in this way. The solution, if it ever comes, will be found looking inside ourselves. (Merlin Donald, “Computation: Part of the Problem of Creativity” in Behavior and Brain Sciences 17:3)
If the problems with content management could be summed up in a few sentences, this quote would do it. The issue with content management is that the software has been designed with the assumption that organizations behave like machines — in structured, predictable ways. We therefore take the form (say, the flowchart for producing a regulation) over the object it represents (the actual interactions between people to create the document).