Last updated: Thu, Jun 29, 2017
You've already read about two models of pain—the specificity model of pain and the gate-control model. Models of this sort represent some phenomenon in a way that ideally is both accurate and succinct. Much of the power of science derives from its ability to develop and substantiate such models. The germ theory of disease (that contagious diseases are caused by “germs”) is an example of a simple, successful model. Newton's formulation of gravitation as a set of equations has sent satellites to the edge of the solar system and men to the moon and back. The power of such models is that they boil down what you need to know to make accurate predictions. They turn a lot of facts into a single powerful idea. They allow nature to be successfully manipulated.
In medicine, models are used to guide research and to develop treatments. If the treatment is well-engineered and based on a valid model of disease processes, it will be effective. If not, it likely won't be.
Michael Schermer, professional skeptic and sometime columnist for "Scientific American" magazine, has suggested that the inclination to see regularities in the world is a strong basic drive shared by all of us. He coined the term "patternicity" to describe our tendency to see patterns in our world. He has argued that the logic of survival favors those who accept that which is apparent as that which is true. If Schermer is correct (and I believe that he is), we are all by nature scientists, but not by nature good scientists. We have a built-in bias to jump to conclusions--to over-generalize, to form models of the world uncritically.
Scientifically, then, is not a natural way of thinking about the world. Scientific method is an artifice, an invention, a kit of tools that have been developed that allow us to access more truth and avoid more error than we might otherwise. A critical and very basic part of the scientific technique is clarity. The ideal scientist declares his assertions clearly: he defines his concepts clearly, and he proposes their relationships clearly. The result is a model that, if true, implies certain observable outcomes. A proposed model is often called a hypothesis, and checking the model against reality is called hypothesis testing. Lacking clear definitions, a clear model, or a clear hypothesis, there is great opportunity for misunderstanding, for miscommunication, and for our propensity to believe what we will to take over.
This ideal scientist, the one who is perfectly clear about everything, unfortunately doesn't show up until after most of the hard work has already been done and the textbooks are written. It is very common that concepts and models change as scientific exploration proceeds, and in fact a clear and concise model that fits the facts is the goal, not the starting point, for science. Haziness can reflect how far the scientific endeavor is from its endpoint, or it can reflect sloppy practice (or both). In either case, though, lack of clarity in terms, model, or hypothesis degrades the truth value of any conclusions that can be drawn.