I’ve been on the stump for mental models since, well, I left for grad school. My work colleagues, as a going away present, gave me a copy of Gentner & Stevens’ Mental Models. My Ph.D. thesis also incorporated them. (In it, I looked at how people used such models, and then looked at the effects of training interventions. TL;DR: there were some benefits!) However, I suspect that the idea is a bit complex, and I tend to err on the side of the conceptual (mea culpa!). So, I thought it was time to be getting concrete about mental models.
For background, mental models are simplified explanations about how some portion of the world works. They’re causal conceptual models that connect the relationships and how effects propagate through systems. Done well, they give us a basis to explain and predict outcomes. For performance, they give us a basis to make decisions; we can run them and see what the various outcomes of choices of actions would be. Then we can choose the best one. And they guide decisions in many different circumstances.
For instance, for something procedural, they tell us why we doing things in a certain order. For instance, when I’m cooking breakfast, I want the onions nicely browned. I know not to put them in before the pan heats, because my mental model of heat transference and vegetable properties lets me know that throwing them into a hot pan will brown them. Instead, if I throw them into a cold pan and raise the heat, they’re likely to just get soft. (There’re nuances around this, of course.) And this can stretch to let me know when to throw in various things, and also why to cook the meat, take it out, and then throw in the veggies. If I forget a step, my mental models can fill in and help me regenerate the necessary step.
Similarly for other decisions. In trouble-shooting, for instance, my knowledge of what makes cars work lets me know that if it’s not running, it’s likely one of two sources. Given that petrol engines require an air/fuel mixture ignited by a spark, I can suspect that either the fuel or electrical system is at fault. Then I can take steps to test each. (Ok, at least I could, back in the days of caburetors and distributors. Electronic ignition and fuel injection have thrown off my game!) Knowing the causal properties let me break down the possible contributors. It’s not going to be the brakes!
And so on. In fact, models guide many types of decisions. Good models, based on empirical research, such as cognitive load theory, give us reasons to do things like precede practice with examples. (Bad models, of course, like learning styles, lead us to waste time and money chasing unobtainable benefits.)
Research on mental models tells us several things. For one, our brains are always building models. It also tells us that once we have a model, we’re reluctant to replace it (no “please sir, may I have another model”), and instead, patch it. Thus, if we want to support the optimal performance, we should provide good models, and build their relevance through examples that show them, and addressing them in feedback.
I hope that the two examples here make the use of models a bit more comprehensible. The goal, of course, is that you start paying more attention to them in your learning designs. They’re not necessarily obvious to elicit, but they are there. Do try to find them, represent them explicitly, and refer to them in examples and feedback. Oh, and do avoid the bad ones (you can look to the research translators for guidance). They’re part of what makes us best at performing optimally, and so should be part of your learning solutions. (In other words, build and use your model of mental models! :)