The Plasticity Principle

The Plasticity Principle describes my conceptual model of what implicit learning is. The core idea is that there are a lot of damn neurons in the brain and even more synapses, and if we start with the assumption that every one of them is plastic (i.e., changeable), what are the consequences of that?

The first is that it is not correct to think of the brain as a bunch of static processing systems like the visual system, the motor system, control systems, the memory system. Instead, we think of every one of those systems as capable of being shaped by experience. The goal of this learning is to continually refine and optimize processing. We don't know exactly how this learning process will go because we don't yet know either the capacity and limits of this inherent plasticity nor exactly what optimal processing looks like.

Chasing those operating characteristics questions leads to psychological and cognitive neuroscience research on learning and memory processes. But I have a growing sense there are other more accessible implications.

1. The world shapes your brain (physically). On the upside, this is the basis for why Everything Bad is Good For You (in some cases). Games and other entertainments that create knowledge or strengthen cognitive skills actually enhance brain function. This happens because those systems strengthen through incidental practice.

2. On the downside, the world can create bad habits in your brain as well. Implicit racism is a good example of this. Regular co-occurence of negative ideas and minorities creates bias that affects your behavior that you don't even realize is there.

3. Cognitive training works. Practice with mental exercise improves cognitive function. Brain training helps with aging and probably in degenerative disorders as well. It will be cool to see how video games can be integrated with this idea.

4. The trick will be figuring out the right things to practice. You can certainly get good at specific skills that don't help much elsewhere. I'd say chess is a good example of this. Chess players are freakishly good at chess and chess memory. It doesn't mean they are very good at anything else, though.

Most of everything I've jotted down in this blog format is strongly influenced by the underlying idea:
5. Thinking about habits and perfection is motivated by marveling at how close to perfection habits can get. The upper bound on optimal performance is pretty amazing and that argues that this type of plasticity is pretty effective.

6. Teaching should incorporate skill development. Not all cognitive skills can be easily taught in games. The classroom is an environment where students will commit to acquiring some other types of skills and teaching should take advantage of that.

7. The Butterfly effect and Nature/Nurture. Most nature/nurture discussions do not take sufficient account of feedback effects through learning. If you assume there's a lot of plasticity in the brain, you should be very sensitive to feedback spirals. That is, a small push/advantage in a domain can cause you (or others who influence your environment) to direct more attention to that domain, which will push you further away from the mean as learning contributes. This will exacerbate the effect of small genetic differences, but only when the feedback loop isn't externally constrained. In the IQ world, things like race and gender have big impacts on getting the feedback loop started and keeping it going.

I think that humans' expertise in face processing might be an excellent example for #7. Mark Johnson's model of a small genetic/pre-wired push towards looking at faces is a good start. Ken Nakayama's examination of individual differences in face recognition ability contributes. His lifespan/ability graph showing the ability peaks in one's early 30s makes me intuitively confident that there's a big learning component. I should dig in and figure out why.

8. Statistical models of language processing. These capture more language than you'd think. And the concept is showing up in technology like Google. It ought to be more prevalent in recommendation algorithms like Netflix/Amazon, I think (but the argument isn't trivial).

Broadly, my thinking here is strongly influenced by John Anderson's Adaptive Character of Thought (his rational analysis of cognition). And I think also by Herb Simon's economic theory of Bounded Rationality (maybe less obviously). The idea of looking for the "operating characteristics" of implicit learning was developed with Larry Squire. I remember he was a fan of the phrase, although I think his model of nondeclarative memory was as a finite set of separate systems rather than a broad "every neuron" principle.

Anyway, is this a useful/interesting collection of implications? I can haz popular science book? Too much real science work to do right now anyway, but I'll keep collecting related thoughts here in the meantime.