How motor skills are different from language

Some people like to argue that the development and learning of motor skills in humans is a lot like the development and learning of language in humans: intertwined, hard to separate, probably with a large element of hardwired knowledge and only a few parameters to tune through experience.

And sure, it may be helpful to think of motor skills this way, and people talk of motor primitives (kind of like words) and grammars that tell you how to combine them, which further helps with the analogy. From a computational standpoint, of course, natural language is a fascinating system clearly governed by rules that nonetheless admit a large degree of fuzziness in practice. Despite several good linguistic models of language production, it's still really hard to make a computer do a decent job of things like conversation and translation. There is, though, spectacular progress, and a whole body of research on the subject. So if motor skills are like natural language, then maybe we can apply things we know about the latter to the former. "Natural" motor skills: their fluidity, robustness to perturbation, and extremely precise yet very compliant control, are hard for robots perhaps the same way language is hard for computers. And it's quite probable that movements are compositional the way sentences are.

But I'd like to point out three fundamental differences.

1. Kinds of compositionality

Utterances are compositional according to the rules of grammar. Things like "apod ihfoa dhf" or "but the why cow give tree storm" don't count as language, even though they are strings composed of letters or phonemes (or words) and strung together. On the other hand, if I start twitiching and winking and throwing my limbs about, this is acceptable movement and maybe even a dance. The point is: movement is compositional (composed of little things you can do called motion primitives) but nothing dictates its acceptability other than physical laws. If a motion sequence is impossible for a human being, it's because the joint doesn't bend that way, or the muscle isn't strong enough, or the required degree of freedom is lacking from our body etc. On the other hand, as demonstrated above, I can make any number of utterances that don't mean or communicate anything because they don't obey the restricted, symbolic compositionality rules (grammar) of a language. Why is this distinction relevant?

2. Kinds of available information for learning

How utterances vs. movements are created, and which ones are acceptable is relevent because it directly bears on the question of how a human infant/child could possibly learn the two sets of skills. It's widely accepted in linguistics that there isn't enough information (enough examples of what does and doesn't constitute a grammatical sentence) in order to learn (infer) a complete grammar from nothing. The conclusion is that we must be born with a language organ in the brain that essentially has a hardwired grammar, the parameters of which need to be set by learning via exposure to a particular language. In particular, children don't hear nearly enough ungrammatical utterances, nor are they usually corrected when they say ungrammatical things, in order to reliably identify the grammar of a language.

Does the same hold in the case of motor skills? Let's see. Every waking minute of every day of our lives, we move our muscles and receive sensory feedback on our movements. We actively send neural control signals to our muscles and directly sense what happens as a result for everything that we do, including sitting, standing, breathing and blinking. Clearly, there are instinctive motor skills such as breathing and blinking that don't require any learning. We have the required neural circuitry at birth. But sitting and standing does not come at birth. Nor do any directed limb movements. But those muscles too get commanded relentlessly and at a high rate. And also relentlessly, at a high rate, we perceive the results of our actions in the form of sensory signals that come from proprioception, touch, and vision (and also hearing and smell and taste sometimes). But mostly proprioception. So it seems like a true wealth of experience to draw on for learning how to move.

Notice also, that there is a wealth of negative experience or negative examples of how not to move for human infants. They lose their balance all the time, they stumble, they fall, they bite their tongues ... They control their muscles in all these ways they shouldn't be, and they immediately get a negative failure signal due to the laws of physics. Again, there is plenty to learn from. And this disparity is specifically due to the fact that motor skill compositionality is not based on rules for stringing symbols, but directly and only on the physical capabilities of the body and the physical universe.

3. Kinds of goals

Utterances are vehicles for communication. Movements are vehicles for displacement. While the intended meaning of an utterance changes with its grammatical structure, any movement that achieves the desired displacement is generally acceptable.

There is surely more to say on the subject, but these three things lead me to believe motor skills are more learnable and less hardwired than linguistic abilities.

Engineering, not computation?

I heard Noah Cowan of JHU give a great talk today. He talked about having an engineer's perspective, a systems perspective, applied to scientific questions in biology. There is so much to be learned about the neural control of animal behavior, and he and colleagues are figuring out how animals close the sensorimotor feedback loop. It turns out, for example, both theoretically and experimentally that cockroaches can't be using simple proportial control when they are running at 1.5m/sec along a wall, but a PD model does in fact predict pretty well the roach motion. Or there are these weakly electric knifefish that really like hiding inside tubes. If you move the tube around, it will look like the fish is attached to it with a spring as it follows along. And it turns out that a harmonic oscillator (spring-mass) mechanics model in fact correctly predicts the behavior at various frequencies of moving the tube around, whereas a kinematic model does not.

As I payed close attention to the controller-plant-feedback diagrams, the second-order differential equations and the low-pass, high-pass and band-pass filter discussions, I noticed that all of these useful tools have nothing to do with computation or a computational worldview. The engineer has a formidable toolbox for use in the science of neural control; the progress that can be made is remarkable. But what can a computer scientist bring to the table? What models, what theoretical tools?

It's pretty obvious to me that computation won't be helpful in figuring out the science of animal motion, in all its graceful and fast glory. So what kinds of biology questions might we address armed with our understanding of data structures, algorithms and complexity? Ideally, questions beyond proving this or that purportedly intelligent activity is NP-hard? My hunch (and I'm betting my research on it) is that our computational tools will be best employed in asking and answering questions at a level above that of the neural control of one organism. Instead, we can study the interactions, communications, and group-level behaviors of many organisms.

Bayesian models of human cognition notwithstanding.