Muscle Homology in Coordinated Rhythmic Movements

One of my main experimental tasks is coordinated rhythmic movement. This is a simple lab task in which I ask people to produce rhythmic movements (typically with a joystick) and coordinate those at some mean relative phase. Not all coordinations are equally easy; without training, people can typically only reliably produce 0° (in-phase) and 180° (anti-phase) movements. People can learn other coordinations, however; I typically train the maximally difficult 90° (although my PhD student has just completed a study training people at 60°; more on that awesome data shortly). I use coordination to study the perceptual control of action and learning.

My work is all designed to test and extend Bingham's mechanistic model of coordination dynamics. This model explicitly identifies all the actual components of the perception-action system producing the behaviour, and models them. In particular, it models the perceptual information we use to perceive relative phase; the relative direction of motion. This is an important contributor to coordination stability and this model is a real step up in terms of how we do business in psychology.

There is another factor that affects coordination stability, however, and the model currently only addresses this implicitly. That factor is muscle homology, and it's been repeatedly shown to be an important factor. For a long time, I have avoided worrying about it, because I have had no mechanistic way to talk about it. I think I have the beginnings of a way now, though, and this post is the first of several as I develop my first draft of that analysis.

Rates of learning and the dynamic pattern approach

One of the interesting features of coordinated rhythmic movement is that people start out with a particular pattern to their performance - there is pre-existing structure to our attempts to coordinate these movements. This structure affects our ability to learn new coordinations, and the pattern of the effects reveals a lot about the cause of this pre-existing structure. 

However, the literature is split into two incompatible accounts of learning, and trying to fix this is part of my ongoing interest in this task. The first account is the dynamic pattern approach, which was pioneered by JAS Kelso, and championed by modelling (Gregor Schöner) and behavioural studies (Pier Zanone). I'm more interested in the latter aspect, because it's the motivation for the former. I've already reviewed how this account fails, but it's still alive and well thanks to some creative history, and needs to be tackled again. The second account, which I prefer, is the perception-action account (Bingham) which developed from empirical work on visual and proprioceptive perception as well as action measures, and embodied in a model.

We haven't explicitly tackled the rate of learning issue, although we will and there is already support for our account in the literature (Wenderoth et al, 2002). But it comes up regularly in the dynamic pattern behavioural work, so it's time to work out what's going on in their data.

Chemero (2009), Chapter 5: Guides to Discovery

The dynamical stance laid out by Chemero in the previous chapter has a potential flaw (besides being a bit weak-ass) - it's not clear how it can serve as a guide to discovery. How do you do productive science taking this approach? Chemero is going to make two suggestions, only one of which I think works: first, he's going to suggest dynamical models such as the Haken-Kelso-Bunz (HKB) model can serve to stimulate empirical work even when they are entirely phenomenological. This approach is, I think, entirely incorrect, and this chapter is full of serious problems (only some of which are unique to Chemero). Second, he's going to suggest that Gibsonian ecological psychology can actually solve the problem much more robustly anyway, by serving as an underlying theory of behaviour. This will work better, and I would advocate Bingham's model of coordination as an exemplar of this, more promising route.

But first, the HKB model as guide to discovery (this chapter is largely the material from Chemero, 2000; I intend to turn this post into a paper to rebut that paper and point to the Bingham model as an alternative, so comments are especially welcome on this one). Time to get a little critical, I'm afraid.

Chemero (2009), Chapter 4: The Dynamical Stance

The problem with the theory of representation Chemero settles on in the previous chapter, as we shall see, is that it makes representation unavoidable. If there are representations, then RECS fails to get off the ground (remember, the radical bit is anti-representationalism). In this chapter, Chemero identifies a path past this problem (the 'dynamical stance'), explains why it works, but then concludes with a new problem, that of how to guide future discovery.

Coordination and the Haken-Kelso-Bunz Model

Other coordination posts are here.

Now we have the basics out of the way, we can explore some of the key characteristics of coordinated rhythmic movements.

Kelso's early experiments established a set of basic phenomena. Recall that, at the moment, the task is to wiggle your fingers in and out (towards the midline) at some mean relative phase and at some frequency. Moving your fingers in-phase (0° mean relative phase) has the fingers moving in the same direction at the same time, and entails the co-activation of homologous muscle groups in each hand.