Motor Abundance & the Affordances for Reaching-to-Grasp

Movements are never the same twice, even when you are trying to do that same thing over and over. Variability is an inescapable fact of trying to organise and run a complex system such as a human body. But there is more than one source of variability in movement; there's noise, and then there's redundancy, and these are not the same thing. 

Our movement systems are redundant; specifically, they always have more degrees of freedom available than are ever required to perform a given task. This means that there is always more than one way to perform any given task, and this can range from slight variations to complete reorganisations. 

Redundancy is a feature, not a bug. It means that we can reliably achieve a task goal in the face of perturbations that range from trial-to-trial fluctuations in execution up to surprises like tripping or the sudden appearance of an obstacle. However, it poses two related control problems. First, a problem of action selection: given that there are many functional organisations of degrees of freedom that could solve that task, which do we choose, and why? Second, a problem of action control: once we have our degrees of freedom organised, we still have some left over that need to be actively controlled; how do we do this, and why do we control them the way we do?

Part of the answer to these questions is the formation of a synergy. A synergy is a particular kind of way of organising system degrees of freedom, such that they work together to compensate for variability. A simple example is pressing down with two fingers (2 degrees of freedom) to produce a single total force (e.g. 20N; a 1 degree of freedom task). The system is redundant (2DOF working to control 1DOF) and so there are many ways to achieve the task (one finger could do 5N, the other 15N; or they could both do 10N, and so on). Trying to maintain a single force output is tricky though, and there will be variability. A synergy is present if, when one degree of freedom slips up, the other one automatically changes it's behaviour in a way that preserves the outcome goal. When a synergy is present, therefore, most of the variability will be along the line that describes where the successful outcomes live in the space of possible outcomes. In addition, no control needs to be exerted to control this variability away, because it's just the synergy doing what it was built to do (hence this particular subset of possible outcomes is often referred to as the uncontrolled manifold). 

So, which functional organisation of system degrees of freedom should we select? The one that operates as the synergy that compensates for variability in a way that preserves the task goal. If we select that synergy, it makes the control problem straight-forward (or at least, manageably low-dimensional). 

There are a variety of analysis methods that work to decompose variability in the performance of a task to look for evidence of a synergy at work; if most of the variability is constrained in a way that reflects the task goal, then a synergy is active. These methods include the uncontrolled manifold analysis, optimal feedback control, nonlinear covariation analysis and tolerance-noise-covariation analysis. 

I have recently been working with the uncontrolled manifold method in the context of targeted throwing, and using it to develop a detailed analysis of the task as well as trying to connect it to the perception of affordances. This work has clarified something very important for me: I really like the underlying motivation for the motor abundance methods, and I think they are all trying to meet the problem of action control head on. However, they are all, in general, completely underconstrained methods right now, because none of them come with a theory of what determines how the perception-action system identifies the synergy to enact. They can all spot one in the data if it's there, but they don't explain how it came to be. So, like dynamical systems, what we have is a good toolkit in search of a good theory, and, as usual, the solution is the ecological approach; specifically, the perception of affordances. 

Affordances Define the Task Goal

Recall that all these methods decompose variability with respect to a task goal. Evidence for a synergy being present comes when this process shows most of the variability has been constrained to live along the solution manifold for that task goal; the subspace in the space of all the things you could be doing right now that allows you to complete the task. 

These methods currently come with researcher degrees of freedom in two places. First, the researcher gets to identify the synergy they think is currently at play (and based on my reading, researchers rarely explicitly justify this beyond some simple biomechanical considerations, if they even go that far). In UCM, these are called the elemental variables. Second, the researcher gets to identify what it is they think this synergy is designed to control; what variable(s) the degrees of freedom are automatically compensating to maintain. In UCM, these are called the performance variables. The analysis methods provide no systematic constraints on these; in principle this is useful, because you can use UCM on a variety of candidate combinations of elemental and performance variables to empirically figure out what a given task requires. In practice, though, people tend to just run and report one or two, without ever justifying the selection beyond a simple task analysis. 

Ecologically, a task analysis is about identifying the affordances of a task. This is a usefully constrained way of analysing a task - it's about identifying the unavoidable dynamical facts of the task space, and seeing what they offer. 

The Research Proposal

In a chapter about throwing affordance research, I lay out the plan: use a task dynamical affordance analysis to identify the task goal and thus meaningfully constrain the search for synergies in movement. In my recent first attempt, I used throwing because I had the task dynamical analysis (from this paper) and in 2017 I had the chance to collect the data with colleagues at the Carnegie sports science group here. I like this paper; but it is not quite fully fit for purpose, and the task is, I think, on the ambitious side for a first swing. In addition, the data collection for this kind of study takes a lot of time and specialised equipment and lab space, which is not impossible but makes the study harder to run. 

So, coming of writing the throwing paper and now having a clear idea of what I need, I am developing a research programme that will use a simpler task I can more easily collect data for, that will allow me still to do what I want, namely connect motor abundance measures to an affordance analysis and show they work well together. I also see Fajen's affordance-based control having a key role here. 

I have selected reach-to-grasp as my task, because I have the kit available to run the studies and it also has an existing task dynamical affordance analysis in the literature. Bingham has been using this task for decades, and there are three papers in particular that develop the affordance analysis, develop the complementary effectivity analysis, contains empirical data from relevant experiments, and models the data from an affordance perspective (Mon-Williams & Bingham, 2011; Bingham et al, 2014; Wang & Bingham, 2019. My first job is to get fully up to speed on the full details of this programme, so I will blog the papers over my next few posts to develop the story. I have already encountered some relevant other papers. In particular, this one from Smeets & Brenner (1999) that proposes a very different effectivity story. Bingham has data against it, but UCM etc are supposed to be able to test hypothesised control architectures and having clear alternative hypotheses to examine will be a good part of this programme. 

So please keep any eye on the blog and my Twitter feed over the next few weeks. I'm going to be developing questions I'll need help answering, and I definitely need people to work with here, particularly people with expertise the various motor abundance methods.