Affordances for the Spatial Structure of Reach-To-Grasp (Mon-Williams & Bingham, 2011)

I have reviewed the spatial and temporal structure of reach-to-grasp movements, and the task dynamical analysis that has connected affordance properties and reach-to-grasp effectivities. Now it's time to work through some papers exploring this task using this analysis.

The first paper is Mon-Williams & Bingham (2011). The goal of this paper was to investigate what target properties shape the spatial structure of the reach-to-grasp movement. This means the behaviour of the hand as it forms a grip aperture and then encloses and contacts the object. Specifically, we want to examine the maximum grip aperture (MGA), which occurs partway through the reach and is larger than the object, and the terminal grip aperture (TGA), which occurs when the hand has stopped moving and the hand encloses the object, but before the fingers are in contact with the object. The question is, what object properties determine how these apertures are scaled? 

The Task Dynamics of Reaching-to-Grasp

In the last post, I reviewed the basic form of the reach-to-grasp task and the basic spatial and temporal structure of the resulting reach-to-grasp action. I'm shortly going to review three papers by Bingham about where all this structure comes from, but first I wanted to sketch out the task analysis those papers will rely on. 

The question at hand is, in the context of reaching-to-grasp an object, what are the relevant object affordances? What follows is derived from Mon-Williams & Bingham (2011), which I will review fully in the next post. I've tried to fully flesh it out, though, to be as complete as possible. The goal is to lay out the likely relevant task dynamics; this leads to specific predictions about which manipulations should affect which parts of the reach-to-grasp action.

The Spatial and Temporal Structure of Reach-to-Grasp Movements

This post is part of my current series where I am developing a research programme to formally connect affordances and motor abundance analyses (see the intro post here). The first thing I need is a task that has a formal affordance analysis; my chosen task is reaching-to-grasp (sometimes called prehension). I'm pivoting to this task from throwing for a few reasons. First, I need a simpler task. The logistics of data collection and analysis for throwing tasks are large and I just don't have the resources right now; I do have the kit for these studies. Second, I want to expand my own skill set to a new domain, and reach-to-grasp is another venerable topic. Finally, it means I get to spend time really grappling with other work by Geoff Bingham, who was my PhD supervisor and who remains the person who's work I admire the most. 

This post is about the basic form of the reach-to-grasp movements studied in the lab, what some of the key measures are, and the fact these are sensitive to task demands (suggesting they are emerging from an active perception-action loop). In the next post I will work through the initial task analysis that leads us to candidate affordance properties. 

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?

What Science Has to Offer the World of Coaching

It started, as these things always do, on Twitter. Someone posted a training drill they were excited about (kids kicking a ball against a wall) and I made a comment to suggest I didn't think this was the most football-useful activity I had ever seen. That's all I intended to say, but enough people (coaches, mainly) got mad at me that more was said, and it quickly devolved into the standard entrenched lines this argument lives between.

There were two related themes to the replies. The first was essentially 'how dare you'; apparently questioning a coach's practice crosses a line (I admit I had been a little snarky, but only a little, I promise!). I find this response quite hard to understand: if you share your practice then it seems fair to expect not everyone will love it, and some may have sound reasons to think it's a bad idea, and I think that should be ok. 

The second theme was then 'what makes you think you can comment?'. This one mostly showed up in the form of demanding to know how much coaching I had done, a question designed to get me to admit 'none' and allow them to simply dismiss my view as that of an ill-informed outsider. This one annoyed me more than the rest, so I wanted to explain how and why I, an academic non-coach, gets to be a part of the conversation around training activities. 

First, let's try to set a positive tone. I value the experience and understanding that coaches have by virtue of their practice. Of course this expertise places you in a key position in the conversation. All I want to develop here is some reasons why other people, including people like me, also get to take part in that conversation, because we have relevant expertise and things to contribute. So let's talk about what science and scientists have to offer to the discussion about training environments.

Lecture 9: The Space Enigmas II: Kant, the Nature of Geometry, and the Geometry of Nature (Turvey, 2019, Lectures on Perception)

The first space enigma was the fact that vision lives in the two dimensions of Flatland, but produces an experience of three dimensional Spaceland. You can't logic or experience your way from Flatland to Spaceland (as described in the famous book). Berkeley tried to solve this problem by providing a guide, in the form of the Spaceland-dwelling body, but this fell apart and the only remaining suggestion was an unrepayable loan of intelligence from God. 

Another way to consider this problem that leads to another proposal is what Turvey calls 'the outness problem'. This is the annoying fact that sensations on the retina are experienced as things out there, in the world. This makes space a necessary precursor to perceptual experience: however the outness problem is solved, a notion of space is required to drive the search for a solution. Kant is the main person who worked to establish how space might be baked into perception; 'Space, therefore, is not an object of perception...but something very different, namely, a mode of perceiving objects' (Turvey, 2019, pg 124). Spoiler alert: it doesn't work, for interesting reasons that feed into the development of the ecological analysis. 

Trip Report from the Uncontrolled Manifold

I've spent the past few months getting a new paper to the 'complete first draft' stage (you can find a copy here in the meantime; it's still got some work to do though). It's about affordances, using targeted long-distance throwing as the task, and it's my first dip into the world of the uncontrolled manifold. I collected this data over five years ago, and it's been deeply satisfying to actually use it after all this time.

Part of what's taken so long is that I've had to learn the details of the uncontrolled manifold analysis. I blogged some about it here and here but this was the year I finally had the time and data to actually get into the maths. I still really like it as an approach to analysing human movement, but learning the details and trying to figure out how to get affordances into it has raised a lot of interesting questions about how it gets used right now and what this all implies for how we think movement is controlled. I'm raising a bunch of these issues in the paper but I wanted to sketch some out here for comment.

Broadly (and this shouldn't be a surprise to anyone really) I've realised that UCM is only a method, not a theory, and it's therefore not able to serve as a 'guide to discovery' about movement control. However, it's being used as if it can, and to be honest I was quite shocked at how carelessly it's being used in the literature.