Do Affordances Select or Control Actions?

I've been working for a while to connect the perception of affordances to motor abundance methods such as the uncontrolled manifold. I proposed the idea in this book chapter, and then I have a paper under review that is my first swing at making this work; the details of the hypothesis are in those, and I recommend you read them. The paper in particular has my most recent thinking spelled out as clearly as I can in the Introduction and Discussion sections. 

As I've been chatting to people about progressing this programme, one thing that keeps coming up is 'why has no-one thought to do this before?'. This link simply seems so obvious to me, and to the people I'm talking to, but no-one has done it. I think I may have figured out why, though, and I thought I'd document the reason because I'm a sucker for these kinds of mysteries, especially when I think I've solved them. 

I think the reason no-one has thought to connect affordances to UCM is that (almost) no ecological psychologist has been studying affordances as a solution to the control problem! Affordances are studied as solutions to the action selection problem only, and this seems to come from Warren. I'll link to some papers and lay out some thoughts for the record here. 

Effectivities for Reach-to-Grasp Actions

I just reviewed the affordance properties that produce the spatial structure in reach-to-grasp actions, and there's an unquestioned assumption lurking in that analysis. Luckily, Mon-Williams & Bingham (2011) actually did question it, so I wanted to cover that part of the paper here.

The assumption in the analyses I described last time is that the unit of action is the aperture between the finger and thumb, and not the fingers themselves. Bingham refers to this as an opposition vector (Iberall, Bingham, & Arbib, 1986; van Bergen et al, 2007). In some ways, this is a weird idea; the action system working to control a space between limbs, and not the limbs! Smeets & Brenner (1999) proposed that grasping is actually about the two limbs. Mon-Williams & Bingham tested these hypotheses and found evidence in favour of the opposition vector.

I want to walk through this in a little detail, though, as of course identifying the relevant elemental variables is part of an UCM analysis, and affordance research helps here too. The task analysis that reveals affordance property options also points to effectivity property options (at least it should - these are complementary after all!). But another part of the UCM approach is that it can, in principle, test hypotheses about elemental and performance variables, so I want to lay this out as well.

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.