You Cannot Perceive a Relational Affordance (A Purple Peril)

One of the more enduring arguments in ecological psychology is about the best way to formally describe affordances. The two basic approaches are that they are dispositions (Turvey, Scarantino, me) or that they are relations (Reitveld, Kiverstein, Chemero). The argument has mostly settled down into just agreeing to disagree, but I am still convinced that the relational analysis is critically flawed and I want to try and either get them to solve the problem or end the debate once and for all. I've reviewed this in a bunch of places (e.g. here, here, and here)  but this post is just setting out my challenge once and for all; you cannot perceive a relational affordance, and there is as yet no good story about how to learn new affordances.

My problem stems from this Gibson (1979) quote (we all have our favourite, but this one seems to cut to the heart of it)

The central question for the theory of affordances is not whether they exist and are real but whether information is available in ambient light for perceiving them.

Right now, the affordances-are-relations camp have no story for how these can structure light (or other energy media) and therefore create information about themselves. They are therefore, as currently formulated, not even in principle perceptible. This means affordances-as-relations is of zero use to the ecological approach. 

Bruinberg et al (2018) tried to address this problem, but as I blogged here their solution is not ecological information and it reveals that these authors do not as yet understand what information actually is. My challenge is therefore this: tell me a story in which affordances-as-relations are able to create ecological information in energy arrays, and might therefore be learned, and the debate will be back on. Until then, affordances-as-dispositions is the only account that formalises the right properties and the debate is over. 

The Evolution of Sex Differences in Throwing

One of the most robust sex differences occurs in throwing. Men can throw (on average) much faster and therefore much farther than women, and this gap even exists at comparable levels of sports such as baseball and softball. The most common explanations are that a) men are, on average, larger and stronger than women, and b) most cultures gender throwing activities as male, leading to earlier acquisition and much more practice. YouTube has plenty of videos of men throwing with their off hand that point to the critical role of learning. 

However, Lombardo & Deaner (2018; L&D) have just published a hypothesis that while these factors are at play, they rest on top of an underlying biological advantage and that 'throwing is a male adaptation'. Specifically, they claim that there has been greater selective evolutionary pressure on men (as compared to women) to develop the strength, skills and anatomy needed to throw for large distances and great accuracy. Men have evolved to be better throwers than women.

This post will briefly review the hypothesis and the evidence, and then come to two conclusions. First, many of the differences they discuss seem quite closely aligned to the cultural sex differences around throwing that we know exist and so may not be biologically innate. Second, and more importantly, there may not even be a throwing-specific sex difference to explain. Right now, the only clear finding is that men throw faster; but they are also (on average) stronger and larger for non-throwing reasons. There is, as yet, no clear evidence that men are better throwers. I will then review some recent data of my own that suggests when the full perception-action task dynamic is analysed in closer detail, trained women show every sign of being equally skilled throwers as trained men.

The Ecological Approach to Virtual Reality

As virtual reality (VR) gear gets better, cheaper, and easier to use, there is renewed interest in trying to figure out how best to make a virtual environment feel real.  The typical for framing for VR is in the name: it's about creating the illusion of reality, a virtual world. Programmers even talk this way; they describe creating virtual (pretend) environments, objects, spaces, etc. From this point of view, VR is an attempt to create a stable illusory experience by faking a world that doesn't really exist. 

Of course, VR programmers don't make worlds; they make information. This makes folding VR into the ecological approach a natural move, and I propose that ecologically, VR development is actually an attempt to design an optic array containing information that can support certain behaviours. It's less virtual reality, and more virtual information. This is important because the nature of the information we are using explains the form of the behaviour we are controlling. Your goal as a developer is therefore not to create tricks and illusions, but to provide information capable of supporting the behaviours you want to be possible,

As a first step towards an ecological understanding of VR, I will first follow the path Gibson laid down taking the science of perception away from illusions and towards information. I'll then think about some of the implications of taking an ecological approach for VR design. Virtual reality needs our theory of perception to become the best it can possibly be, and I hope that this post serves as an entry point for designers to become aware of what we have to offer them.

Affordance Maps and the Geometry of Solution Spaces

I study throwing for two basic reasons. One, it is intrinsically fascinating and I want to know how it works. Second, it's become a rich domain in which to study affordances, and it is really forcing me to engage in great detail with the specifics of what these are.

My approach to affordances is that they are dynamical properties of tasks, which means that in order to study them, I need to be able to characterise my task dynamics in great detail. I developed an analysis (Wilson et al, 2016) to do this, and I also have a hunch this analysis will fit perfectly with the motor abundance analyses like UCM (Wilson, Zhu & Bingham, in press). I have recently discovered that another research group (led by Dagmar Sternad) has been doing this whole package for a few years, which is exciting news. Here I just want to briefly summarise the analysis and what the future might hold for this work.

General Ecological Information Does Not Support the Perception of Anything

One common critique of the ecological approach is how can we use perception to explain behaviour that is organised with respect to things in the world that aren't currently in our area? How do we plan for future activities, or how do we know that the closed fridge has beer? 

A recent attempt to get ecological about this comes from Reitveld & Kiverstein (2014) who propose a relational account of affordances that enables them to talk about opportunities for more complex behaviours. This account has developed into the Skilled Intentionality Framework (e.g. Bruineberg & Rietveld, 2014), where skill is an 'optimal grip' on a field of task-relevant, relational affordances. 

I have always had one primary problem with this programme of work - I don't believe that they can show how these affordances create information and thus can be perceived. I discuss this here and here, and there's comments and replies for Rietveld and Kiverstein there too. You can indeed carve the world up into their kind of entities, but if they don't create information then they cannot be perceived and they are irrelevant to behaviour. 

I was therefore excited to see a new paper from the group called 'General ecological information supports engagement with affordances for ‘higher’ cognition' (Bruineberg, Chemero & Rietveld, 2018; hence BC&R). There is a lot of excellent work in here; but their proposal for a general ecological information is, in fact, neither ecological nor information. It is a good way of talking ecologically about conventional constraints on behaviour, but it doesn't make those perceivable and so the main thesis of the paper fails. 

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.

A Test of Direct Learning (Michaels et al, 2008)

Direct learning (Jacobs & Michaels, 2007) is an ecological hypothesis about the process of perceptual learning. I describe the theory here, and evaluate it here. One of the current weaknesses is little direct empirical support; the 2007 paper only reanalysed earlier studies from the new perspective. Michaels et al (2008) followed up with a specific test of the theory in the context of dynamic touch. The study was designed to provide data that could be plotted in an information space, which provides some qualitative hypotheses about how learning should proceed.

There are some minor devils in the detail; but overall this paper is a nice concrete tutorial on how to develop information spaces, how to test them empirically and how to evaluate the results that come out. The overall process will benefit from committing more fully to a mechanistic, real-parts criterion but otherwise shows real promise.