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.