Lecture 26: Strong Anticipation and Direct Perception (Turvey, 2019, Lectures on Perception)

In this final chapter, Turvey lays out the basics of the ecological approach to coordinating action with respect to the future. This is actually a key part of behaviour - we reach for things we know are graspable before grasping them, we move to intercept or avoid things before they are anywhere near us, and so on. The question is how do we do this.

The ecological approach cannot, of course, use prediction to solve this problem. Prediction entails representation, making educated guesses about what is coming up based on loans of intelligence we cannot ever pay off. The whole point of this book has been to lay out the argument that a) this approach is doomed to fail but that b) the ecological approach, grounded in laws, is different enough in kind to be a viable option. 

Coordinating with things not in the immediate present is therefore going to rely on lawful informational coupling to dynamical events, with several interesting consequences. 

Lecture 22: Ontology at the Ecological Scale (Turvey, 2019, Lectures on Perception)

After a book length set of preliminaries, we are finally getting to the meat of the positive case the ecological approach has to make. That case of course sits on top of a rejection of the Cartesian ontology that has been driving everything else; this chapter describe the first part of the ecological replacement, specifically affordances. 

Brains learn to perceive, not predict, the world

The current trendy way to think about brains is as predictive machines. Bayesian methods, free energy, generative models, and all the other things in Andy Clark's new book are where it's at. Brains have to predict because, in Clark's terms, we are 'surfing uncertainty' - one step from disaster as we control our behaviour using sensory information we had to spend time processing and that is therefore a few hundred milliseconds out of date. Our brains build models of the world that constantly generate active predictions about what sensation will tell us next, and our behaviour holds together to the extent that the models do.

The ecological approach provides a different job description for the brain (Charles, Golonka & Wilson, 2014). We are embedded in environments rich in information that specifies a wide variety of behaviourally relevant properties. Some of that information is prospective, that is, it is information available now that is about something in the future. Two examples are the information variables you can use to catch a fly ball; both are states of the current time that, when used, allow you to achieve a behaviour in the future (specifically, to be in the right place in the right time to catch that ball). Another example is tau and the various other variables for time-to-collision.

This post reviews a paper (van der Meer, Svantesson & van der Weel, 2012) that measured visual evoked potentials in infants longitudinally at two ages, using stimuli that 'loomed' (i.e. looked like they were going to collide with the infants). The data show that the infant brains were not learning to predict the world. Instead, neural activity became more tightly coupled to information about the time-to-collision. We learn to perceive, not predict, the world.

Prospective Control I: The Outfielder Problem

A couple of posts ago I raised the distinction between prediction and prospective control. I was trying to make the point that, if you are coupled to the right information, you don't need to be mentally simulating what's happening so you can run this simulation ahead and predict what's coming up. Prediction of this sort is invoked by representational cognitive scientists to cope with things like delays in the nervous system (e.g. Changizi's 'perceiving-the-present' framework). It's a risky business (if you make a mistake, you are suddenly controlling your behaviour according to an incorrect guess), and the better solution is prospective control. This is when you couple your behaviour to information in the world that doesn't tell you about the future, but that, if you use it for a while, will get you where you want to go.

People requested some more on this topic, and so here we go. People also requested something other than the outfielder problem, but I am going to start with this problem because it is still the best example, and I'll get into some more as we go to demonstrate it's not just baseball where this works.

Embodied solutions to neural delays: Information and Network Motifs

One of the bugbears of direct perception is the fact of neural delays. The transmission of signals through the nervous system takes time, and this means that there is a lag between something happening (at, say, the retina) and that event having consequences in cortex, let alone behaviour. In control theory terms, delays in a system can lead to instability in that system's behaviour as you are forced to make corrections that are then incorrect and must themselves be corrected.

It's typically suggested in psychology that these delays are compensated for via computational predictions; the nervous system 'perceives the present' by taking the lagged input and using it as the basis of a guess about what's going on now (e.g. Changizi et al, 2008, plus see this post from a while back). This is a problematic idea: if the perceptual control of actions is based on a guess compensating for a variable time lag, then the stability issue remains, not to mention the consequences for mis-predicting the future. Regardless, it's not really an option for a theory of direct perception, and I want to discuss a couple of options.