Predictive Processing

In a sort of curious coincidence, a few days after I published my last few posts, Scott Alexander posted a book review of Andy Clark’s book Surfing Uncertainty. A major theme of my posts was that in a certain sense, a decision consists in the expectation of performing the action decided upon. In a similar way, Andy Clark claims that the human brain does something very similar from moment to moment. Thus he begins chapter 4 of his book:

To surf the waves of sensory stimulation, predicting the present is simply not enough. Instead, we are built to engage the world. We are built to act in ways that are sensitive to the contingencies of the past, and that actively bring forth the futures that we need and desire. How does a guessing engine (a hierarchical prediction machine) turn prediction into accomplishment? The answer that we shall explore is: by predicting the shape of its own motor trajectories. In accounting for action, we thus move from predicting the rolling present to predicting the near-future, in the form of the not-yet-actual trajectories of our own limbs and bodies. These trajectories, predictive processing suggests, are specified by their distinctive sensory (especially proprioceptive) consequences. In ways that we are about to explore, predicting these (non-actual) sensory states actually serves to bring them about.

Such predictions act as self-fulfilling prophecies. Expecting the flow of sensation that would result were you to move your body so as to keep the surfboard in that rolling sweet spot results (if you happen to be an expert surfer) in that very flow, locating the surfboard right where you want it. Expert prediction of the world (here, the dynamic ever-changing waves) combines with expert prediction of the sensory flow that would, in that context, characterize the desired action, so as to bring that action about.

There is a great deal that could be said about the book, and about this theory, but for the moment I will content myself with remarking on one of Scott Alexander’s complaints about the book, and making one additional point. In his review, Scott remarks:

In particular, he’s obsessed with showing how “embodied” everything is all the time. This gets kind of awkward, since the predictive processing model isn’t really a natural match for embodiment theory, and describes a brain which is pretty embodied in some ways but not-so-embodied in others. If you want a hundred pages of apologia along the lines of “this may not look embodied, but if you squint you’ll see how super-duper embodied it really is!”, this is your book.

I did not find Clark obsessed with this, and I think it would be hard to reasonably describe any hundred pages in the book as devoted to this particular topic. This inclines to me to suggest that Scott may be irritated by such discussion of the topic that comes up because it does not seem relevant to him. I will therefore explain the relevance, namely in relation to a different difficulty which Scott discusses in another post:

There’s something more interesting in Section 7.10 of Surfing Uncertainty [actually 8.10], “Escape From The Darkened Room”. It asks: if the brain works to minimize prediction error, isn’t its best strategy to sit in a dark room and do nothing forever? After all, then it can predict its sense-data pretty much perfectly – it’ll always just stay “darkened room”.

Section 7.10 [8.10] gives a kind of hand-wave-y answer here, saying that of course organisms have some drives, and probably it makes sense for them to desire novelty and explore new options, and so on. Overall this isn’t too different from PCT’s idea of “intrinsic error”, and as long as we remember that it’s not really predicting anything in particular it seems like a fair response.

Clark’s response may be somewhat “hand-wave-y,” but I think the response might seem slightly more problematic to Scott than it actually is, precisely because he does not understand the idea of embodiment, and how it applies to this situation.

If we think about predictions on a general intellectual level, there is a good reason not to predict that you will not eat something soon. If you do predict this, you will turn out to be wrong, as is often discovered by would-be adopters of extreme fasts or diets. You will in fact eat something soon, regardless of what you think about this; so if you want the truth, you should believe that you will eat something soon.

The “darkened room” problem, however, is not about this general level. The argument is that if the brain is predicting its actions from moment to moment on a subconscious level, then if its main concern is getting accurate predictions, it could just predict an absence of action, and carry this out, and its predictions would be accurate. So why does this not happen? Clark gives his “hand-wave-y” answer:

Prediction-error-based neural processing is, we have seen, part of a potent recipe for multi-scale self-organization. Such multiscale self-organization does not occur in a vacuum. Instead, it operates only against the backdrop of an evolved organismic (neural and gross-bodily) form, and (as we will see in chapter 9) an equally transformative backdrop of slowly accumulated material structure and cultural practices: the socio-technological legacy of generation upon generation of human learning and experience.

To start to bring this larger picture into focus, the first point to notice is that explicit, fast timescale processes of prediction error minimization must answer to the needs and projects of evolved, embodied, and environmentally embedded agents. The very existence of such agents (see Friston, 2011b, 2012c) thus already implies a huge range of structurally implicit creature-specific ‘expectations’. Such creatures are built to seek mates, to avoid hunger and thirst, and to engage (even when not hungry and thirsty) in the kinds of sporadic environmental exploration that will help prepare them for unexpected environmental shifts, resource scarcities, new competitors, and so on. On a moment-by-moment basis, then, prediction error is minimized only against the backdrop of this complex set of creature-defining ‘expectations’.”

In one way, the answer here is a historical one. If you simply ask the abstract question, “would it minimize prediction error to predict doing nothing, and then to do nothing,” perhaps it would. But evolution could not bring such a creature into existence, while it was able to produce a creature that would predict that it would engage the world in various ways, and then would proceed to engage the world in those ways.

The objection, of course, would not be that the creature of the “darkened room” is possible. The objection would be that since such a creature is not possible, it must be wrong to describe the brain as minimizing prediction error. But notice that if you predict that you will not eat, and then you do not eat, you are no more right or wrong than if you predict that you will eat, and then you do eat. Either one is possible from the standpoint of prediction, but only one is possible from the standpoint of history.

This is where being “embodied” is relevant. The brain is not an abstract algorithm which has no content except to minimize prediction error; it is a physical object which works together in physical ways with the rest of the human body to carry out specifically human actions and to live a human life.

On the largest scale of evolutionary history, there were surely organisms that were nourished and reproduced long before there was anything analagous to a mind at work in those organisms. So when mind began to be, and took over some of this process, this could only happen in such a way that it would continue the work that was already there. A “predictive engine” could only begin to be by predicting that nourishment and reproduction would continue, since any attempt to do otherwise would necessarily result either in false predictions or in death.

This response is necessarily “hand-wave-y” in the sense that I (and presumably Clark) do not understand the precise physical implementation. But it is easy to see that it was historically necessary for things to happen this way, and it is an expression of “embodiment” in the sense that “minimize prediction error” is an abstract algorithm which does not and cannot exhaust everything which is there. The objection would be, “then there must be some other algorithm instead.” But this does not follow: no abstract algorithm will exhaust a physical object. Thus for example, animals will fall because they are heavy. Asking whether falling will satisfy some abstract algorithm is not relevant. In a similar way, animals had to be physically arranged in such a way that they would usually eat and reproduce.

I said I would make one additional point, although it may well be related to the above concern. In section 4.8 Clark notes that his account does not need to consider costs and benefits, at least directly:

But the story does not stop there. For the very same strategy here applies to the notion of desired consequences and rewards at all levels. Thus we read that ‘crucially, active inference does not invoke any “desired consequences”. It rests only on experience-dependent learning and inference: experience induces prior expectations, which guide perceptual inference and action’ (Friston, Mattout, & Kilner, 2011, p. 157). Apart from a certain efflorescence of corollary discharge, in the form of downward-flowing predictions, we here seem to confront something of a desert landscape: a world in which value functions, costs, reward signals, and perhaps even desires have been replaced by complex interacting expectations that inform perception and entrain action. But we could equally say (and I think this is the better way to express the point) that the functions of rewards and cost functions are now simply absorbed into a more complex generative model. They are implicit in our sensory (especially proprioceptive) expectations and they constrain behavior by prescribing their distinctive sensory implications.

The idea of the “desert landscape” seems to be that this account appears to do away with the idea of the good, and the idea of desire. The brain predicts what it is going to do, and those predictions cause it to do those things. This all seems purely intellectual: it seems that there is no purpose or goal or good involved.

The correct response to this, I think, is connected to what I have said elsewhere about desire and good. I noted there that we recognize our desires as desires for particular things by noticing that when we have certain feelings, we tend to do certain things. If we did not do those things, we would never conclude that those feelings are desires for doing those things. Note that someone could raise a similar objection here: if this is true, then are not desire and good mere words? We feel certain feelings, and do certain things, and that is all there is to be said. Where is good or purpose here?

The truth here is that good and being are convertible. The objection (to my definition and to Clark’s account) is not a reasonable objection at all: it would be a reasonable objection only if we expected good to be something different from being, in which case it would of course be nothing at all.

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2 thoughts on “Predictive Processing

  1. Well put. I find considering the microscopic dynamics to be useful for revealing this point about embodiment. At the low level, we have this biochemical machines pumping ions and maintaining cell walls, etc. Even if you imagine something-we-can-call-consciousness arising at a single cellular level, it arises within a system which is already doing things by mechanical necessity. So, it starts by being able to predict that.

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