So Scott Alexander has an interesting book review up about Surfing Uncertainty which I encourage everyone to read themselves. However, most of the post is really an exploration of the “predictive processing” model for brain function. I’ll leave a more in depth explanation of what this model is to Scott and just offer the following excerpt for those readers to lazy to click through.
Predictive processing begins by asking: how does this happen? By what process do our incomprehensible sense-data get turned into a meaningful picture of the world.
The key insight: the brain is a multi-layer prediction machine. All neural processing consists of two streams: a bottom-up stream of sense data, and a top-down stream of predictions. These streams interface at each level of processing, comparing themselves to each other and adjusting themselves as necessary.
As these two streams move through the brain side-by-side, they continually interface with each other. Each level receives the predictions from the level above it and the sense data from the level below it. Then each level uses Bayes’ Theorem to integrate these two sources of probabilistic evidence as best it can. This can end up a couple of different ways.
The upshot of these different ways is that when everything happens as predicted the higher levels remain unnotified of any change but that when there is a mismatch it draws attention from these higher layers. However, in some circumstances a strong prediction from a higher layer can cause lower layers to “rewrite the sense data to make it look as predicted.”
I admit that I’m intrigued by the idea of predictive processing, especially the suggestion that our muscle control is actually effectuated merely by `predicting’ our arm will be in a certain state and acting to minimize prediction error. However, my first reaction is to wonder how much content there is in this model.
Describing some kind of processing or control task in terms of predictions has a certain universality kind of feel to it. This is only a vague sense based on a book review but I worry that invoking the predictive processing model to describe how our brains work is much like invoking the lambda calculus model to describe how a particular computer functions. Namely, I worry that predictive processing is such a powerful model that virtually anything remotely plausible as a mechanism for processing sense data and effectuating control over our limbs could be fit into the model — meaning it offers no real insight.
I mean it was already apparent before this model came on to the scene that how we see even low level visual data is affected by high level classifications. The various figure-ground illusions make this point quite clearly. It was also already apparent that attention to one task (counting passes) could limit our ability to notice some other kind of oddity (a guy in a gorilla suit). However, its far from clear that the predictive processing model really adds anything to our understanding here.
Indeed, to even make sense of these examples we have to understand the relevant predictions to happen at a very abstract level that is highly context dependent so that by focusing on the number of basketball passes in a game it no longer counts as a sufficiently unpredicted event when a man in a gorilla suit walks past (or allows some other story about why paying one sort of attention suppresses this kind of notice). That’s fine but allowing this level of abstraction/freedom in describing the thing to be predicted makes me wonder what couldn’t be suitably described in terms of this model.
The attempt to describe our imagination, e.g., our ability to picture a generic police officer in our minds, as utilizing the mental machinery that would generate a sense-data stream as a prediction to match against reality raises more questions. Obviously, the notion of matching must be a very high level one quite removed from the actual pictorial representation if the mental image we conjure when we think of policemen is to be seen as matching the sense-data stream experienced when we encounter a policeman. Yet if the level at which we are evaluating a predictive match is so abstract why do we imagine a particular image when we think of a policeman and not merely whatever vague high level abstracta we will judge to match when we actually view a policeman. I’m sure there is a plausible theory to tell here about invoking the same lower level machinery we use to process sense-data when we imagine and leveraging that same feedback but, again, I’m left wondering what work predictive processing is really doing here.
More generally, I wonder to what extent all these predictions wouldn’t result from just assuming, as we know to be true, that the brain processes information in ‘layers’, there can be feedback between these layers and frequently the goal of our mental tasks is to predict events or control actions. Its not even obvious to me that the claimed predictions of the theory like the placebo effect couldn’t have equally well been spun the other way if the effect had been different, e.g., when your high level processes predict that you won’t feel pain it will be particularly salient when you nevertheless do feel pain so placebo pain meds should result in more people reporting pain.
But I haven’t read the book myself yet so maybe predictive processing has been suitably preciscified in the book so as to rule out many plausible ways the brain might have behaved and to clearly predict outcomes like the placebo effect. However, I wrote this post merely to raise the possibility that a paradigm like this can fail precisely because it is too good at describing phenomena. Hopefully, my worries are misplaced and someone can explain to me in the comments just what kind of plausible models of brain function this paradigm rules out.