The Open Mind

Cogito Ergo Sum

Predictive Processing: Unlocking the Mysteries of Mind & Body (Part I)

leave a comment »

I’ve been away from writing for a while because I’ve had some health problems relating to my neck.  A few weeks ago I had double-cervical-disc replacement surgery and so I’ve been unable to write and respond to comments and so forth for a little while.  I’m in the second week following my surgery now and have finally been able to get back to writing, which feels very good given that I’m unable to lift or resume martial arts for the time being.  Anyway, I want to resume my course of writing beginning with a post-series that pertains to Predictive Processing (PP) and the Bayesian brain.  I’ve written one post on this topic a little over a year ago (which can be found here) as I’ve become extremely interested in this topic for the last several years now.

The Predictive Processing (PP) theory of perception shows a lot of promise in terms of finding an overarching schema that can account for everything that the brain seems to do.  While its technical application is to account for the acts of perception and active inference in particular, I think it can be used more broadly to account for other descriptions of our mental life such as beliefs (and knowledge), desires, emotions, language, reasoning, cognitive biases, and even consciousness itself.  I want to explore some of these relationships as viewed through a PP lens more because I think it is the key framework needed to reconcile all of these aspects into one coherent picture, especially within the evolutionary context of an organism driven to survive.  Let’s begin this post-series by first looking at how PP relates to perception (including imagination), beliefs, emotions, and desires (and by extension, the actions resulting from particular desires).

Within a PP framework, beliefs can be best described as simply the set of particular predictions that the brain employs which encompass perception, desires, action, emotion, etc., and which are ultimately mediated and updated in order to reduce prediction errors based on incoming sensory evidence (and which approximates a Bayesian form of inference).  Perception then, which is constituted by a subset of all our beliefs (with many of them being implicit or unconscious beliefs), is more or less a form of controlled hallucination in the sense that what we consciously perceive is not the actual sensory evidence itself (not even after processing it), but rather our brain’s “best guess” of what the causes for the incoming sensory evidence are.

Desires can be best described as another subset of one’s beliefs, and a set of beliefs which has the special characteristic of being able to drive action or physical behavior in some way (whether driving internal bodily states, or external ones that move the body in various ways).  Finally, emotions can be thought of as predictions pertaining to the causes of internal bodily states and which may be driven or changed by changes in other beliefs (including changes in desires or perceptions).

When we believe something to be true or false, we are basically just modeling some kind of causal relationship (or its negation) which is able to manifest itself into a number of highly-weighted predicted perceptions and actions.  When we believe something to be likely true or likely false, the same principle applies but with a lower weight or precision on the predictions that directly corresponds to the degree of belief or disbelief (and so new sensory evidence will more easily sway such a belief).  And just like our perceptions, which are mediated by a number of low and high-level predictions pertaining to incoming sensory data, any prediction error that the brain encounters results in either updating the perceptual predictions to new ones that better reduce the prediction error and/or performing some physical action that reduces the prediction error (e.g. rotating your head, moving your eyes, reaching for an object, excreting hormones in your body, etc.).

In all these cases, we can describe the brain as having some set of Bayesian prior probabilities pertaining to the causes of incoming sensory data, and these priors changing over time in response to prediction errors arising from new incoming sensory evidence that fails to be “explained away” by the predictive models currently employed.  Strong beliefs are associated with high prior probabilities (highly-weighted predictions) and therefore need much more counterfactual sensory evidence to be overcome or modified than for weak beliefs which have relatively low priors (low-weighted predictions).

To illustrate some of these concepts, let’s consider a belief like “apples are a tasty food”.  This belief can be broken down into a number of lower level, highly-weighted predictions such as the prediction that eating a piece of what we call an “apple” will most likely result in qualia that accompany the perception of a particular satisfying taste, the lower level prediction that doing so will also cause my perception of hunger to change, and the higher level prediction that it will “give me energy” (with these latter two predictions stemming from the more basic category of “food” contained in the belief).  Another prediction or set of predictions is that these expectations will apply to not just one apple but a number of apples (different instances of one type of apple, or different types of apples altogether), and a host of other predictions.

These predictions may even result (in combination with other perceptions or beliefs) in an actual desire to eat an apple which, under a PP lens could be described as the highly weighted prediction of what it would feel like to find an apple, to reach for an apple, to grab it, to bite off a piece of it, to chew it, and to swallow it.  If I merely imagine doing such things, then the resulting predictions will necessarily carry such a small weight that they won’t be able to influence any actual motor actions (even if these imagined perceptions are able to influence other predictions that may eventually lead to some plan of action).  Imagined perceptions will also not carry enough weight (when my brain is functioning normally at least) to trick me into thinking that they are actual perceptions (by “actual”, I simply mean perceptions that correspond to incoming sensory data).  This low-weighting attribute of imagined perceptual predictions thus provides a viable way for us to have an imagination and to distinguish it from perceptions corresponding to incoming sensory data, and to distinguish it from predictions that directly cause bodily action.  On the other hand, predictions that are weighted highly enough (among other factors) will be uniquely capable of affecting our perception of the real world and/or instantiating action.

This latter case of desire and action shows how the PP model takes the organism to be an embodied prediction machine that is directly influencing and being influenced by the world that its body interacts with, with the ultimate goal of reducing any prediction error encountered (which can be thought of as maximizing Bayesian evidence).  In this particular example, the highly-weighted prediction of eating an apple is simply another way of describing a desire to eat an apple, which produces some degree of prediction error until the proper actions have taken place in order to reduce said error.  The only two ways of reducing this prediction error are to change the desire (or eliminate it) to one that no longer involves eating an apple, and/or to perform bodily actions that result in actually eating an apple.

Perhaps if I realize that I don’t have any apples in my house, but I realize that I do have bananas, then my desire will change to one that predicts my eating a banana instead.  Another way of saying this is that my higher-weighted prediction of satisfying hunger supersedes my prediction of eating an apple specifically, thus one desire is able to supersede another.  However, if the prediction weight associated with my desire to eat an apple is high enough, it may mean that my predictions will motivate me enough to avoid eating the banana, and instead to predict what it is like to walk out of my house, go to the store, and actually get an apple (and therefore, to actually do so).  Furthermore, it may motivate me to predict actions that lead me to earn the money such that I can purchase the apple (if I don’t already have the money to do so).  To do this, I would be employing a number of predictions having to do with performing actions that lead to me obtaining money, using money to purchase goods, etc.

This is but a taste of what PP has to offer, and how we can look at basic concepts within folk psychology, cognitive science, and theories of mind in a new light.  Associated with all of these beliefs, desires, emotions, and actions (which again, are simply different kinds of predictions under this framework), is a number of elements pertaining to ontology (i.e. what kinds of things we think exist in the world) and pertaining to language as well, and I’d like to explore this relationship in my next post.  This link can be found here.


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: