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Posts Tagged ‘Bayesian brain

The Brain as a Prediction Machine

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Over the last year, I’ve been reading a lot about Karl Friston and Andy Clark’s work on the concept of perception and action being mediated by a neurological schema centered on “predictive coding”, what Friston calls “active inference”, the “free energy principle”, and Bayesian inference in general as it applies to neuro-scientific models of perception, attention, and action.  Here’s a few links (Friston here; Clark here, here, and here) to some of their work as it is worth reading for those interested in neural modeling, information theory, and learning more about meta-theories pertaining to how the brain integrates and processes information.

I find it fascinating how this newer research and these concepts relate to and help to bring together some content from several of my previous blog posts, in particular, those that mention the concept of hierarchical neurological hardware and those that mention my own definition of knowledge “as recognized causal patterns that allow us to make successful predictions.”  For those that may be interested, here’s a list of posts I’ve made over the last few years that I think contain some relevant content (in chronological order).

The ideas formulated by Friston and expanded on by Clark center around the brain being (in large part) a prediction generating machine.  This fits in line with my own conclusions about what the brain seems to be doing when it’s acquiring knowledge over time (however limited my reading is on the subject).  Here’s an image of the basic predictive processing schema:

PPschema

The basic Predictive Processing schema (adapted from Lupyan and Clark (2014))

One key element in Friston and Clark’s work (among the work of some others) is the amalgamation of perception and action.  In this framework, perception itself is simply the result of the brain’s highest level predictions of incoming sensory data.  But also important in this framework is that prediction error minimization is accomplished through embodiment itself.  That is to say, their models posit that the brain not only tries to reduce prediction errors by updating its prediction models based on the actual incoming sensory information (with only the error feeding forward to update the models, similar to data compression schema), but the concept of active inference involves the minimization of prediction error through the use of motor outputs.  This could be taken to mean that motor outputs themselves are, in a sense, caused by the brain trying to reduce prediction errors pertaining to predicted sensory input — specifically sensory input that we would say stems from our desires and goals (e.g. desire to fulfill hunger, commuting to work, opening the car door, etc.).

To give a simple example of this model in action, let’s consider an apple resting on a table in front of me.  If I see the apple in front of me and I have a desire to grab it, my brain would not only predict what that apple looks like and how it is perceived over time (and how my arm looks while reaching for it), but it would also predict what it should feel like to reach for the apple.  So if I reach for it based on the somato-sensory prediction and there is some error in that prediction, corroborated by my visual cortex observing my arm moving in some wrong direction, the brain would respond by updating its models that predict what it should feel so that my arm starts moving in the proper direction.  This prediction error minimization is then fine-tuned as I get closer to the apple and can finally grab it.

This embodiment ingrained in the predictive processing models of Friston and Clark can also be well exemplified by the so-called “Outfielder’s Problem”.  In this problem, an outfielder is trying to catch a fly ball.  Now we know that outfielders are highly skilled at doing this rather effectively.  But if we ask the outfielder to merely stand still and watch a batted ball and predict where it will land, their accuracy is generally pretty bad.  So when we think about what strategy the brain takes to accomplish this when moving the body quickly, we begin to see the relevance of active inference and embodiment in the brain’s prediction schema.  The outfielder’s brain employs a brilliant strategy called “optical acceleration cancellation” (OAC).  Here, the well-trained outfielder sees the fly ball, and moves his or her body (while watching the ball) in order to cancel out any optical acceleration observed during the ball’s flight.  If they do this, then they will end up exactly where the ball was going to land, and then they’re able to catch it successfully.

We can imagine fine-grained examples of this active inference during everyday tasks, where I may simply be looking at a picture on my living room wall, and when my brain is predicting how it will look over the span of a few seconds, my head may slightly change its tilt, or direction, or my eyes may slowly move a fraction of a degree this way or that way, however imperceptible to me.  My brain in this case is predicting what the picture on the wall will look like over time and this prediction (according to my understanding of Clark) is identical to what we actually perceive.  One key thing to note here is that the prediction models are not simply updated based on the prediction error that is fed forward through the brain’s neurological hierarchies, but it is also getting some “help” from various motor movements to correct for the errors through action, rather than simply freezing all my muscles and updating the model itself (which may in fact be far less economical for the brain to do).

Another area of research that pertains to this framework, including ways of testing its validity, is that of evolutionary psychology and biology, where one would surmise (if these models are correct) that evolution likely provided our brains with certain hard-wired predictive models and our learning processes over time use these as starting points to produce innate reflexes (such as infant suckling to give a simple example) that allow us to survive long enough to update our models with actual new acquired information.  There are many different facets to this framework and I look forward to reading more about Friston and Clark’s work over the next few years.  I have a feeling that they have hit on something big, something that will help to answer a lot of questions about embodied cognition, perception, and even consciousness itself.

I encourage you to check out the links I provided pertaining to Friston and Clark’s work, to get a taste of the brilliant ideas they’ve been working on.

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