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

In the previous post, part 4 in this series on Predictive Processing (PP), I explored some aspects of reasoning and how different forms of reasoning can be built from a foundational bedrock of Bayesian inference (click here for parts 1, 2, or 3).  This has a lot to do with language, but I also claimed that it depends on how the brain is likely generating new models, which I think is likely to involve some kind of natural selection operating on neural networks.  The hierarchical structure of the generative models for these predictions as described within a PP framework, also seems to fit well with the hierarchical structure that we find in the brain’s neural networks.  In this post, I’m going to talk about the relation between memory, imagination, and unconscious and conscious forms of reasoning.

Memory, Imagination, and Reasoning

Memory is of course crucial to the PP framework whether for constructing real-time predictions of incoming sensory information (for perception) or for long-term predictions involving high-level, increasingly abstract generative models that allow us to accomplish complex future goals (like planning to go grocery shopping, or planning for retirement).  Either case requires the brain to have stored some kind of information pertaining to predicted causal relations.  Rather than memories being some kind of exact copy of past experiences (where they’d be stored like data on a computer), research has shown that memory functions more like a reconstruction of those past experiences which are modified by current knowledge and context, and produced by some of the same faculties used in imagination.

This accounts for any false or erroneous aspects of our memories, where the recalled memory can differ substantially from how the original event was experienced.  It also accounts for why our memories become increasingly altered as more time passes.  Over time, we learn new things, continuing to change many of our predictive models about the world, and thus have a more involved reconstructive process the older the memories are.  And the context we find ourselves in when trying to recall certain memories, further affect this reconstruction process, adapting our memories in some sense to better match what we find most salient and relevant in the present moment.

Conscious vs. Unconscious Processing & Intuitive Reasoning (Intuition)

Another attribute of memory is that it is primarily unconscious, where we seem to have this pool of information that is kept out of consciousness until parts of it are needed (during memory recall or or other conscious thought processes).  In fact, within the PP framework we can think of most of our generative models (predictions), especially those operating in the lower levels of the hierarchy, as being out of our conscious awareness as well.  However, since our memories are composed of (or reconstructed with) many higher level predictions, and since only a limited number of them can enter our conscious awareness at any moment, this implies that most of the higher-level predictions are also being maintained or processed unconsciously as well.

It’s worth noting however that when we were first forming these memories, a lot of the information was in our consciousness (the higher-level, more abstract predictions in particular).  Within PP, consciousness plays a special role since our attention modifies what is called the precision weight (or synaptic gain) on any prediction error that flows upward through the predictive hierarchy.  This means that the prediction errors produced from the incoming sensory information or at even higher levels of processing are able to have a greater impact on modifying and updating the predictive models.  This makes sense from an evolutionary perspective, where we can ration our cognitive resources in a more adaptable way, by allowing things that catch our attention (which may be more important to our survival prospects) to have the greatest effect on how we understand the world around us and how we need to act at any given moment.

After repeatedly encountering certain predicted causal relations in a conscious fashion, the more likely those predictions can become automated or unconsciously processed.  And if this has happened with certain rules of inference that govern how we manipulate and process many of our predictive models, it seems reasonable to suspect that this would contribute to what we call our intuitive reasoning (or intuition).  After all, intuition seems to give people the sense of knowing something without knowing how it was acquired and without any present conscious process of reasoning.

This is similar to muscle memory or procedural memory (like learning how to ride a bike) which is consciously processed at first (thus involving many parts of the cerebral cortex), but after enough repetition it becomes a faster and more automated process that is accomplished more economically and efficiently by the basal ganglia and cerebellum, parts of the brain that are believed to handle a great deal of unconscious processing like that needed for procedural memory.  This would mean that the predictions associated with these kinds of causal relations begin to function out of our consciousness, even if the same predictive strategy is still in place.

As mentioned above, one difference between this unconscious intuition and other forms of reasoning that operate within the purview of consciousness is that our intuitions are less likely to be updated or changed based on new experiential evidence since our conscious attention isn’t involved in the updating process. This means that the precision weight of upward flowing prediction errors that encounter downward flowing predictions that are operating unconsciously will have little impact in updating those predictions.  Furthermore, the fact that the most automated predictions are often those that we’ve been using for most of our lives, means that they are also likely to have extremely high Bayesian priors, further isolating them from modification.

Some of these priors may become what are called hyperpriors or priors over priors (many of these believed to be established early in life) where there may be nothing that can overcome them, because they describe an extremely abstract feature of the world.  An example of a possible hyperprior could be one that demands that the brain settle on one generative model even when it’s comparable to several others under consideration.  One could call this a “tie breaker” hyperprior, where if the brain didn’t have this kind of predictive mechanism in place, it may never be able to settle on a model, causing it to see the world (or some aspect of it) as a superposition of equiprobable states rather than simply one determinate state.  We could see the potential problem in an organism’s survival prospects if it didn’t have this kind of hyperprior in place.  Whether or not a hyperprior like this is a form of innate specificity, or acquired in early learning is debatable.

An obvious trade-off with intuition (or any kind of innate biases) is that it provides us with fast, automated predictions that are robust and likely to be reliable much of the time, but at the expense of not being able to adequately handle more novel or complex situations, thereby leading to fallacious inferences.  Our cognitive biases are also likely related to this kind of unconscious reasoning whereby evolution has naturally selected cognitive strategies that work well for the kind of environment we evolved in (African savanna, jungle, etc.) even at the expense of our not being able to adapt as well culturally or in very artificial situations.

Imagination vs. Perception

One large benefit of storing so much perceptual information in our memories (predictive models with different spatio-temporal scales) is our ability to re-create it offline (so to speak).  This is where imagination comes in, where we are able to effectively simulate perceptions without requiring a stream of incoming sensory data that matches it.  Notice however that this is still a form of perception, because we can still see, hear, feel, taste and smell predicted causal relations that have been inferred from past sensory experiences.

The crucial difference, within a PP framework, is the role of precision weighting on the prediction error, just as we saw above in terms of trying to update intuitions.  If precision weighting is set or adjusted to be relatively low with respect to a particular set of predictive models, then prediction error will have little if any impact on the model.  During imagination, we effectively decouple the bottom-up prediction error from the top-down predictions associated with our sensory cortex (by reducing the precision weighting of the prediction error), thus allowing us to intentionally perceive things that aren’t actually in the external world.  We need not decouple the error from the predictions entirely, as we may want our imagination to somehow correlate with what we’re actually perceiving in the external world.  For example, maybe I want to watch a car driving down the street and simply imagine that it is a different color, while still seeing the rest of the scene as I normally would.  In general though, it is this decoupling “knob” that we can turn (precision weighting) that underlies our ability to produce and discriminate between normal perception and our imagination.

So what happens when we lose the ability to control our perception in a normal way (whether consciously or not)?  Well, this usually results in our having some kind of hallucination.  Since perception is often referred to as a form of controlled hallucination (within PP), we could better describe a pathological hallucination (such as that arising from certain psychedelic drugs or a condition like Schizophrenia) as a form of uncontrolled hallucination.  In some cases, even with a perfectly normal/healthy brain, when the prediction error simply can’t be minimized enough, or the brain is continuously switching between models, based on what we’re looking at, we experience perceptual illusions.

Whether it’s illusions, hallucinations, or any other kind of perceptual pathology (like not being able to recognize faces), PP offers a good explanation for why these kinds of experiences can happen to us.  It’s either because the models are poor (their causal structure or priors) or something isn’t being controlled properly, like the delicate balance between precision weighting and prediction error, any of which that could result from an imbalance in neurotransmitters or some kind of brain damage.

Imagination & Conscious Reasoning

While most people would tend to define imagination as that which pertains to visual imagery, I prefer to classify all conscious experiences that are not directly resulting from online perception as imagination.  In other words, any part of our conscious experience that isn’t stemming from an immediate inference of incoming sensory information is what I consider to be imagination.  This is because any kind of conscious thinking is going to involve an experience that could in theory be re-created by an artificial stream of incoming sensory information (along with our top-down generative models that put that information into a particular context of understanding).  As long as the incoming sensory information was a particular way (any way that we can imagine!), even if it could never be that way in the actual external world we live in, it seems to me that it should be able to reproduce any conscious process given the right top-down predictive model.  Another way of saying this is that imagination is simply another word to describe any kind of offline conscious mental simulation.

This also means that I’d classify any and all kinds of conscious reasoning processes as yet another form of imagination.  Just as is the case with more standard conceptions of imagination (within PP at least), we are simply taking particular predictive models, manipulating them in certain ways in order to simulate some result with this process decoupled (at least in part) from actual incoming sensory information.  We may for example, apply a rule of inference that we’ve picked up on and manipulate several predictive models of causal relations using that rule.  As mentioned in the previous post and in the post from part 2 of this series, language is also likely to play a special role here where we’ll likely be using it to help guide this conceptual manipulation process by organizing and further representing the causal relations in a linguistic form, and then determining the resulting inference (which will more than likely be in a linguistic form as well).  In doing so, we are able to take highly abstract properties of causal relations and apply rules to them to extract new information.

If I imagine a purple elephant trumpeting and flying in the air over my house, even though I’ve never experienced such a thing, it seems clear that I’m manipulating several different types of predicted causal relations at varying levels of abstraction and experiencing the result of that manipulation.  This involves inferred causal relations like those pertaining to visual aspects of elephants, the color purple, flying objects, motion in general, houses, the air, and inferred causal relations pertaining to auditory aspects like trumpeting sounds and so forth.

Specific instances of these kinds of experienced causal relations have led to my inferring them as an abstract probabilistically-defined property (e.g. elephantness, purpleness, flyingness, etc.) that can be reused and modified to some degree to produce an infinite number of possible recreated perceptual scenes.  These may not be physically possible perceptual scenes (since elephants don’t have wings to fly, for example) but regardless I’m able to add or subtract, mix and match, and ultimately manipulate properties in countless ways, only limited really by what is logically possible (so I can’t possibly imagine what a square circle would look like).

What if I’m performing a mathematical calculation, like “adding 9 + 9”, or some other similar problem?  This appears (upon first glance at least) to be very qualitatively different than simply imagining things that we tend to perceive in the world like elephants, books, music, and other things, even if they are imagined in some phantasmagorical way.  As crazy as those imagined things may be, they still contain things like shapes, colors, sounds, etc., and a mathematical calculation seems to lack this.  I think the key thing to realize here is the fundamental process of imagination as being able to add or subtract and manipulate abstract properties in any way that is logically possible (given our current set of predictive models).  This means that we can imagine properties or abstractions that lack all the richness of a typical visual/auditory perceptual scene.

In the case of a mathematical calculation, I would be manipulating previously acquired predicted causal relations that pertain to quantity and changes in quantity.  Once I was old enough to infer that separate objects existed in the world, then I could infer an abstraction of how many objects there were in some space at some particular time.  Eventually, I could abstract the property of how many objects without applying it to any particular object at all.  Using language to associate a linguistic symbol for each and every specific quantity would lay the groundwork for a system of “numbers” (where numbers are just quantities pertaining to no particular object at all).  Once this was done, then my brain could use the abstraction of quantity and manipulate it by following certain inferred rules of how quantities can change by adding to or subtracting from them.  After some practice and experience I would now be in a reasonable position to consciously think about “adding 9 + 9”, and either do it by following a manual iterative rule of addition that I’ve learned to do with real or imagined visual objects (like adding up some number of apples or dots/points in a row or grid), or I can simply use a memorized addition table and search/recall the sum I’m interested in (9 + 9 = 18).

Whether we consider imagining a purple elephant, mentally adding up numbers, thinking about what I’m going to say to my wife when I see her next, or trying to explicitly apply logical rules to some set of concepts, all of these forms of conscious thought or reasoning are all simply different sets of predictive models that I’m simply manipulating in mental simulations until I arrive at a perception that’s understood in the desired context and that has minimal prediction error.

Putting it all together

In summary, I think we can gain a lot of insight by looking at all the different aspects of brain function through a PP framework.  Imagination, perception, memory, intuition, and conscious reasoning fit together very well when viewed as different aspects of hierarchical predictive models that are manipulated and altered in ways that give us a much more firm grip on the world we live in and its inferred causal structure.  Not only that, but this kind of cognitive architecture also provides us with an enormous potential for creativity and intelligence.  In the next post in this series, I’m going to talk about consciousness, specifically theories of consciousness and how they may be viewed through a PP framework.

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The Brain as a Prediction Machine

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.

Conscious Realism & The Interface Theory of Perception

A few months ago I was reading an interesting article in The Atlantic about Donald Hoffman’s Interface Theory of Perception.  As a person highly interested in consciousness studies, cognitive science, and the mind-body problem, I found the basic concepts of his theory quite fascinating.  What was most interesting to me was the counter-intuitive connection between evolution and perception that Hoffman has proposed.  Now it is certainly reasonable and intuitive to assume that evolutionary natural selection would favor perceptions that are closer to “the truth” or closer to the objective reality that exists independent of our minds, simply because of the idea that perceptions that are more accurate will be more likely to lead to survival than perceptions that are not accurate.  As an example, if I were to perceive lions as inert objects like trees, I would be more likely to be naturally selected against and eaten by a lion when compared to one who perceives lions as a mobile predator that could kill them.

While this is intuitive and reasonable to some degree, what Hoffman actually shows, using evolutionary game theory, is that with respect to organisms with comparable complexity, those with perceptions that are closer to reality are never going to be selected for nearly as much as those with perceptions that are tuned to fitness instead.  More so, truth in this case will be driven to extinction when it is up against perceptual models that are tuned to fitness.  That is to say, evolution will select for organisms that perceive the world in a way that is less accurate (in terms of the underlying reality) as long as the perception is tuned for survival benefits.  The bottom line is that given some specific level of complexity, it is more costly to process more information (costing more time and resources), and so if a “heuristic” method for perception can evolve instead, one that “hides” all the complex information underlying reality and instead provides us with a species-specific guide to adaptive behavior, that will always be the preferred choice.

To see this point more clearly, let’s consider an example.  Let’s imagine there’s an animal that regularly eats some kind of insect, such as a beetle, but it needs to eat a particular sized beetle or else it has a relatively high probability of eating the wrong kind of beetle (and we can assume that the “wrong” kind of beetle would be deadly to eat).  Now let’s imagine two possible types of evolved perception: it could have really accurate perceptions about the various sizes of beetles that it encounters so it can distinguish many different sizes from one another (and then choose the proper size range to eat), or it could evolve less accurate perceptions such that all beetles that are either too small or too large appear as indistinguishable from one another (maybe all the wrong-sized beetles whether too large or too small look like indistinguishable red-colored blobs) and perhaps all the beetles that are in the ideal size range for eating appear as green-colored blobs (that are again, indistinguishable from one another).  So the only discrimination in this latter case of perception is between red and green colored blobs.

Both types of perception would solve the problem of which beetles to eat or not eat, but the latter type (even if much less accurate) would bestow a fitness advantage over the former type, by allowing the animal to process much less information about the environment by not focusing on relatively useless information (like specific beetle size).  In this case, with beetle size as the only variable under consideration for survival, evolution would select for the organism that knows less total information about beetle size, as long as it knows what is most important about distinguishing the edible beetles from the poisonous beetles.  Now we can imagine that in some cases, the fitness function could align with the true structure of reality, but this is not what we ever expect to see generically in the world.  At best we may see some kind of overlap between the two but if there doesn’t have to be any then truth will go extinct.

Perception is Analogous to a Desktop Computer Interface

Hoffman analogizes this concept of a “perception interface” with the desktop interface of a personal computer.  When we see icons of folders on the desktop and drag one of those icons to the trash bin, we shouldn’t take that interface literally, because there isn’t literally a folder being moved to a literal trash bin but rather it is simply an interface that hides most if not all of what is really going on in the background — all those various diodes, resistors and transistors that are manipulated in order to modify stored information that is represented in binary code.

The desktop interface ultimately provides us with an easy and intuitive way of accomplishing these various information processing tasks because trying to do so in the most “truthful” way — by literally manually manipulating every diode, resistor, and transistor to accomplish the same task — would be far more cumbersome and less effective than using the interface.  Therefore the interface, by hiding this truth from us, allows us to “navigate” through that computational world with more fitness.  In this case, having more fitness simply means being able to accomplish information processing goals more easily, with less resources, etc.

Hoffman goes on to say that even though we shouldn’t take the desktop interface literally, obviously we should still take it seriously, because moving that folder to the trash bin can have direct implications on our lives, by potentially destroying months worth of valuable work on a manuscript that is contained in that folder.  Likewise we should take our perceptions seriously, even if we don’t take them literally.  We know that stepping in front of a moving train will likely end our conscious experience even if it is for causal reasons that we have no epistemic access to via our perception, given the species-specific “desktop interface” that evolution has endowed us with.

Relevance to the Mind-body Problem

The crucial point with this analogy is the fact that if our knowledge was confined to the desktop interface of the computer, we’d never be able to ascertain the underlying reality of the “computer”, because all that information that we don’t need to know about that underlying reality is hidden from us.  The same would apply to our perception, where it would be epistemically isolated from the underlying objective reality that exists.  I want to add to this point that even though it appears that we have found the underlying guts of our consciousness, i.e., the findings in neuroscience, it would be mistaken to think that this approach will conclusively answer the mind-body problem because the interface that we’ve used to discover our brains’ underlying neurobiology is still the “desktop” interface.

So while we may think we’ve found the underlying guts of “the computer”, this is far from certain, given the possibility of and support for this theory.  This may end up being the reason why many philosophers claim there is a “hard problem” of consciousness and one that can’t be solved.  It could be that we simply are stuck in the desktop interface and there’s no way to find out about the underlying reality that gives rise to that interface.  All we can do is maximize our knowledge of the interface itself and that would be our epistemic boundary.

Predictions of the Theory

Now if this was just a fancy idea put forward by Hoffman, that would be interesting in its own right, but the fact that it is supported by evolutionary game theory and genetic algorithm simulations shows that the theory is more than plausible.  Even better, the theory is actually a scientific theory (and not just a hypothesis), because it has made falsifiable predictions as well.  It predicts that “each species has its own interface (with some similarities between phylogenetically related species), almost surely no interface performs reconstructions (read the second link for more details on this), each interface is tailored to guide adaptive behavior in the relevant niche, much of the competition between and within species exploits strengths and limitations of interfaces, and such competition can lead to arms races between interfaces that critically influence their adaptive evolution.”  The theory predicts that interfaces are essential to understanding evolution and the competition between organisms, whereas the reconstruction theory makes such understanding impossible.  Thus, evidence of interfaces should be widespread throughout nature.

In his paper, he mentions the Jewel beetle as a case in point.  This beetle has a perceptual category, desirable females, which works well in its niche, and it uses it to choose larger females because they are the best mates.  According to the reconstructionist thesis, the male’s perception of desirable females should incorporate a statistical estimate of the true sizes of the most fertile females, but it doesn’t do this.  Instead, it has a category based on “bigger is better” and although this bestows a high fitness behavior for the male beetle in its evolutionary niche, if it comes into contact with a “stubbie” beer bottle, it falls into an infinite loop by being drawn to this supernormal stimuli since it is smooth, brown, and extremely large.  We can see that the “bigger is better” perceptual category relies on less information about the true nature of reality and instead chooses an “informational shortcut”.  The evidence of supernormal stimuli which have been found with many species further supports the theory and is evidence against the reconstructionist claim that perceptual categories estimate the statistical structure of the world.

More on Conscious Realism (Consciousness is all there is?)

This last link provided here shows the mathematical formalism of Hoffman’s conscious realist theory as proved by Chetan Prakash.  It contains a thorough explanation of the conscious realist theory (which goes above and beyond the interface theory of perception) and it also provides answers to common objections put forward by other scientists and philosophers on this theory.