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

In the first post in this series I re-introduced the basic concepts underling the Predictive Processing (PP) theory of perception, in particular how the brain uses a form of active Bayesian inference to form predictive models that effectively account for perception as well as action.  I also mentioned how the folk psychological concepts of beliefs, desires and emotions can fit within the PP framework.  In the second post in this series I expanded the domain of PP a bit to show how it relates to language and ontology, and how we perceive the world to be structured as discrete objects, objects with “fuzzy boundaries”, and other more complex concepts all stemming from particular predicted causal relations.

It’s important to note that this PP framework differs from classical computational frameworks for brain function in a very big way, because the processing and learning steps are no longer considered separate stages within the PP framework, but rather work at the same time (learning is effectively occurring all the time).  Furthermore, classical computational frameworks for brain function treat the brain more or less like a computer which I think is very misguided, and the PP framework offers a much better alternative that is far more creative, economical, efficient, parsimonious and pragmatic.  The PP framework holds the brain to be more of a probabilistic system rather than a deterministic computational system as held in classical computationalist views.  Furthermore, the PP framework puts a far greater emphasis on the brain using feed-back loops whereas traditional computational approaches tend to suggest that the brain is primarily a feed-forward information processing system.

Rather than treating the brain like a generic information processing system that passively waits for new incoming data from the outside world, it stays one step ahead of the game, by having formed predictions about the incoming sensory data through a very active and creative learning process built-up by past experiences through a form of active Bayesian inference.  Rather than utilizing some kind of serial processing scheme, this involves primarily parallel neuronal processing schemes resulting in predictions that have a deeply embedded hierarchical structure and relationship with one another.  In this post, I’d like to explore how traditional and scientific notions of knowledge can fit within a PP framework as well.

Knowledge as a Subset of Predicted Causal Relations

It has already been mentioned that, within a PP lens, our ontology can be seen as basically composed of different sets of highly differentiated, probabilistic causal relations.  This allows us to discriminate one object or concept from another, as they are each “composed of” or understood by the brain as causes that can be “explained away” by different sets of predictions.  Beliefs are just another word for predictions, with higher-level beliefs (including those that pertain to very specific contexts) consisting of a conjunction of a number of different lower level predictions.  When we come to “know” something then, it really is just a matter of inferring some causal relation or set of causal relations about a particular aspect of our experience.

Knowledge is often described by philosophers using various forms of the Platonic definition: Knowledge = Justified True Belief.  I’ve talked about this to some degree in previous posts (here, here) and I came up with what I think is a much better working definition for knowledge which could be defined as such:

Knowledge consists of recognized patterns of causality that are stored into memory for later recall and use, that positively and consistently correlate with reality, and for which that correlation has been validated by empirical evidence (i.e. successful predictions made and/or goals accomplished through the use of said recalled patterns).

We should see right off the bat how this particular view of knowledge fits right in to the PP framework, where knowledge consists of predictions of causal relations, and the brain is in the very business of making predictions of causal relations.  Notice my little caveat however, where these causal relations should positively and consistently correlate with “reality” and be supported by empirical evidence.  This is because I wanted to distinguish all predicted causal relations (including those that stem from hallucinations or unreliable inferences) from the subset of predicted causal relations that we have a relatively high degree of certainty in.

In other words, if we are in the game of trying to establish a more reliable epistemology, we want to distinguish between all beliefs and the subset of beliefs that have a very high likelihood of being true.  This distinction however is only useful for organizing our thoughts and claims based on our level of confidence in their truth status.  And for all beliefs, regardless of the level of certainty, the “empirical evidence” requirement in my definition given above is still going to be met in some sense because the incoming sensory data is the empirical evidence (the causes) that support the brain’s predictions (of those causes).

Objective or Scientific Knowledge

Within a domain like science however, where we want to increase the reliability or objectivity of our predictions pertaining to any number of inferred causal relations in the world, we need to take this same internalized strategy of modifying the confidence levels of our predictions by comparing them to the predictions of others (third-party verification) and by testing these predictions with externally accessible instrumentation using some set of conventions or standards (including the scientific method).

Knowledge then, is largely dependent on or related to our confidence levels in our various beliefs.  And our confidence level in the truth status of a belief is just another way of saying how probable such a belief is to explain away a certain set of causal relations, which is equivalent to our brain’s Bayesian prior probabilities (our “priors”) that characterize any particular set of predictions.  This means that I would need a lot of strong sensory evidence to overcome a belief with high Bayesian priors, not least because it is likely to be associated with a large number of other beliefs.  This association between different predictions seems to me to be a crucial component not only for knowledge generally (or ontology as mentioned in the last post), but also for our reasoning processes.  In the next post of this series, I’m going to expand a bit on reasoning and how I view it through a PP framework.

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

In the first post of this series I introduced some of the basic concepts involved in the Predictive Processing (PP) theory of perception and action.  I briefly tied together the notions of belief, desire, emotion, and action from within a PP lens.  In this post, I’d like to discuss the relationship between language and ontology through the same framework.  I’ll also start talking about PP in an evolutionary context as well, though I’ll have more to say about that in future posts in this series.

Active (Bayesian) Inference as a Source for Ontology

One of the main themes within PP is the idea of active (Bayesian) inference whereby we physically interact with the world, sampling it and modifying it in order to reduce our level of uncertainty in our predictions about the causes of the brain’s inputs.  Within an evolutionary context, we can see why this form of embodied cognition is an ideal schema for an information processing system to employ in order to maximize chances of survival in our highly interactive world.

In order to reduce the amount of sensory information that has to be processed at any given time, it is far more economical for the brain to only worry about the prediction error that flows upward through the neural system, rather than processing all incoming sensory data from scratch.  If the brain is employing a set of predictions that can “explain away” most of the incoming sensory data, then the downward flow of predictions can encounter an upward flow of sensory information (effectively cancelling each other out) and the only thing that remains to propagate upward through the system and do any “cognitive work” (i.e. the only thing that needs to be processed) on the predictive models flowing downward is the remaining prediction error (prediction error = predictions of sensory input minus the actual sensory input).  This is similar to data compression strategies for video files (for example) that only worry about the information that changes over time (pixels that change brightness/color) and then simply compress the information that remains constant (pixels that do not change from frame-to-frame).

The ultimate goal for this strategy within an evolutionary context is to allow the organism to understand its environment in the most salient ways for the pragmatic purposes of accomplishing goals relating to survival.  But once humans began to develop culture and evolve culturally, the predictive strategy gained a new kind of evolutionary breathing space, being able to predict increasingly complex causal relations and developing technology along the way.  All of these inferred causal relations appear to me to be the very source of our ontology, as each hierarchically structured prediction and its ability to become associated with others provides an ideal platform for differentiating between any number of spatio-temporal conceptions and their categorical or logical organization.

An active Bayesian inference system is also ideal to explain our intellectual thirst, human curiosity, and interest in novel experiences (to some degree), because we learn more about the world (and ourselves) by interacting with it in new ways.  In doing so, we are provided with a constant means of fueling and altering our ontology.

Language & Ontology

Language is an important component as well and it fits well within a PP framework as it serves to further link perception and action together in a very important way, allowing us to make new kinds of predictions about the world that wouldn’t have been possible without it.   A tool like language makes a lot of sense from an evolutionary perspective as well since better predictions about the world result in a higher chance of survival.

When we use language by speaking or writing it, we are performing an action which is instantiated by the desire to do so (see previous post about “desire” within a PP framework).  When we interpret language by listening to it or by reading, we are performing a perceptual task which is again simply another set of predictions (in this case, pertaining to the specific causes leading to our sensory inputs).  If we were simply sending and receiving non-lingual nonsense, then the same basic predictive principles underlying perception and action would still apply, but something new emerges when we send and receive actual language (which contains information).  With language, we begin to associate certain sounds and visual information with some kind of meaning or meaningful information.  Once we can do this, we can effectively share our thoughts with one another, or at least many aspects of our thoughts with one another.  This provides for an enormous evolutionary advantage as now we can communicate almost anything we want to one another, store it in external forms of memory (books, computers, etc.), and further analyze or manipulate the information for various purposes (accounting, inventory, science, mathematics, etc.).

By being able to predict certain causal outcomes through the use of language, we are effectively using the lower level predictions associated with perceiving and emitting language to satisfy higher level predictions related to more complex goals including those that extend far into the future.  Since the information that is sent and received amounts to testing or modifying our predictions of the world, we are effectively using language to share and modulate one brain’s set of predictions with that of another brain.  One important aspect of this process is that this information is inherently probabilistic which is why language often trips people up with ambiguities, nuances, multiple meanings behind words and other attributes of language that often lead to misunderstanding.  Wittgenstein is one of the more prominent philosophers who caught onto this property of language and its consequence on philosophical problems and how we see the world structured.  I think a lot of the problems Wittgenstein elaborated on with respect to language can be better accounted for by looking at language as dealing with probabilistic ontological/causal relations that serve some pragmatic purpose, with the meaning of any word or phrase as being best described by its use rather than some clear-cut definition.

This probabilistic attribute of language in terms of the meanings of words having fuzzy boundaries also tracks very well with the ontology that a brain currently has access to.  Our ontology, or what kinds of things we think exist in the world, are often categorized in various ways with some of the more concrete entities given names such as: “animals”, “plants”, “rocks”, “cats”, “cups”, “cars”, “cities”, etc.  But if I morph a wooden chair (say, by chipping away at parts of it with a chisel), eventually it will no longer be recognizable as a chair, and it may begin to look more like a table than a chair or like nothing other than an oddly shaped chunk of wood.  During this process, it may be difficult to point to the exact moment that it stopped being a chair and instead became a table or something else, and this would make sense if what we know to be a chair or table or what-have-you is nothing more than a probabilistic high-level prediction about certain causal relations.  If my brain perceives an object that produces too high of a prediction error based on the predictive model of what a “chair” is, then it will try another model (such as the predictive model pertaining to a “table”), potentially leading to models that are less and less specific until it is satisfied with recognizing the object as merely a “chunk of wood”.

From a PP lens, we can consider lower level predictions pertaining to more basic causes of sensory input (bright/dark regions, lines, colors, curves, edges, etc.) to form some basic ontological building blocks and when they are assembled into higher level predictions, the amount of integrated information increases.  This information integration process leads to condensed probabilities about increasingly complex causal relations, and this ends up reducing the dimensionality of the cause-effect space of the predicted phenomenon (where a set of separate cause-effect repertoires are combined into a smaller number of them).

You can see the advantage here by considering what the brain might do if it’s looking at a black cat sitting on a brown chair.  What if the brain were to look at this scene as merely a set of pixels on the retina that change over time, where there’s no expectations of any subset of pixels to change in ways that differ from any other subset?  This wouldn’t be very useful in predicting how the visual scene will change over time.  What if instead, the brain differentiates one subset of pixels (that correspond to what we call a cat) from all the rest of the pixels, and it does this in part by predicting proximity relations between neighboring pixels in the subset (so if some black pixels move from the right to the left visual field, then some number of neighboring black pixels are predicted to move with it)?

This latter method treats the subset of black-colored pixels as a separate object (as opposed to treating the entire visual scene as a single object), and doing this kind of differentiation in more and more complex ways leads to a well-defined object or concept, or a large number of them.  Associating sounds like “meow” with this subset of black-colored pixels, is just one example of yet another set of properties or predictions that further defines this perceived object as distinct from the rest of the perceptual scene.  Associating this object with a visual or auditory label such as “cat” finally links this ontological object with language.  As long as we agree on what is generally meant by the word “cat” (which we determine through its use), then we can share and modify the predictive models associated with such an object or concept, as we can do with any other successful instance of linguistic communication.

Language, Context, and Linguistic Relativism

However, it should be noted that as we get to more complex causal relations (more complex concepts/objects), we can no longer give these concepts a simple one word label and expect to communicate information about them nearly as easily as we could for the concept of a “cat”.  Think about concepts like “love” or “patriotism” or “transcendence” and realize how there’s many different ways that we use those terms and how they can mean all sorts of different things and so our meaning behind those words will be heavily conveyed to others by the context that they are used in.  And context in a PP framework could be described as simply the (expected) conjunction of multiple predictive models (multiple sets of causal relations) such as the conjunction of the predictive models pertaining to the concepts of food, pizza, and a desirable taste, and the word “love” which would imply a particular use of the word “love” as used in the phrase “I love pizza”.  This use of the word “love” is different than one which involves the conjunction of predictive models pertaining to the concepts of intimacy, sex, infatuation, and care, implied in a phrase like “I love my wife”.  In any case, conjunctions of predictive models can get complicated and this carries over to our stretching our language to its very limits.

Since we are immersed in language and since it is integral in our day-to-day lives, we also end up being conditioned to think linguistically in a number of ways.  For example, we often think with an interior monologue (e.g. “I am hungry and I want pizza for lunch”) even when we don’t plan on communicating this information to anyone else, so it’s not as if we’re simply rehearsing what we need to say before we say it.  I tend to think however that this linguistic thinking (thinking in our native language) is more or less a result of the fact that the causal relations that we think about have become so strongly associated with certain linguistic labels and propositions, that we sort of automatically think of the causal relations alongside the labels that we “hear” in our head.  This seems to be true even if the causal relations could be thought of without any linguistic labels, in principle at least.  We’ve simply learned to associate them so strongly to one another that in most cases separating the two is just not possible.

On the flip side, this tendency of language to associate itself with our thoughts, also puts certain barriers or restrictions on our thoughts.  If we are always preparing to share our thoughts through language, then we’re going to become somewhat entrained to think in ways that can be most easily expressed in a linguistic form.  So although language may simply be along for the ride with many non-linguistic aspects of thought, our tendency to use it may also structure our thinking and reasoning in large ways.  This would account for why people raised in different cultures with different languages see the world in different ways based on the structure of their language.  While linguistic determinism seems to have been ruled out (the strong version of the Sapir-Whorf hypothesis), there is still strong evidence to support linguistic relativism (the weak version of the Sapir-Whorf hypothesis), whereby one’s language effects their ontology and view of how the world is structured.

If language is so heavily used day-to-day then this phenomenon makes sense as viewed through a PP lens since we’re going to end up putting a high weight on the predictions that link ontology with language since these predictions have been demonstrated to us to be useful most of the time.  Minimal prediction error means that our Bayesian evidence is further supported and the higher the weight carried by these predictions, the more these predictions will restrict our overall thinking, including how our ontology is structured.

Moving on…

I think that these are but a few of the interesting relationships between language and ontology and how a PP framework helps to put it all together nicely, and I just haven’t seen this kind of explanatory power and parsimony in any other kind of conceptual framework about how the brain functions.  This bodes well for the framework and it’s becoming less and less surprising to see it being further supported over time with studies in neuroscience, cognition, psychology, and also those pertaining to pathologies of the brain, perceptual illusions, etc.  In the next post in this series, I’m going to talk about knowledge and how it can be seen through the lens of PP.

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

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.

Virtual Reality & Its Moral Implications

There’s a lot to be said about virtual reality (VR) in terms of our current technological capabilities, our likely prospects for future advancements, and the vast amount of utility that we gain from it.  But, as it is with all other innovations, with great power comes great responsibility.

While there are several types of VR interfaces on the market, used for video gaming or various forms of life simulation, they do have at least one commonality, namely the explicit goal of attempting to convince the user that what they are experiencing is in fact real in at least some sense.  This raises a number of ethical concerns.  While we can’t deny the fact that even reading books and watching movies influences our behavior to some degree, VR is bound to influence our behavior much more readily because of the sheer richness of the qualia and the brain’s inability to distinguish significant differences between a virtual reality and our natural one.  Since the behavioral conditioning schema that our brain employs has evolved to be well adapted to our natural reality, any virtual variety that increasingly approximates it is bound to increasingly affect our behavior.  So we need to be concerned with VR in terms of how it can affect our beliefs, our biases, and our moral inclinations and other behaviors.

One concern with VR is the desensitization to, or normalization of, violence and other undesirable or immoral behaviors.  Many video games have been criticized over the years for this very reason, with the claim that they promote similar behaviors in the users of those games (most especially younger users with more impressionable minds).  These claims have been significantly validated by the American Psychological Association and the American Academy of Pediatrics, where they have both taken firm stances against children and teens playing violent video games, as a result of the accumulated research and meta studies showing a strong link between violent video gaming and increased aggression, anti-social behavior, and sharp decreases in moral engagement and empathy.

Thus, the increasingly realistic nature of VR and the ever-consistent increase in the capacities one has at their disposal within such a virtual space, is bound to exacerbate these types of problems.  If people are able to simulate rape or pedophilia among other morally reprehensible actions and social taboos, will they too become more susceptible to actually engaging in these behaviors once they leave the virtual space and re-enter the real world?  Even if they don’t increase their susceptibility to perform those behaviors, what does such a virtual escapade do to that person’s moral character?  Are they more likely to condone those behaviors (even if they don’t participate in them directly), or to condone other behaviors that have some kind of moral relevance or cognitive overlap with one another?

On the flip side, what if it was possible to use VR as a therapeutic tool to help cure pedophilia or other behavioral problems?  What if one was able to simulate rape, pedophilia or otherwise to reduce their chances of performing those acts in the real world?  Hardly anyone would argue that a virtual rape or molestation is anywhere near as abhorrent or consequential as real instances of such crimes would be, horrific crimes made against real human beings.  While this may only apply to a small number of people, it is at least plausible that such a therapeutic utility would make the world a better place if it prevented an actual rape or other crime from taking place.  If certain people have hard-wired impulses that would normally ruin their lives or the lives of others if left unchecked, then it would be prudent if not morally obligatory to do what we can to prevent such harms from taking place.  So even though this technology could make the otherwise healthy user begin to engage in bad behaviors, it could also be used as an outlet of expression for those already afflicted with similar impulses.  Just as they’ve used VR to help cure anxiety disorders, phobias, PTSD, and other pathologies, by exposing people to various stimuli that help them to overcome their ills, so too may VR possibly provide a cure for other types of mental illnesses and aggressive predispositions such as those related to murder, sexual assault, etc.

Whether VR is used as an outlet for certain behaviors to prevent them from actually happening in the real world, or as a means of curing a person from those immoral inclinations (where the long term goal is to eventually no longer need any VR treatment at all), there are a few paths that could show some promising results to decrease crime and so forth.  But, beyond therapeutic uses, we need to be careful about how these technologies are used generally and how that usage will increasingly affect our moral inclinations.

If society chose to implement some kind of prohibition to limit the types of things people could do in these virtual spaces, that may be of some use, but beyond the fact that this kind of prohibition would likely be difficult to enforce, it would also be a form of selective prohibition that may not be justified to implement.  If one chose to prohibit simulated rape and pedophilia (for example), but not prohibit murder or other forms of assault and violence, then what would justify such a selective prohibition?  We can’t simply rely on an intuition that the former simulated behaviors are somehow more repugnant than the latter (and besides, many would say that murder is just as bad if not worse anyway).  It seems that instead we need to better assess the consequences of each type of simulated behavior on our behavioral conditioning to see if at least some simulated activities should be prohibited while allowing others to persist unregulated.  On the other hand, if prohibiting this kind of activity is not practical or if it can only be implemented by infringing on certain liberties that we have good reasons to protect, then we need to think about some counter-strategies to either better inform people about these kinds of dangers and/or to make other VR products that help to encourage the right kinds of behaviors.

I can’t think of a more valuable use for VR than to help us cultivate moral virtues and other behaviors that are conducive to our well-being and to our living a more fulfilled life.  Anything from reducing our prejudices and biases through exposure to various simulated “out-groups” (for example), to modifying our moral character in more profound ways through artificial realities that can encourage the user to help others in need and to develop habits and inclinations that are morally praiseworthy.  We can even use this technology (and have already to some degree) to work out various moral dilemmas and our psychological response to them without anybody actually dying or getting physically hurt.  Overall, VR certainly holds a lot of promise, but it also poses a lot of psychological danger, thus making it incumbent upon us to talk more about these technologies as they continue to develop.

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).

• Knowledge and the “Brain in a Vat” scenario
• Knowledge: An Expansion of the Platonic Definition
• Neuroscience Arms Race & Our Changing World View
• Neurological Configuration & the Prospects of an Innate Ontology
• Darwin’s Big Idea May Be The Biggest Yet

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:

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.

Neurological Configuration & the Prospects of an Innate Ontology

After a brief discussion on another blog pertaining to whether or not humans possess some kind of an innate ontology or other forms of what I would call innate knowledge, I decided to expand on my reply to that blog post.

While I agree that at least most of our knowledge is acquired through learning, specifically through the acquisition and use of memorized patterns of perception (as this is generally how I would define knowledge), I also believe that there are at least some innate forms of knowledge, including some that would likely result from certain aspects of our brain’s innate neurological configuration and implementation strategy.  This proposed form of innate knowledge would seem to bestow a foundation for later acquiring the bulk of our knowledge that is accomplished through learning.  This foundation would perhaps be best described as a fundamental scaffold of our ontology and thus an innate aspect that our continually developing ontology is based on.

My basic contention is that the hierarchical configuration of neuronal connections in our brains is highly analogous to the hierarchical relationships utilized to produce our conceptualization of reality.  In order for us to make sense of the world, our brains seem to fracture reality into many discrete elements, properties, concepts, propositions, etc., which are all connected to each other through various causal relationships or what some might call semantic hierarchies.  So it seems plausible if not likely that the brain is accomplishing a fundamental aspect of our ontology by our utilizing an innate hardware schema that involves neurological branching.

As the evidence in the neurosciences suggests, it certainly appears that our acquisition of knowledge through learning what those discrete elements, properties, concepts, propositions, etc., are, involves synaptogenesis followed by pruning, modifying, and reshaping a hierarchical neurological configuration, in order to end up with a more specific hierarchical neurological arrangement, and one that more accurately correlates with the reality we are interacting with and learning about through our sensory organs.  Since the specific arrangement that eventually forms couldn’t have been entirely coded for in our DNA (due to it’s extremely high level of complexity and information density), it ultimately had to be fine-tuned to this level of complexity after it’s initial pre-sensory configuration developed.  Nevertheless, the DNA sequences that were naturally selected for to produce the highly capable brains of human beings (as opposed to the DNA that guides the formation of the brain of a much less intelligent animal), clearly have encoded increasingly more effective hardware implementation strategies than our evolutionary ancestors.  These naturally selected neurological strategies seem to control what particular types of causal patterns the brain is theoretically capable of recognizing (including some upper limit of complexity), and they also seem to control how the brain stores and organizes these patterns for later use.  So overall, my contention is that these naturally selected strategies in themselves are a type of knowledge, because they seem to provide the very foundation for our initial ontology.

Based on my understanding, after many of the initial activity-independent mechanisms for neural development have occurred in some region of the developing brain such as cellular differentiation, cellular migration, axon guidance, and some amount of synapse formation, then the activity-dependent mechanisms for neuronal development (such as neural activity caused by the sensory organs in the process of learning), finally begin to modify those synapses and axons into a new hierarchical arrangement.  It is especially worth noting that even though much of the synapse formation during neural development is mediated by activity-dependent mechanisms, such as the aforementioned neural activity produced by the sensory organs during perceptual development and learning, there is also spontaneous neural activity forming many of these synapses even before any sensory input is present, thus contributing to the innate neurological configuration (i.e. that which is formed before any sensation or learning has occurred).

Thus, the subsequent hierarchy formed through neural/sensory stimulation via learning appears to begin from a parent hierarchical starting point based on neural developmental processes that are coded for in our DNA as well as synaptogenic mechanisms involving spontaneous pre-sensory neural activity.  So our brain’s innate (i.e. pre-sensory) configuration likely contributes to our making sense of the world by providing a starting point that reflects the fundamental hierarchical nature of reality that all subsequent knowledge is built off of.  In other words, it seems that if our mature conceptualization of reality involves a very specific type of hierarchy, then an innate/pre-sensory hierarchical schema of neurons would be a plausible if not expected physical foundation for it (see Edelman’s Theory of Neuronal Group Selection within this link for more empirical support of these points).

Additionally, if the brain’s wiring has evolved in order to see dimensions of difference in the world (unique sensory/perceptual patterns that is, such as quantity, colors, sounds, tastes, smells, etc.), then it would make sense that the brain can give any particular pattern an identity by having a unique schema of hardware or unique use of said hardware to perceive such a pattern and distinguish it from other patterns.  After the brain does this, the patterns are then arguably organized by the logical absolutes.  For example, if the hardware scheme or process used to detect a particular pattern “A” exists and all other patterns we perceive have or are given their own unique hardware-based identity (i.e. “not-A” a.k.a. B, C, D, etc.), then the brain would effectively be wired such that pattern “A” = pattern “A” (law of identity), any other pattern which we can call “not-A” does not equal pattern “A” (law of non-contradiction), and any pattern must either be “A” or some other pattern even if brand new, which we can also call “not-A” (law of the excluded middle).  So by the brain giving a pattern a physical identity (i.e. a specific type of hardware configuration in our brain that when activated, represents a detection of one specific pattern), our brains effectively produce the logical absolutes by nature of the brain’s innate wiring strategy which it uses to distinguish one pattern from another.  So although it may be true that there can’t be any patterns stored in the brain until after learning begins (through sensory experience), the fact that the DNA-mediated brain wiring strategy inherently involves eventually giving a particular learned pattern a unique neurological hardware identity to distinguish it from other stored patterns, suggests that the logical absolutes themselves are an innate and implicit property of how the brain stores recognized patterns.

In short, if it is true that any and all forms of reasoning as well as the ability to accumulate knowledge simply requires logic and the recognition of causal patterns, and if the brain’s innate neurological configuration schema provides the starting foundation for both, then it would seem reasonable to conclude that the brain has at least some types of innate knowledge.

Knowledge: An Expansion of the Platonic Definition

In the first post I ever wrote on this blog, titled: Knowledge and the “Brain in a Vat” scenario, I discussed some elements concerning the Platonic definition of knowledge, that is, that knowledge is ultimately defined as “justified true belief”.  I further refined the Platonic definition (in order to account for the well-known Gettier Problem) such that knowledge could be better described as “justified non-coincidentally-true belief”.  Beyond that, I also discussed how one’s conception of knowledge (or how it should be defined) should consider the possibility that our reality may be nothing more than the product of a mad scientist feeding us illusory sensations/perceptions with our brain in a vat, and thus, that how we define things and adhere to those definitions plays a crucial role in our conception and mutual understanding of any kind of knowledge.  My concluding remarks in that post were:

“While I’m aware that anything discussed about the metaphysical is seen by some philosophers to be completely and utterly pointless, my goal in making the definition of knowledge compatible with the BIV scenario is merely to illustrate that if knowledge exists in both “worlds” (and our world is nothing but a simulation), then the only knowledge we can prove has to be based on definitions — which is a human construct based on hierarchical patterns observed in our reality.”

While my views on what knowledge is or how it should be defined have changed somewhat in the past three years or so since I wrote that first blog post, in this post, I’d like to elaborate on this key sentence, specifically with regard to how knowledge is ultimately dependent on the recall and use of previously observed patterns in our reality as I believe that this is the most important aspect regarding how to define knowledge.  After making a few related comments on another blog (https://nwrickert.wordpress.com/2015/03/07/knowledge-vs-belief/), I decided to elaborate on some of those comments accordingly.

I’ve elsewhere mentioned how there is a plethora of evidence that suggests that intelligence is ultimately a product of pattern recognition (1, 2, 3).  That is, if we recognize patterns in nature and then commit them to memory, we can later use those remembered patterns to our advantage in order to accomplish goals effectively.  The more patterns that we can recognize and remember, specifically those that do in fact correlate with reality (as opposed to erroneously “recognized” patterns that are actually non-existent), the better our chances of predicting the consequences of our actions accurately, and thus the better chances we have at obtaining our goals.  In short, the more patterns that we can recognize and remember, the greater our intelligence.  It is therefore no coincidence that intelligence tests are primarily based on gauging one’s ability to recognize patterns (e.g. solving Raven’s Progressive Matrices, puzzles, etc.).

To emphasize the role of pattern recognition as it applies to knowledge, if we use my previously modified Platonic definition of knowledge, that is,  that knowledge is defined as “justified, non-coincidentally-true belief”, then I must break down the individual terms of this definition as follows, starting with “belief”:

• Belief = Recognized patterns of causality that are stored into memory for later recall and use.
• Non-Coincidentally-True = The belief positively and consistently correlates with reality, and thus not just through luck or chance.
• Justified = Empirical evidence exists to support said belief.

So in summary, I have defined knowledge (more specifically) as:

“Recognized patterns of causality that are stored into memory for later recall and use, that positively and consistently correlate with reality, and for which that correlation has been validated by empirical evidence (e.g. successful predictions made and/or goals accomplished through the use of said recalled patterns)”.

This means that if we believe something to be true that is unfalsifiable (such as religious beliefs that rely on faith), since it has not met the justification criteria, it fails to be considered knowledge (even if it is still considered a “belief”).  Also, if we are able to make a successful prediction with the patterns we’ve recognized, yet are only able to do so once, due to the lack of consistency, we likely just got lucky and didn’t actually correctly identify a pattern that correlates with reality, and thus this would fail to count as knowledge.  Finally, one should also note that the patterns that are recognized were not specifically defined as “consciously” recognized/remembered, nor was it specified that the patterns couldn’t be innately acquired/stored into memory (through DNA coded or other pre-sensory neural developmental mechanisms).  Thus, even procedural knowledge like learning to ride a bike or other forms of “muscle memory” used to complete a task, or any innate form of knowledge (acquired before/without sensory input) would be an example of unconscious or implicit knowledge that still fulfills this definition I’ve given above.  In the case of unconscious/implicit knowledge, we would have to accept that “beliefs” can also be unconscious/implicit (in order to remain consistent with the definition I’ve chosen), and I don’t see this as being a problem at all.  One just has to keep in mind that when people use the term “belief”, they are likely going to be referring to only those that are in our consciousness, a subset of all beliefs that exist, and thus still correct and adherent to the definition laid out here.

This is how I prefer to define “knowledge”, and I think it is a robust definition that successfully solves many (though certainly not all) of the philosophical problems that one tends to encounter in epistemology.

Neuroscience Arms Race & Our Changing World View

At least since the time of Hippocrates, people began to realize that the brain was the physical correlate of consciousness and thought.  Since then, the fields of psychology, neuroscience, and several inter-related fields have emerged.  There have been numerous advancements made within the field of neuroscience during the last decade or so, and in that same time frame there has also been an increased interest in the social, religious, philosophical, and moral implications that have precipitated from such a far-reaching field.  Certainly the medical knowledge we’ve obtained from the neurosciences has been the primary benefit of such research efforts, as we’ve learned quite a bit more about how the brain works, how it is structured, and the ongoing neuropathology that has led to improvements in diagnosing and treating various mental illnesses.  However, it is the other side of neuroscience that I’d like to focus on in this post — the paradigm shift relating to how we are starting to see the world around us (including ourselves), and how this is affecting our goals as well as how to achieve them.

Paradigm Shift of Our World View

Aside from the medical knowledge we are obtaining from the neurosciences, we are also gaining new perspectives on what exactly the “mind” is.  We’ve come a long way in demonstrating that “mental” or “mind” states are correlated with physical brain states, and there is an ever growing plethora of evidence which suggests that these mind states are in fact caused by these brain states.  It should come as no surprise then that all of our thoughts and behaviors are also caused by these physical brain states.  It is because of this scientific realization that society is currently undergoing an important paradigm shift in terms of our world view.

If all of our thoughts and behaviors are mediated by our physical brain states, then many everyday concepts such as thinking, learning, personality, and decision making can take on entirely new meanings.  To illustrate this point, I’d like to briefly mention the well known “nature vs. nurture” debate.  The current consensus among scientists is that people (i.e. their thoughts and behavior) are ultimately products of both their genes and their environment.

Genes & Environment

From a neuroscientific perspective, the genetic component is accounted for by noting that genes have been shown to play a very large role in directing the initial brain wiring schema of an individual during embryological development and through gestation.  During this time, the brain is developing very basic instinctual behavioral “programs” which are physically constituted by vastly complex neural networks, and the body’s developing sensory organs and systems are also connected to particular groups of these neural networks.  These complex neural networks, which have presumably been naturally selected for in order to benefit the survival of the individual, continue being constructed after gestation and throughout the entire ontogenic evolution of the individual (albeit to lesser degrees over time).

As for the environmental component, this can be further split into two parts: the internal and the external environment.  The internal environment within the brain itself, including various chemical concentration gradients partly mediated by random Brownian motion, provides some gene expression constraints as well as some additional guidance to work with the genetic instructions to help guide neuronal growth, migration, and connectivity.  The external environment, consisting of various sensory stimuli, seems to modify this neural construction by providing a form of inputs which may cause the constituent neurons within these neural networks to change their signal strength, change their action potential threshold, and/or modify their connections with particular neurons (among other possible changes).

Causal Constraints

This combination of genetic instructions and environmental interaction and input produces a conscious, thinking, and behaving being through a large number of ongoing and highly complex hardware changes.  It isn’t difficult to imagine why these insights from neuroscience might modify our conventional views of concepts such as thinking, learning, personality, and decision making.  Prior to these developments over the last few decades, the brain was largely seen as a sort of “black box”, with its internal milieu and functional properties remaining mysterious and inaccessible.  From that time and prior to it, for millennia, many people have assumed that our thoughts and behaviors were self-caused or causa sui.  That is, people believed that they themselves (i.e. some causally free “consciousness”, or “soul”, etc.) caused their own thoughts and behavior as opposed to those thoughts and behaviors being ultimately caused by physical processes (e.g. neuronal activity, chemical reactions, etc.).

Neuroscience (as well as biochemistry and its underlying physics) has shed a lot of light on this long-held assumption and, as it stands, the evidence has shown this prior assumption to be false.  The brain is ultimately controlled by the laws of physics since every chemical reaction and neural event that physically produces our thoughts, choices, and behaviors, have never been shown to be causally free from these physically guiding constraints.  I will mention that quantum uncertainty or quantum “randomness” (if ontologically random) does provide some possible freedom from physical determinism.  However, these findings from quantum physics do not provide any support for self-caused thoughts or behaviors.  Rather, it merely shows that those physically constrained thoughts and behaviors may never be completely predictable by physical laws no matter how much data is obtained.  In other words, our thoughts and behaviors are produced by highly predictable (although not necessarily completely predictable) physical laws and constraints as well as some possible random causal factors.

As a result of these physical causal constraints, the conventional perspective of an individual having classical free will has been shattered.  Our traditional views of human attributes including morality, choices, ideology, and even individualism are continuing to change markedly.  Not surprisingly, there are many people uncomfortable with these scientific discoveries including members of various religious and ideological groups that are largely based upon and thus depend on the very presupposition of precepts such as classical free will and moral responsibility.  The evidence that is compiling from the neurosciences is in fact showing that while people are causally responsible for their thoughts, choices, and behavior (i.e. an individual’s thoughts and subsequent behavior are constituents of a causal chain of events), they are not morally responsible in the sense that they can choose to think or behave any differently than they do, for their thoughts and behavior are ultimately governed by physically constrained neural processes.

New World View

Now I’d like to return to what I mentioned earlier and consider how these insights from neuroscience may be drastically modifying how we look at concepts such as thinking, learning, personality, and decision making.  If our brain is operating via these neural network dynamics, then conscious thought appears to be produced by a particular subset of these neural network configurations and processes.  So as we continue to learn how to more directly control or alter these neural network arrangements and processes (above and beyond simply applying electrical potentials to certain neural regions in order to bring memories or other forms of imagery into consciousness, as we’ve done in the past), we should be able to control thought generation from a more “bottom-up” approach.  Neuroscience is definitely heading in this direction, although there is a lot of work to be done before we have any considerable knowledge of and control over such processes.

Likewise, learning seems to consist of a certain type of neural network modification (involving memory), leading to changes in causal pattern recognition (among other things) which results in our ability to more easily achieve our goals over time.  We’ve typically thought of learning as the successful input, retention, and recall of new information, and we have been achieving this “learning” process through the input of environmental stimuli via our sensory organs and systems.  In the future, it may be possible to once again, as with the aforementioned bottom-up thought generation, physically modify our neural networks to directly implant memories and causal pattern recognition information in order to “learn” without any actual sensory input, and/or we may be able to eventually “upload” information in a way that bypasses the typical sensory pathways thus potentially allowing us to catalyze the learning process in unprecedented ways.

If we are one day able to more directly control the neural configurations and processes that lead to specific thoughts as well as learned information, then there is no reason that we won’t be able to modify our personalities, our decision-making abilities and “algorithms”, etc.  In a nutshell, we may be able to modify any aspect of “who” we are in extraordinary ways (whether this is a “good” or “bad” thing is another issue entirely).  As we come to learn more about the genetic components of these neural processes, we may also be able to use various genetic engineering techniques to assist with the necessary neural modifications required to achieve these goals.  The bottom line here is that people are products of their genes and environment, and by manipulating both of those causal constraints in more direct ways (e.g. through the use of neuroscientific techniques), we may be able to achieve previously unattainable abilities and perhaps in a relatively miniscule amount of time.  It goes without saying that these methods will also significantly affect our evolutionary course as a species, allowing us to enter new landscapes through our substantially enhanced ability to adapt.  This may be realized through these methods by finding ways to improve our intelligence, memory, or other cognitive faculties, effectively giving us the ability to engineer or re-engineer our brains as desired.

Neuroscience Arms Race

We can see that increasing our knowledge and capabilities within the neurosciences has the potential for drastic societal changes, some of which are already starting to be realized.  The impact that these fields will have on how we approach the problem of criminal, violent, or otherwise undesirable behavior can not be overstated.  Trying to correct these issues by focusing our efforts on the neural or cognitive substrate that underlie them, as opposed to using less direct and more external means (e.g. social engineering methods) that we’ve been using thus far, may lead to much less expensive solutions as well as solutions that may be realized much, much more quickly.

As with any scientific discovery or subsequent technology produced from it, neuroscience has the power to bestow on us both benefits as well as disadvantages.  I’m reminded of the ground-breaking efforts made within nuclear physics several decades ago, whereby physicists not only gained precious information about subatomic particles (and their binding energies) but also how to release these enormous amounts of energy from nuclear fusion and fission reactions.  It wasn’t long after these breakthrough discoveries were made before they were used by others to create the first atomic bombs.  Likewise, while our increasing knowledge within neuroscience has the power to help society improve by optimizing our brain function and behavior, it can also be used by various entities to manipulate the populace for unethical reasons.

For example, despite the large number of free market proponents who claim that the economy need not be regulated by anything other than rational consumers and their choices of goods and services, corporations have clearly increased their use of marketing strategies that take advantage of many humans’ irrational tendencies (whether it is “buy one get one free” offers, “sales” on items that have artificially raised prices, etc.).  Politicians and other leaders have been using similar tactics by taking advantage of voters’ emotional vulnerabilities on certain controversial issues that serve as nothing more than an ideological distraction in order to reduce or eliminate any awareness or rational analysis of the more pressing issues.

There are already research and development efforts being made by these various entities in order to take advantage of these findings within neuroscience such that they can have greater influence over people’s decisions (whether it relates to consumers’ purchases, votes, etc.).  To give an example of some of these R&D efforts, it is believed that MRI (Magnetic Resonance Imaging) or fMRI (functional Magnetic Resonance Imaging) brain scans may eventually be able to show useful details about a person’s personality or their innate or conditioned tendencies (including compulsive or addictive tendencies, preferences for certain foods or behaviors, etc.).  This kind of capability (if realized) would allow marketers to maximize how many dollars they can squeeze out of each consumer by optimizing their choices of goods and services and how they are advertised. We have already seen how purchases made on the internet, if tracked, begin to personalize the advertisements that we see during our online experience (e.g. if you buy fishing gear online, you may subsequently notice more advertisements and pop-ups for fishing related goods and services).  If possible, the information found using these types of “brain probing” methods could be applied to other areas, including that of political decision making.

While these methods derived from the neurosciences may be beneficial in some cases, for instance, by allowing the consumer more automated access to products that they may need or want (which will likely be a selling point used by these corporations for obtaining consumer approval of such methods), it will also exacerbate unsustainable consumption and other personal or societally destructive tendencies and it is likely to continue to reduce (or eliminate) whatever rational decision making capabilities we still have left.

Final Thoughts

As we can see, neuroscience has the potential to (and is already starting to) completely change the way we look at the world.  Further advancements in these fields will likely redefine many of our goals as well as how to achieve them.  It may also allow us to solve many problems that we face as a species, far beyond simply curing mental illnesses or ailments.  The main question that comes to mind is:  Who will win the neuroscience arms race?  Will it be those humanitarians, scientists, and medical professionals that are striving to accumulate knowledge in order to help solve the problems of individuals and societies as well as to increase their quality of life?  Or will it be the entities that are trying to accumulate similar knowledge in order to take advantage of human weaknesses for the purposes of gaining wealth and power, thus exacerbating the problems we currently face?