“The Bounds of Subjectivity”

The world as it really is
Unknowable, despite conviction
We hope for objectivity
And yet we see prediction

Perception is the mind’s best guess, to make some sense of all the mess
Expectations frame the lens, ontology, each one depends

Controlled hallucination
To see what we want to see
Perception is not a mirror
But the bounds of subjectivity

Our minds are but a model, sensations flowing, open throttle
A story written, a narrative, to which high credence we will give

Grounded on abstraction
Toward emotion reason shouts
Embodied filters blinding us
Wishful thinking wins the bout

Behold the power of intuition, as certainty comes to fruition
Housed in our unconscious mind, yet fallible we ought to find

Beliefs entangled with desire
Can I hold this view of mine?
Search for reasons to confirm
True or not, we all assign

Not all views have equal merit, unable to know, unless we share it
Reducing that complexity, except when over-vexed we’ll be

Worlds are shaped by what we want
In how we act and how we view
Rigid ways take hold of us
The old interprets all the new

Emotions are the reason’s master, ignoring this will bring disaster
Hume was right about the passions, finding reasons is our fashion

When evidence begins to mount
Against a highly prized belief
Minds can change, a last resort
From dissonance, we seek relief

Ignore the proof, for it can’t be! Or change your views for harmony
Highlight all coincidence, though it lacks significance

Must I believe what’s likely true?
Not if I can find a way!
A means to cover my own eyes
Truth be damned, emotions stray

Coincidence we seem to find, memories, tricks of the mind
‘Tis the frequency illusion, we’re falling prey to this delusion

The path of least resistance
Always tempting to the end
Sacrificing truth for self
How far the mind can bend

A marvel of our evolution, the ego fights its dissolution
Fallacies run far and wide, despite the logic by our side

Remember what you must
Your world is up to you
Conveniently forget the rest
And false becomes the true

Few will try to face the truth
Combat the bias, critique the “I”
Only the bravest make attempts
By far the most would rather die

By far the most would rather lie
To themselves, to everyone
Confirmation biases
From human nature, we try to run

You can run but you can’t hide!
Our biases remain
But evidence has verified
There’s knowledge we can gain


“Black Mirror” Reflections: U.S.S. Callister (S4, E1)

This Black Mirror reflection will explore season 4, episode 1, which is titled “U.S.S. Callister”.  You can click here to read my last Black Mirror reflection (season 3, episode 2: “Playtest”).  In U.S.S. Callister, we’re pulled into the life of Robert Daly (Jesse Plemons), the Chief Technical Officer at Callister Inc., a game development company that has produced a multiplayer simulated reality game called Infinity.  Within this game, users control a starship (an obvious homage to Star Trek), although Daly, the brilliant programmer behind this revolutionary game, has his own offline version of the game which has been modded to look like his favorite TV show Space Fleet, where Daly is the Captain of the ship.

We quickly learn that most of the employees at Callister Inc. don’t treat Daly very kindly, including his company’s co-founder James Walton (Jimmy Simpson).  Daly appears to be an overly passive, shy, introvert.  During one of Daly’s offline gaming sessions at home, we come to find out that the Space Fleet characters aboard the starship look just like his fellow employees, and as Captain of his simulated crew, he indulges in berating them all.  Due to the fact that Daly is Captain and effectively controls the game, he is rendered nearly omnipotent, and able to force his crew to perpetually bend to his will, lest they suffer immensely.

It turns out that these characters in the game are actually conscious, created from Daly having surreptitiously acquired his co-workers’ DNA and somehow replicated their consciousness and memories and uploaded them into the game (and any biologists or neurologists out there, let’s just forget for the moment that DNA isn’t complex enough to store this kind of neurological information).  At some point, a wrench is thrown into Daly’s deviant exploits when a new co-worker, programmer Nanette Cole (Cristin Milioti), is added to his game and manages to turn the crew against him.  Once the crew finds a backdoor means of communicating with the world outside the game, Daly’s world is turned upside down with a relatively satisfying ending chock-full of poetic justice, as his digitized, enslaved crew members manage to escape while he becomes trapped inside his own game as it’s being shutdown and destroyed.

Daly stuck

This episode is rife with a number of moral issues that build on one another, all deeply coupled with the ability to engineer a simulated reality (perceptual augmentation).  Virtual worlds carry a level of freedom that just isn’t possible in the real world, where one can behave in countless ways with little or no consequence, whether acting with beneficence or utter malice.  One can violate physical laws as well as prescriptive laws, opening up a new world of possibilities that are free to evolve without the feedback of social norms, legal statutes, and law enforcement.

People have long known about various ways of escaping social and legal norms through fiction and game playing, where one can imagine they are somebody else, living in a different time and place, and behave in ways they’d never even think of doing in the real world.

But what happens when they’re finished with the game and go back to the real world with all its consequences, social norms and expectations?  Doesn’t it seem likely that at least some of the behaviors cultivated in the virtual world will begin to rear their ugly heads in the real world?  One can plausibly argue that violent game playing is simply a form of psychological sublimation, where we release many of our irrational and violent impulses in a way that’s more or less socially acceptable.  But there’s also bound to be a difference between playing a very abstract game involving violence or murder, such as the classic board-game Clue, and playing a virtual reality game where your perceptions are as realistic as can be and you choose to murder some other character in cold blood.

Clearly in this episode, Daly was using the simulated reality as a means of releasing his anger and frustration, by taking it out on reproductions of his own co-workers.  And while a simulated experiential alternative could be healthy in some cases, in terms of its therapeutic benefit and better control over the consequences of the simulated interaction, we can see that Daly took advantage of his creative freedom, and wielded it to effectively fashion a parallel universe where he was free to become a psychopath.

It would already be troubling enough if Daly behaved as he did to virtual characters that were not actually conscious, because it would still show Daly pretending that they are conscious; a man who wants them to be conscious.  But the fact that Daly knows they are conscious makes him that much more sadistic.  He is effectively in the position of a god, given his powers over the simulated world and every conscious being trapped within it, and he has used these powers to generate a living hell (thereby also illustrating the technology’s potential to, perhaps one day, generate a living heaven).  But unlike the hell we hear about in myths and religious fables, this is an actual hell, where a person can suffer the worst fates imaginable (it is in fact only limited by the programmer’s imagination) such as experiencing the feeling of suffocation, yet unable to die and thus with no end in sight.  And since time is relative, in this case based on the ratio of real time to simulated time (or the ratio between one simulated time and another), a character consciously suffering in the game could feel as if they’ve been suffering for months, when the god-like player has only felt several seconds pass.  We’ve never had to morally evaluate these kinds of situations before, and we’re getting to a point where it’ll be imperative for us to do so.

Someday, it’s very likely that we’ll be able to create an artificial form of intelligence that is conscious, and it’s up to us to initiate and maintain a public conversation that addresses how our ethical and moral systems will need to accommodate new forms of conscious moral agents.

Jude Law, Haley Joel Osment, Brendan Gleeson, and Brian Turk in Artificial Intelligence: AI (2001)

We’ll also need to figure out how to incorporate a potentially superhuman level of consciousness into our moral frameworks, since these frameworks often have an internal hierarchy that is largely based on the degree or level of consciousness that we ascribe to other individuals and to other animals.  If we give moral preference to a dog over a worm, and preference to a human over a dog (for example), then where would a being with superhuman consciousness fit within that framework?  Most people certainly wouldn’t want these beings to be treated as gods, but we shouldn’t want to treat them like slaves either.  If nothing else, they’ll need to be treated like people.

Technologies will almost always have that dual potential, where they can be used for achieving truly admirable goals and to enhance human well being, or used to dominate others and to exacerbate human suffering.  So we need to ask ourselves, given a future world where we have the capacity to make any simulated reality we desire, what kind of world do we want?  What kind of world should we want?  And what kind of person do you want to be in that world?  Answering these questions should say a lot about our moral qualities, serving as a kind of window into the soul of each and every one of us.

“Black Mirror” Reflections: Playtest (S3, E2)


Black Mirror, the British science-fiction anthology series created by Charlie Brooker, does a pretty good job experimenting with a number of illuminating concepts that highlight how modern culture is becoming increasingly shaped by (and vulnerable to) various technological advances and changes.  I’ve been interested in a number of these concepts for many years now, not least because of the many important philosophical implications (including a number of moral issues) that they point to.  I’ve decided to start a blog post series that will explore the contents of these episodes.  My intention with this blog post series, which I’m calling “Black Mirror” Reflections, will be to highlight some of my own takeaways from some of my favorite episodes.

I’d like to begin with season 2, episode 3, “Playtest”.  I’m just going to give a brief summary here as you can read the full episode summary in the link provided above.  In this episode, Cooper (Wyatt Russell) decides to travel around the world, presumably as a means of dealing with the recent death of his father, who died from early-onset Alzheimer’s.  After finding his way to London, his last destination before planning to return home to America, he runs into a problem with his credit card and bank account where he can’t access the money he needs to buy his plane ticket.

While he waits for his bank to fix the problem with his account, Cooper decides to earn some cash using an “Oddjobs” app, which provides him with a number of short-term job listings in the area, eventually leading him to “SaitoGemu,” a video game company looking for game testers to try a new kind of personalized horror game involving a (seemingly) minimally invasive brain implant procedure.  He briefly hesitates but, desperate for money and reasonably confident in its safety, he eventually consents to the procedure whereby the implant is intended to wire itself into the gamer’s brain, resulting in a form of perceptual augmentation and a semi-illusory reality.


The implant is designed to (among other things) scan your memories and learn what your worst fears are, in order to integrate these into the augmented perceptions, producing a truly individualized, and maximally frightening horror game experience.  Needless to say, at some point Cooper begins to lose track of what’s real and what’s illusory, and due to a malfunction, he’s unable to exit the game and he ends up going mad and eventually dying as a result of the implant unpredictably overtaking (and effectively frying) his brain.

nanobots in brain

There are a lot of interesting conceptual threads in this story, and the idea of perceptual augmentation is a particularly interesting theme that finds it’s way into a number of other Black Mirror episodes.  While holographic and VR-headset gaming technologies can produce their own form of augmented reality, perceptual augmentation carried out on a neurological level isn’t even in the same ballpark, having qualitative features that are far more advanced and which are more akin to those found in the Wachowski’s The Matrix trilogy or James Cameron’s Total Recall.  Once the user is unable to distinguish between the virtual world and the external reality, with the simulator having effectively passed a kind of graphical version of the Turing Test, then one’s previous notion of reality is effectively shattered.  To me, this technological idea is one of the most awe-inspiring (yet sobering) ideas within the Black Mirror series.

The inability to discriminate between the two worlds means that both worlds are, for all practical purposes, equally “real” to the person experiencing them.  And the fact that one simulationcan’t tell the difference between such worlds ought to give a person pause to re-evaluate what it even means for something to be real.  If you doubt this, then just imagine if you were to find out one day that your entire life has really been the result of a computer simulation, created and fabricated by some superior intelligence living in a layer of reality above your own (we might even think of this being as a “god”).  Would this realization suddenly make your remembered experiences imaginary and meaningless?  Or would your experiences remain just as “real” as they’ve always been, even if they now have to be reinterpreted within a context that grounds them in another layer of reality?

To answer this question honestly, we ought to first realize that we’re likely fully confident that what we’re experiencing right now is reality, is real, is authentic, and is meaningful (just as Cooper was at some point in his gaming “adventure”).  And this seems to be at least a partial basis for how we define what is real, and how we differentiate the most vivid and qualitatively rich experiences from those we might call imaginary, illusory, or superficial.  If what we call reality is really just a set of perceptions, encompassing every conscious experience from the merely quotidian to those we deem to be extraordinary, would we really be justified in dismissing all of these experiences and their value to us if we were to discover that there’s a higher layer of reality, residing above the only reality we’ve ever known?

For millennia, humans have pondered over whether or not the world is “really” the way we see it, with perhaps the most rigorous examination of this metaphysical question undertaken by the German philosopher Immanuel Kant, with his dichotomy of the phenomenon and the noumenon (i.e. the way we see the world or something in the world versus the way the world or thing “really is” in itself, independent of our perception).  Even if we assume the noumenon exists, we can never know anything about the thing in itself, by our being fundamentally limited by our own perceptual categories and the way we subjectively interpret the world.  Similarly, we can never know for certain whether or not we’re in a simulation.

Looking at the situation through this lens, we can then liken the question of how to (re)define reality within the context of a simulation with the question of how to (re)define reality within the context of a world as it really is in itself, independent of our perception.  Both the possibility of our being in a simulation and the possibility of our perceptions stemming from an unknowable noumenal world could be true (and would likely be unfalsifiable), and yet we still manage to use and maintain a relatively robust conception and understanding of reality.  This leads me to conclude that reality is ultimately defined by pragmatic considerations (mostly those pertaining to our ability to make successful predictions and achieve our goals), and thus the possibility of our one day learning about a new, higher level of reality should merely add to our existing conception of reality, rather than completely negating it, even if it turns out to be incomplete.

Another interesting concept in this episode involves the basic design of the individualized horror game itself, where a computer can read your thoughts and memories, and then surmise what your worst fears are.  This is a technological feat that is possible in principle, and one with far-reaching implications that concern our privacy, safety, and autonomy.  Just imagine if such a power were unleashed by corporations, or mind-readingthe governments owned by those corporations, to acquire whatever information they wanted from your mind, to find out how to most easily manipulate you in terms of what you buy, who you vote for, what you generally care about, or what you do any and every day of your life.  The Orwellian possibilities are endless.

Marketing firms (both corporatocratic and political) have already been making use of discoveries in psychology and neuroscience, finding new ways to more thoroughly exploit our cognitive biases to get us to believe and desire whatever will benefit them most.  Adding to this the future ability to read our thoughts and manipulate our perceptions (even if this is first implemented as a seemingly innocuous video game), this will establish a new means of mass surveillance, where we can each become a potential “camera” watching one another (a theme also highlighted in BM, S4E3: Crocodile), while simultaneously exposing our most private of thoughts, and transmitting them to some centralized database.  Once we reach these technological heights (it’s not a matter of if but when), depending on how it’s employed, we may find ourselves no longer having the freedom to lie or to keep a secret, nor the freedom of having any mental privacy whatsoever.

To be fair, we should realize that there are likely to be undeniable benefits in our acquiring these capacities (perceptual augmentation and mind-reading), such as making virtual paradises with minimal resources, finding new ways of treating phobias, PTSD, brain to cloudand other pathologies; giving us the power of telepathy and superhuman intelligence by connecting our brains to the cloud, giving us the ability to design error-proof lie detectors and other vast enhancements in maximizing personal security and reducing crime.  But there are also likely to be enormous losses in personal autonomy, as our available “choices” are increasingly produced and constrained by automated algorithms; there are likely to be losses in privacy, and increasing difficulties in ascertaining what is true and what isn’t, since our minds will be vulnerable to artificially generated perceptions created by entities and institutions that want to deceive us.

Although we’ll constantly need to be on the lookout for these kinds of potential dangers as they arise, in the end, we may find ourselves inadvertently allowing these technologies to creep into our lives, one consumer product at a time.

The Experientiality of Matter

If there’s one thing that Nietzsche advocated for, it was to eliminate dogmatism and absolutism from one’s thinking.  And although I generally agree with him here, I do think that there is one exception to this rule.  One thing that we can be absolutely certain of is our own conscious experience.  Nietzsche actually had something to say about this (in Beyond Good and Evil, which I explored in a previous post), where he responds to Descartes’ famous adage “cogito ergo sum” (the very adage associated with my blog!), and he basically says that Descartes’ conclusion (“I think therefore I am”) shows a lack of reflection concerning the meaning of “I think”.  He wonders how he (and by extension, Descartes) could possibly know for sure that he was doing the thinking rather than the thought doing the thinking, and he even considers the possibility that what is generally described as thinking may actually be something more like willing or feeling or something else entirely.

Despite Nietzsche’s criticism against Descartes in terms of what thought is exactly or who or what actually does the thinking, we still can’t deny that there is thought.  Perhaps if we replace “I think therefore I am” with something more like “I am conscious, therefore (my) conscious experience exists”, then we can retain some core of Descartes’ insight while throwing out the ambiguities or uncertainties associated with how exactly one interprets that conscious experience.

So I’m in agreement with Nietzsche in the sense that we can’t be certain of any particular interpretation of our conscious experience, including whether or not there is an ego or a self (which Nietzsche actually describes as a childish superstition similar to the idea of a soul), nor can we make any certain inferences about the world’s causal relations stemming from that conscious experience.  Regardless of these limitations, we can still be sure that conscious experience exists, even if it can’t be ascribed to an “I” or a “self” or any particular identity (let alone a persistent identity).

Once we’re cognizant of this certainty, and if we’re able to crawl out of the well of solipsism and eventually build up a theory about reality (for pragmatic reasons at the very least), then we must remain aware of the priority of consciousness in any resultant theory we construct about reality, with regard to its structure or any of its other properties.  Personally, I believe that some form of naturalistic physicalism (a realistic physicalism) is the best candidate for an ontological theory that is the most parsimonious and explanatory for all that we experience in our reality.  However, most people that make the move to adopt some brand of physicalism seem to throw the baby out with the bathwater (so to speak), whereby consciousness gets eliminated by assuming it’s an illusion or that it’s not physical (therefore having no room for it in a physicalist theory, aside from its neurophysiological attributes).

Although I used to feel differently about consciousness (and it’s relationship to physicalism), where I thought it was plausible for it to be some kind of an illusion, upon further reflection I’ve come to realize that this was a rather ridiculous position to hold.  Consciousness can’t be an illusion in the proper sense of the word, because the experience of consciousness is real.  Even if I’m hallucinating where my perceptions don’t directly correspond with the actual incoming sensory information transduced through my body’s sensory receptors, then we can only say that the perceptions are illusory insofar as they don’t directly map onto that incoming sensory information.  But I still can’t say that having these experiences is itself an illusion.  And this is because consciousness is self-evident and experiential in that it constitutes whatever is experienced no matter what that experience consists of.

As for my thoughts on physicalism, I came to realize that positing consciousness as an intrinsic property of at least some kinds of physical material (analogous to a property like mass) allows us to avoid having to call consciousness non-physical.  If it is simply an experiential property of matter, that doesn’t negate its being a physical property of that matter.  It may be that we can’t access this property in such a way as to evaluate it with external instrumentation, like we can for all the other properties of matter that we know of such as mass, charge, spin, or what-have-you, but that doesn’t mean an experiential property should be off limits for any physicalist theory.  It’s just that most physicalists assume that everything can or has to be reducible to the externally accessible properties that our instrumentation can measure.  And this suggests that they’ve simply assumed that the physical can only include externally accessible properties of matter, rather than both internally and externally accessible properties of matter.

Now it’s easy to see why science might push philosophy in this direction because its methodology is largely grounded on third-party verification and a form of objectivity involving the ability to accurately quantify everything about a phenomenon with little or no regard for introspection or subjectivity.  And I think that this has caused many a philosopher to paint themselves into a corner by assuming that any ontological theory underlying the totality of our reality must be constrained in the same way that the physical sciences are.  To see why this is an unwarranted assumption, let’s consider a “black box” that can only be evaluated by a certain method externally.  It would be fallacious to conclude that just because we are unable to access the inside of the box, that the box must therefore be empty inside or that there can’t be anything substantially different inside the box compared to what is outside the box.

We can analogize this limitation of studying consciousness with our ability to study black holes within the field of astrophysics, where we’ve come to realize that accessing any information about their interior (aside from how much mass there is) is impossible to do from the outside.  And if we managed to access this information (if there is any) from the inside by leaping past its outer event horizon, it would be impossible for us to escape and share any of that information.  The best we can do is to learn what we can from the outside behavior of the black hole in terms of its interaction with surrounding matter and light and infer something about the inside, like how much matter it contains (e.g. we can infer the mass of a black hole from its outer surface area).  And we can learn a little bit more by considering what is needed to create or destroy a black hole, thus creating or destroying any interior qualities that may or may not exist.

A black hole can only form from certain configurations of matter, particularly aggregates that are above a certain mass and density.  And it can only be destroyed by starving it to death, by depriving it of any new matter, where it will slowly die by evaporating entirely into Hawking radiation, thus destroying anything that was on the inside in the process.  So we can infer that any internal qualities it does have, however inaccessible they may be, can be brought into and out of existence with certain physical processes.

Similarly, we can infer some things about consciousness by observing one’s external behavior including inferring some conditions that can create, modify, or destroy that type of consciousness, but we are unable to know what it’s like to be on the inside of that system once it exists.  We’re only able to know about the inside of our own conscious system, where we are in some sense inside our own black hole with nobody else able to access this perspective.  And I think it is easy enough to imagine that certain configurations of matter simply have an intrinsic, externally inaccessible experiential property, just as certain configurations of matter lead to the creation of a black hole with its own externally inaccessible and qualitatively unknown internal properties.  Despite the fact that we can’t access the black hole’s interior with a strictly external method, to determine its internal properties, this doesn’t mean we should assume that whatever properties may exist inside it are therefore fundamentally non-physical.  Just as we wouldn’t consider alternate dimensions (such as those predicted in M-theory/String-Theory) that we can’t physically access to be non-physical.  Perhaps one or more of these inaccessible dimensions (if they exist) is what accounts for an intrinsic experiential property within matter (though this is entirely speculative and need not be true for the previous points to hold, but it’s an interesting thought nevertheless).

Here’s a relevant quote from the philosopher Galen Strawson, where he outlines what physicalism actually entails:

Real physicalists must accept that at least some ultimates are intrinsically experience-involving. They must at least embrace micropsychism. Given that everything concrete is physical, and that everything physical is constituted out of physical ultimates, and that experience is part of concrete reality, it seems the only reasonable position, more than just an ‘inference to the best explanation’… Micropsychism is not yet panpsychism, for as things stand realistic physicalists can conjecture that only some types of ultimates are intrinsically experiential. But they must allow that panpsychism may be true, and the big step has already been taken with micropsychism, the admission that at least some ultimates must be experiential. ‘And were the inmost essence of things laid open to us’ I think that the idea that some but not all physical ultimates are experiential would look like the idea that some but not all physical ultimates are spatio-temporal (on the assumption that spacetime is indeed a fundamental feature of reality). I would bet a lot against there being such radical heterogeneity at the very bottom of things. In fact (to disagree with my earlier self) it is hard to see why this view would not count as a form of dualism… So now I can say that physicalism, i.e. real physicalism, entails panexperientialism or panpsychism. All physical stuff is energy, in one form or another, and all energy, I trow, is an experience-involving phenomenon. This sounded crazy to me for a long time, but I am quite used to it, now that I know that there is no alternative short of ‘substance dualism’… Real physicalism, realistic physicalism, entails panpsychism, and whatever problems are raised by this fact are problems a real physicalist must face.

— Galen Strawson, Consciousness and Its Place in Nature: Does Physicalism Entail Panpsychism?
While I don’t believe that all matter has a mind per se, because a mind is generally conceived as being a complex information processing structure, I think it is likely that all matter has an experiential quality of some kind, even if most instantiations of it are entirely unrecognizable as what we’d generally consider to be “consciousness” or “mentality”.  I believe that the intuitive gap here is born from the fact that the only minds we are confident exist are those instantiated by a brain, which has the ability to make an incredibly large number of experiential discriminations between various causal relations, thus giving it a capacity that is incredibly complex and not observed anywhere else in nature.  On the other hand, a particle of matter on its own would be hypothesized to have the capacity to make only one or two of these kinds of discriminations, making it unintelligent and thus incapable of brain-like activity.  Once a person accepts that an experiential quality can come in varying degrees from say one experiential “bit” to billions or trillions of “bits” (or more), then we can plausibly see how matter could give rise to systems that have a vast range in their causal power, from rocks that don’t appear to do much at all, to living organisms that have the ability to store countless representations of causal relations (memory) allowing them to behave in increasingly complex ways.  And perhaps best of all, this approach solves the mind-body problem by eliminating the mystery of how fundamentally non-experiential stuff could possibly give rise to experientiality and consciousness.

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

This is the last post I’m going to write for this particular post-series on Predictive Processing (PP).  Here’s the links to parts 1, 2, 3, 4, and 5.  I’ve already explored a bit on how a PP framework can account for folk psychological concepts like beliefs, desires, and emotions, how it accounts for action, language and ontology, knowledge, and also perception, imagination, and reasoning.  In this final post for this series, I’m going to explore consciousness itself and how some theories of consciousness fit very nicely within a PP framework.

Consciousness as Prediction (Predicting The Self)

Earlier in this post-series I explored how PP treats perception (whether online or an offline form like imagination) as simply predictions pertaining to incoming visual information with varying degrees of precision weighting assigned to the resulting prediction error.  In a sense then, consciousness just is prediction.  At the very least, it is a subset of the predictions, likely those that are higher up in the predictive hierarchy.  This is all going to depend on which aspect or level of consciousness we are trying to explain, and as philosophers and cognitive scientists well know, consciousness is difficult to pin down and define in any way.

By consciousness, we could mean any kind of awareness at all, or we could limit this term to only apply to a system that is aware of itself.  Either way we have to be careful here if we’re looking to distinguish between consciousness generally speaking (consciousness in any form, which may be unrecognizable to us) and the unique kind of consciousness that we as human beings experience.  If an ant is conscious, it doesn’t likely have any of the richness that we have in our experience nor is it likely to have self-awareness like we do (even though a dolphin, which has a large neocortex and prefrontal cortex, is far more likely to).  So we have to keep these different levels of consciousness in mind in order to properly assess their being explained by any cognitive framework.

Looking through a PP lens, we can see that what we come to know about the world and our own bodily states is a matter of predictive models pertaining to various inferred causal relations.  These inferred causal relations ultimately stem from bottom-up sensory input.  But when this information is abstracted at higher and higher levels, eventually one can (in principle) get to a point where those higher level models begin to predict the existence of a unified prediction engine.  In other words, a subset of the highest-level predictive models may eventually predict itself as a self.  We might describe this process as the emergence of some level of self-awareness, even if higher levels of self-awareness aren’t possible unless particular kinds of higher level models have been generated.

What kinds of predictions might be involved with this kind of emergence?  Well, we might expect that predictions pertaining to our own autobiographical history, which is largely composed of episodic memories of our past experience, would contribute to this process (e.g. “I remember when I went to that amusement park with my friend Mary, and we both vomited!”).  If we begin to infer what is common or continuous between those memories of past experiences (even if only 1 second in the past), we may discover that there is a form of psychological continuity or identity present.  And if this psychological continuity (this type of causal relation) is coincident with an incredibly stable set of predictions pertaining to (especially internal) bodily states, then an embodied subject or self can plausibly emerge from it.

This emergence of an embodied self is also likely fueled by predictions pertaining to other objects that we infer existing as subjects.  For instance, in order to develop a theory of mind about other people, that is, in order to predict how other people will behave, we can’t simply model their external behavior as we can for something like a rock falling down a hill.  This can work up to a point, but eventually it’s just not good enough as behaviors become more complex.  Animal behavior, most especially that of humans, is far more complex than that of inanimate objects and as such it is going to be far more effective to infer some internal hidden causes for that behavior.  Our predictions would work well if they involved some kind of internal intentionality and goal-directedness operating within any animate object, thereby transforming that object into a subject.  This should be no less true for how we model our own behavior.

If this object that we’ve now inferred to be a subject seems to behave in ways that we see ourselves as behaving (especially if it’s another human being), then we can begin to infer some kind of equivalence despite being separate subjects.  We can begin to infer that they too have beliefs, desires, and emotions, and thus that they have an internal perspective that we can’t directly access just as they aren’t able to access ours.  And we can also see ourselves from a different perspective based on how we see those other subjects from an external perspective.  Since I can’t easily see myself from an external perspective, when I look at others and infer that we are similar kinds of beings, then I can begin to see myself as having both an internal and external side.  I can begin to infer that others see me similar to the way that I see them, thus further adding to my concept of self and the boundaries that define that self.

Multiple meta-cognitive predictions can be inferred based on all of these interactions with others, and from the introspective interactions with our brain’s own models.  Once this happens, a cognitive agent like ourselves may begin to think about thinking and think about being a thinking being, and so on and so forth.  All of these cognitive moves would seem to provide varying degrees of self-hood or self-awareness.  And these can all be thought of as the brain’s best guesses that account for its own behavior.  Either way, it seems that the level of consciousness that is intrinsic to an agent’s experience is going to be dependent on what kinds of higher level models and meta-models are operating within the agent.

Consciousness as Integrated Information

One prominent theory of consciousness is the Integrated Information Theory of Consciousness, otherwise known as IIT.  This theory, initially formulated by Giulio Tononi back in 2004 and which has undergone development ever since, posits that consciousness is ultimately dependent on the degree of information integration that is inherent in the causal properties of some system.  Another way of saying this is that the causal system specified is unified such that every part of the system must be able to affect and be affected by the rest of the system.  If you were to physically isolate one part of a system from the rest of it (and if this part was the least significant to the rest of the system), then the resulting change in the cause-effect structure of the system would quantify the degree of integration.  A large change in the cause-effect structure based on this part’s isolation from the system (that is, by having introduced what is called a minimum partition to the system) would imply a high degree of information integration and vice versa.  And again, a high degree of integration implies a high degree of consciousness.

Notice how this information integration axiom in IIT posits that a cognitive system that is entirely feed-forward will not be conscious.  So if our brain processed incoming sensory information from the bottom up and there was no top-down generative model feeding downward through the system, then IIT would predict that our brain wouldn’t be able to produce consciousness.  PP on the other hand, posits a feedback system (as opposed to feed-forward) where the bottom-up sensory information that flows upward is met with a downward flow of top-down predictions trying to explain away that sensory information.  The brain’s predictions cause a change in the resulting prediction error, and this prediction error serves as feedback to modify the brain’s predictions.  Thus, a cognitive architecture like that suggested by PP is predicted to produce consciousness according to the most fundamental axiom of IIT.

Additionally, PP posits cross-modal sensory features and functionality where the brain integrates (especially lower level) predictions spanning various spatio-temporal scales from different sensory modalities, into a unified whole.  For example, if I am looking at and petting a black cat lying on my lap and hearing it purr, PP posits that my perceptual experience and contextual understanding of that experience are based on having integrated the visual, tactile, and auditory expectations that I’ve associated to constitute such an experience of a “black cat”.  It is going to be contingent on a conjunction of predictions that are occurring simultaneously in order to produce a unified experience rather than a barrage of millions or billions of separate causal relations (let alone those which stem from different sensory modalities) or having millions or billions of separate conscious experiences (which would seem to necessitate separate consciousnesses if they are happening at the same time).

Evolution of Consciousness

Since IIT identifies consciousness with integrated information, it can plausibly account for why it evolved in the first place.  The basic idea here is that a brain that is capable of integrating information is more likely to exploit and understand an environment that has a complex causal structure on multiple time scales than a brain that has informationally isolated modules.  This idea has been tested and confirmed to some degree by artificial life simulations (animats) where adaptation and integration are both simulated.  The organism in these simulations was a Braitenberg-like vehicle that had to move through a maze.  After 60,000 generations of simulated brains evolving through natural selection, it was found that there was a monotonic relationship between their ability to get through the maze and the amount of simulated information integration in their brains.

This increase in adaptation was the result of an effective increase in the number of concepts that the organism could make use of given the limited number of elements and connections possible in its cognitive architecture.  In other words, given a limited number of connections in a causal system (such as a group of neurons), you can pack more functions per element if the level of integration with respect to those connections is high, thus giving an evolutionary advantage to those with higher integration.  Therefore, when all else is equal in terms of neural economy and resources, higher integration gives an organism the ability to take advantage of more regularities in their environment.

From a PP perspective, this makes perfect sense because the complex causal structure of the environment is described as being modeled at many different levels of abstraction and at many different spatio-temporal scales.  All of these modeled causal relations are also described as having a hierarchical structure with models contained within models, and with many associations existing between various models.  These associations between models can be accounted for by a cognitive architecture that re-uses certain sets of neurons in multiple models, so the association is effectively instantiated by some literal degree of neuronal overlap.  And of course, these associations between multiply-leveled predictions allows the brain to exploit (and create!) as many regularities in the environment as possible.  In short, both PP and IIT make a lot of practical sense from an evolutionary perspective.

That’s All Folks!

And this concludes my post-series on the Predictive Processing (PP) framework and how I see it as being applicable to a far more broad account of mentality and brain function, than it is generally assumed to be.  If there’s any takeaways from this post-series, I hope you can at least appreciate the parsimony and explanatory scope of predictive processing and viewing the brain as a creative and highly capable prediction engine.

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.

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

In the previous post which was part 3 in this series (click here for parts 1 and 2) on Predictive Processing (PP), I discussed how the PP framework can be used to adequately account for traditional and scientific notions of knowledge, by treating knowledge as a subset of all the predicted causal relations currently at our brain’s disposal.  This subset of predictions that we tend to call knowledge has the special quality of especially high confidence levels (high Bayesian priors).  Within a scientific context, knowledge tends to have an even stricter definition (and even higher confidence levels) and so we end up with a smaller subset of predictions which have been further verified through comparing them with the inferred predictions of others and by testing them with external means of instrumentation and some agreed upon conventions for analysis.

However, no amount of testing or verification is going to give us direct access to any knowledge per se.  Rather, the creation or discovery of knowledge has to involve the application of some kind of reasoning to explain the causal inputs, and only after this reasoning process can the resulting predicted causal relations be validated to varying degrees by testing it (through raw sensory data, external instruments, etc.).  So getting an adequate account of reasoning within any theory or framework of overall brain function is going to be absolutely crucial and I think that the PP framework is well-suited for the job.  As has already been mentioned throughout this post-series, this framework fundamentally relies on a form of Bayesian inference (or some approximation) which is a type of reasoning.  It is this inferential strategy then, combined with a hierarchical neurological structure for it to work upon, that would allow our knowledge to be created in the first place.

Rules of Inference & Reasoning Based on Hierarchical-Bayesian Prediction Structure, Neuronal Selection, Associations, and Abstraction

While PP tends to focus on perception and action in particular, I’ve mentioned that I see the same general framework as being able to account for not only the folk psychological concepts of beliefs, desires, and emotions, but also that the hierarchical predictive structure it entails should plausibly be able to account for language and ontology and help explain the relationship between the two.  It seems reasonable to me that the associations between all of these hierarchically structured beliefs or predicted causal relations at varying levels of abstraction, can provide a foundation for our reasoning as well, whether intuitive or logical forms of reasoning.

To illustrate some of the importance of associations between beliefs, consider an example like the belief in object permanence (i.e. that objects persist or continue to exist even when I can no longer see them).  This belief of ours has an extremely high prior because our entire life experience has only served to support this prediction in a large number of ways.  This means that it’s become embedded or implicit in a number of other beliefs.  If I didn’t predict that object permanence was a feature of my reality, then an enormous number of everyday tasks would become difficult if not impossible to do because objects would be treated as if they are blinking into and out of existence.

We have a large number of beliefs that require object permanence (and which are thus associated with object permanence), and so it is a more fundamental lower-level prediction (though not as low level as sensory information entering the visual cortex) and we use this lower-level prediction to build upon into any number of higher-level predictions in the overall conceptual/predictive hierarchy.  When I put money in a bank, I expect to be able to spend it even if I can’t see it anymore (such as with a check or debit card).  This is only possible if my money continues to exist even when out of view (regardless of if the money is in a paper/coin or electronic form).  This is just one of many countless everyday tasks that depend on this belief.  So it’s no surprise that this belief (this set of predictions) would have an incredibly high Bayesian prior, and therefore I would treat it as a non-negotiable fact about reality.

On the other hand, when I was a newborn infant, I didn’t have this belief of object permanence (or at best, it was a very weak belief).  Most psychologists estimate that our belief in object permanence isn’t acquired until after several months of brain development and experience.  This would translate to our having a relatively low Bayesian prior for this belief early on in our lives, and only once a person begins to form predictions based on these kinds of recognized causal relations can we begin to increase that prior and perhaps eventually reach a point that results in a subjective experience of a high degree in certainty for this particular belief.  From that point on, we are likely to simply take that belief for granted, no longer questioning it.  The most important thing to note here is that the more associations made between beliefs, the higher their effective weighting (their priors), and thus the higher our confidence in those beliefs becomes.

Neural Implementation, Spontaneous or Random Neural Activity & Generative Model Selection

This all seems pretty reasonable if a neuronal implementation worked to strengthen Bayesian priors as a function of the neuronal/synaptic connectivity (among other factors), where neurons that fire together are more likely to wire together.  And connectivity strength will increase the more often this happens.  On the flip-side, the less often this happens or if it isn’t happening at all then the connectivity is likely to be weakened or non-existent.  So if a concept (or a belief composed of many conceptual relations) is represented by some cluster of interconnected neurons and their activity, then it’s applicability to other concepts increases its chances of not only firing but also increasing the strength of wiring with those other clusters of neurons, thus plausibly increasing the Bayesian priors for the overlapping concept or belief.

Another likely important factor in the Bayesian inferential process, in terms of the brain forming new generative models or predictive hypotheses to test, is the role of spontaneous or random neural activity and neural cluster generation.  This random neural activity could plausibly provide a means for some randomly generated predictions or random changes in the pool of predictive models that our brain is able to select from.  Similar to the role of random mutation in gene pools which allows for differential reproductive rates and relative fitness of offspring, some amount of randomness in neural activity and the generative models that result would allow for improved models to be naturally selected based on those which best minimize prediction error.  The ability to minimize prediction error could be seen as a direct measure of the fitness of the generative model, within this evolutionary landscape.

This idea is related to the late Gerald Edelman’s Theory of Neuronal Group Selection (NGS), also known as Neural Darwinism, which I briefly explored in a post I wrote long ago.  I’ve long believed that this kind of natural selection process is applicable to a number of different domains (aside from genetics), and I think any viable version of PP is going to depend on it to at least some degree.  This random neural activity (and the naturally selected products derived from them) could be thought of as contributing to a steady supply of new generative models to choose from and thus contributing to our overall human creativity as well whether for reasoning and problem solving strategies or simply for artistic expression.

Increasing Abstraction, Language, & New Rules of Inference

This kind of use it or lose it property of brain plasticity combined with dynamic associations between concepts or beliefs and their underlying predictive structure, would allow for the brain to accommodate learning by extracting statistical inferences (at increasing levels of abstraction) as they occur and modifying or eliminating those inferences by changing their hierarchical associative structure as prediction error is encountered.  While some form of Bayesian inference (or an approximation to it) underlies this process, once lower-level inferences about certain causal relations have been made, I believe that new rules of inference can be derived from this basic Bayesian foundation.

To see how this might work, consider how we acquire a skill like learning how to speak and write in some particular language.  The rules of grammar, the syntactic structure and so forth which underlie any particular language are learned through use.  We begin to associate words with certain conceptual structures (see part 2 of this post-series for more details on language and ontology) and then we build up the length and complexity of our linguistic expressions by adding concepts built on higher levels of abstraction.  To maximize the productivity and specificity of our expressions, we also learn more complex rules pertaining to the order in which we speak or write various combinations of words (which varies from language to language).

These grammatical rules can be thought of as just another higher-level abstraction, another higher-level causal relation that we predict will convey more specific information to whomever we are speaking to.  If it doesn’t seem to do so, then we either modify what we have mistakenly inferred to be those grammatical rules, or depending on the context, we may simply assume that the person we’re talking to hasn’t conformed to the language or grammar that my community seems to be using.

Just like with grammar (which provides a kind of logical structure to our language), we can begin to learn new rules of inference built on the same probabilistic predictive bedrock of Bayesian inference.  We can learn some of these rules explicitly by studying logic, induction, deduction, etc., and consciously applying those rules to infer some new piece of knowledge, or we can learn these kinds of rules implicitly based on successful predictions (pertaining to behaviors of varying complexity) that happen to result from stumbling upon this method of processing causal relations within various contexts.  As mentioned earlier, this would be accomplished in part by the natural selection of randomly-generated neural network changes that best reduce the incoming prediction error.

However, language and grammar are interesting examples of an acquired set of rules because they also happen to be the primary tool that we use to learn other rules (along with anything we learn through verbal or written instruction), including (as far as I can tell) various rules of inference.  The logical structure of language (though it need not have an exclusively logical structure), its ability to be used for a number of cognitive short-cuts, and it’s influence on our thought complexity and structure, means that we are likely dependent on it during our reasoning processes as well.

When we perform any kind of conscious reasoning process, we are effectively running various mental simulations where we can intentionally manipulate our various generative models to test new predictions (new models) at varying levels of abstraction, and thus we also manipulate the linguistic structure associated with those generative models as well.  Since I have a lot more to say on reasoning as it relates to PP, including more on intuitive reasoning in particular, I’m going to expand on this further in my next post in this series, part 5.  I’ll also be exploring imagination and memory including how they relate to the processes of reasoning.