Showing posts with label brain plasticity. Show all posts
Showing posts with label brain plasticity. Show all posts

Monday, April 29, 2024

An expanded view of human minds and their reality.

I want to pass on this recent essay by Venkatesh Rao in its entirety, because it has changed my mind on agreeing with Daniel Dennett that the “Hard Problem” of consciousness is a fabrication that doesn’t actually exist. There are so many interesting ideas in this essay that I will be returning to it frequently in the future.  

We Are All Dennettians Now

An homage riff on AI+mind+evolution in honor of Daniel Dennett

The philosopher Daniel Dennett (1942-2024) died last week. Dennett’s contributions to the 1981 book he co-edited with Douglas Hofstadter, The Mind’s I,¹ which I read in 1996 (rather appropriately while doing an undergrad internship at the Center for AI and Robotics in Bangalore), helped shape a lot of my early philosophical development. A few years later (around 1999 I think), I closely read his trollishly titled 1991 magnum opus, Consciousness Explained (alongside Steven Pinker’s similar volume How the Mind Works), and that ended up shaping a lot of my development as an engineer. Consciousness Explained is effectively a detailed neuro-realistic speculative engineering model of the architecture of the brain in a pseudo-code like idiom. I stopped following his work closely at that point, since my tastes took me in other directions, but I did take care to keep him on my radar loosely.

So in his honor, I’d like to (rather chaotically) riff on the interplay of the three big topics that form the through-lines of his life and work: AI, the philosophy of mind, and Darwinism. Long before we all turned into philosophers of AI overnight with the launch of ChatGPT, he defined what that even means.

When I say Dennett’s views shaped mine, I don’t mean I necessarily agreed with them. Arguably, your early philosophical development is not shaped by discovering thinkers you agree with. That’s for later-life refinements (Hannah Arendt, whom I first read only a few years ago, is probably the most influential agree-with philosopher for me). Your early development is shaped by discovering philosophers you disagree with.

But any old disagreement will not shape your thinking. I read Ayn Rand too (if you want to generously call her a philosopher) around the same time I discovered Dennett, and while I disagreed with her too, she basically had no effect on my thinking. I found her work to be too puerile to argue with. But Dennett — disagreeing with him forced me to grow, because it took serious work over years to decades — some of it still ongoing — to figure out how and why I disagreed. It was philosophical weight training. The work of disagreeing with Dennett led me to other contemporary philosophers of mind like David Chalmers and Ned Block, and various other more esoteric bunnytrails. This was all a quarter century ago, but by the time I exited what I think of as the path-dependent phase of my philosophical development circa 2003, my thinking bore indelible imprints of Dennett’s influence.

I think Dennett was right about nearly all the details of everything he touched, and also right (and more crucially, tasteful) in his choices of details to focus on as being illuminating and significant. This is why he was able to provide elegant philosophical accounts of various kinds of phenomenology that elevated the corresponding discourses in AI, psychology, neuroscience, and biology. His work made him a sort of patron philosopher of a variety of youngish scientific disciplines that lacked robust philosophical traditions of their own. It also made him a vastly more relevant philosopher than most of his peers in the philosophy world, who tend, through some mix of insecurity, lack of courage, and illiteracy, to stay away from the dirty details of technological modernity in their philosophizing (and therefore cut rather sorry figures when they attempt to weigh in on philosophy-of-technology issues with cartoon thought experiments about trolleys or drowning children). Even the few who came close, like John Searle, could rarely match Dennett’s mastery of vast oceans of modern techno-phenomenological detail, even if they tended to do better with clever thought experiments. As far as I am aware, Dennett has no clever but misleading Chinese Rooms or Trolley Problems to his credit, which to my mind makes him a superior rather than inferior philosopher.

I suspect he paid a cost for his wide-ranging, ecumenical curiosities in his home discipline. Academic philosophers like to speak in a precise code about the simplest possible things, to say what they believe to be the most robust things they can. Dennett on the other hand talked in common language about the most complex things the human mind has ever attempted to grasp. The fact that he got his hands (and mind!) dirty with vast amounts of technical detail, and dealt in facts with short half-lives from fast-evolving fields, and wrote in a style accessible to any intelligent reader willing to pay attention, made him barely recognizable as a philosopher at all. But despite the cosmetic similarities, it would be a serious mistake to class him with science popularizers or TED/television scientists with a flair for spectacle at the expense of substance.

Though he had a habit of being uncannily right about a lot of the details, I believe Dennett was almost certainly wrong about a few critical fundamental things. We’ll get to what and why later, but the big point to acknowledge is that if he was indeed wrong (and to his credit, I am not yet 100% sure he was), he was wrong in ways that forced even his opponents to elevate their games. He was as much a patron philosopher (or troll or bugbear) to his philosophical rivals as to the scientists of the fields he adopted. You could not even be an opponent of Dennett except in Dennettian ways. To disagree with the premises of Strong AI or Dennett’s theory of mind is to disagree in Dennettian ways.

If I were to caricature how I fit in the Dennettian universe, I suspect I’d be closest to what he called a “mysterian” (though I don’t think the term originated with him). Despite mysterian being something of a dismissive slur, it does point squarely at the core of why his opponents disagree with him, and the parts of their philosophies they must work to harden and make rigorous, to withstand the acid forces of the peculiarly Dennetian mode of scrutiny I want to talk about here.

So to adapt the line used by Milton Friedman to describe Keynes: We are all Dennettians now.

Let’s try and unpack what that means.

Mysterianism

As I said, in Dennettian terms, I am a “mysterian.” At a big commitments level, mysterianism is the polar opposite of the position Dennett consistently argued across his work, a version of what we generally call a “Strong AI” position. But at the detailed level, there are no serious disagreements. Mysterians and Strong AI people agree about most of the details of how the mind works. They just put the overall picture together differently because mysterians want to accommodate certain currently mysterious things that Strong AI people typically reject as either meaningless noise or shallow confusions/illusions.

Dennett’s version of Strong AI was both more robustly constructed than the sophomoric versions one typically encounters, and more broadly applied: beyond AI to human brains and seemingly intelligent processes like evolution. Most importantly, it was actually interesting. Reading his accounts of minds and computers, you do not come away with the vague suspicion of a non-neurotypical succumbing to the typical-mind fallacy and describing the inner life of a robot or philosophical zombie as “truth.” From his writing, it sounds like he had a fairly typical inner-life experience, so why did he seem to deny the apparent ineffable essence of it? Why didn’t he try to eff that essence the way Chalmers, for instance, does? Why did he seemingly dismiss it as irrelevant, unreal, or both?

To be a mysterian in Dennettian terms is to take ineffable, vitalist essences seriously. With AIs and minds, it means taking the hard problem of consciousness seriously. With evolution, it means believing that Darwinism is not the whole story. Dennett tended to use the term as a dismissive slur, but many, (re)claim it as a term of approbation, and I count myself among them.

To be a rigorous mysterian, as opposed to one of the sillier sorts Dennett liked to stoop to conquer (naive dualists, intelligent-designers, theological literalists, overconfident mystics…), you have to take vitalist essences “seriously but not literally.” My version of doing that is to treat my vitalist inclinations as placeholder pointers to things that lurk in the dank, ungrokked margins of the thinkable, just beyond the reach of my conceptualizing mind. Things I suspect exist by the vague shapes of the barely sensed holes they leave in my ideas. In pursuit of such things, I happily traffic in literary probing of Labatutian/Lovecraftian/Ballardian varieties, self-consciously magical thinking, junk from various pre-paradigmatic alchemical thought spaces, constructs that uncannily resemble astrology, and so on. I suppose it’s a sort of intuitive-ironic cognitive kayfabe for the most part, but it’s not entirely so.

So for example, when I talk of elan vital, as I frequently do in this newsletter, I don’t mean to imply I believe in some sort of magical fluid flowing through living things or a Gaian planetary consciousness. Nor do I mean the sort of overwrought continental metaphysics of time and subjectivity associated with Henri Bergson (which made him the darling of modernist literary types and an object of contempt to Einstein). I simply mean I suspect there are invisible things going on in the experience and phenomenology of life that are currently beyond my ability to see, model, and talk about using recognizably rational concepts, and I’d rather talk about them as best I can with irrational concepts than pretend they don’t exist.

Or to take another example, when I say that “Darwin is not the whole story,” I don’t mean I believe in intelligent design or a creator god (I’m at least as strong an atheist as Dennett was). I mean that Darwinian principles of evolution constrain but do not determine the nature of nature, and we don’t yet fully grok what completes the picture except perhaps in hand-wavy magical-thinking ways. To fully determine what happens, you need to add more elements. For example, you can add ideas like those of Stuart Kauffman and other complexity theorists. You could add elements of what Maturana and Varela called autopoiesis. Or it might be none of these candidate hole-filling ideas, but something to be dreamt up years in the future. Or never. But just because there are only unsatisfactory candidate ways for talking about stuff doesn’t mean you should conclude the stuff doesn’t exist.

In all such cases, there are more things present in phenomenology I can access than I can talk about using terms of reference that would be considered legitimate by everybody. This is neither known-unknowns (which are holes with shapes defined by concepts that seem rational), nor unknown-unknown (which have not yet appeared in your senses, and therefore, to apply a Gilbert Ryle principle, cannot be in your mind).

These are things that we might call magically known. Like chemistry was magically known through alchemy. For phenomenology to be worth magically knowing, the way-of-knowing must offer interesting agency, even if it doesn’t hang together conceptually.

Dennett seemed to mostly fiercely resist and reject such impulses. He genuinely seemed to think that belief in (say) the hard problem of consciousness was some sort of semantic confusion. Unlike say B. F. Skinner, whom critics accused of only pretending to not believe in inner processes, Dennett seemed to actually disbelieve in them.

Dennett seemed to disregard a cousin to the principle that absence of evidence is not evidence of absence: Presence of magical conceptualizations does not mean absence of phenomenology. A bad pointer does not disprove the existence of what it points to. This sort of error is easy to avoid in most cases. Lightning is obviously real even if some people seem to account for it in terms of Indra wielding his vajra. But when we try to talk of things that are on the phenomenological margins, barely within the grasp of sensory awareness, or worse, potentially exist as incommunicable but universal subjective phenomenology (such as the experience of the color “blue”), things get tricky.

Dennett was a successor of sorts to philosophers like Gilbert Ryle, and psychologists like B. F. Skinner. In evolutionary philosophy, his thinking aligned with people like Richard Dawkins and Steven Pinker, and against Noam Chomsky (often classified as a mysterian, though I think the unreasonable effectiveness of LLMs kinda vindicates to a degree Chomsky’s notions of an ineffable more-than-Darwin essence around universal grammars that we don’t yet understand).

I personally find it interesting to poke at why Dennett took the positions he took, given that he was contemplating the same phenomenological data and low-to-mid-level conceptual categories as the rest of us (indeed, he supplied much of it for the rest of us). One way to get at it is to ask: Was Dennett a phenomenologist? Are the limits of his ideas are the limits of phenomenology?

I think the answers are yes and yes, but he wasn’t a traditional sort of phenomenologist, and he didn’t encounter the more familiar sorts of limits.

The Limits of Phenomenology

Let’s talk regular phenomenology first, before tackling what I think was Dennett’s version.

I think of phenomenology, as a working philosophical method, to be something like a conceited form of empiricism that aims to get away from any kind of conceptually mediated seeing.

When you begin to inquire into a complex question with any sort of fundamentally empirical approach, your philosophy can only be as good as a) the things you know now through your (potentially technologically augmented) senses and b) the ways in which you know those things.

The conceit of phenomenology begins with trying to “unknow” what is known to be known, and contemplate the resulting presumed “pure” experiences “directly.” There are various flavors of this: Husserlian bracketing in the Western tradition, Zen-like “beginner mind” practices, Vipassana style recursive examination of mental experiences, and so on. Some flavors apply only to sense-observations of external phenomena, others apply only to subjective introspection, and some apply to both. Given the current somewhat faddish uptick in Eastern-flavored disciplines of interiority, it is important to take note of the fact that the phenomenological attitude is not necessarily inward-oriented. For example, the 19th century quest to measure a tenth of a second, and factor out the “human equation” in astronomical observations, was a massive project in Western phenomenology. The abstract thought experiments with notional clocks in the theory of relativity began with the phenomenology of real clocks.

In “doing” phenomenology, you are assuming that you know what you know relatively completely (or can come to know it), and have a reliable procedure for either unknowing it, or systematically alloying it with skeptical doubt, to destabilize unreliable perceptions it might be contributing to. Such destabilizability of your default, familiar way of knowing, in pursuit of a more-perfect unknowing, is in many ways the essence of rationality and objectivity. It is the (usually undeclared) starting posture for doing “science,” among other things.

Crucially, for our purposes in this essay, you do not make a careful distinction between things you know in a rational way and things you know in a magical or mysterian way, but effectively assume that only the former matter; that the latter can be trivially brushed aside as noise signifying nothing that needs unknowing. I think the reverse is true. It is harder, to the point of near impossible, to root out magical ideas from your perceptions, and they signify the most important things you know. More to the point, it is not clear that trying to unknow things, especially magical things, is in fact a good idea, or that unknowing is clarifying rather than blinding. But phenomenology is committed to trying. This has consequences for “phenomenological projects” of any sort, be they Husserlian or Theravadan in spirit.

A relatively crude example: “life” becomes much less ineffable (and depending on your standards, possibly entirely drained of mystery) once you view it through the lens of DNA. Not only do you see new things through new tools, you see phenomenology you could already see, such as Mendelian inheritance, in a fundamentally different way that feels phenomenologically “deeper” when in fact it relies on more conceptual scaffolding, more things that are invisible to most of us, and requires instruments with elaborate theories attached to even render intelligible. You do not see “ATCG” sequences when contemplating a pea flower. You could retreat up the toolchain and turn your attention to how instruments construct the “idea of DNA” but to me that feels like a usually futile yak shave. The better thing to do is ask why a more indirect way of knowing somehow seems to perceive more clearly than more direct ways.

It is obviously hard to “unsee” knowledge of DNA today when contemplating the nature of life. But it would have been even harder to recognize that something “DNA shaped” was missing in say 1850, regardless of your phenomenological skills, by unknowing things you knew then. In fact, clearing away magical ways of knowing might have swept away critical clues.

To become aware, as Mendel did, that there was a hidden order to inheritance in pea flowers, takes a leap of imagination that cannot be purely phenomenological. To suspect in 1943, as Schrodinger did, the existence of “aperiodic covalent bonded crystals” at the root of life, and point the way to DNA, takes a blend of seeing and knowing that is greater than either. Magical knowing is pathfinder-knowing that connects what we know and can see to what we could know and see. It is the bootstrapping process of the mind.

Mendel and Schrodinger “saw” DNA before it was discovered, in terms of reference that would have been considered “rational” in their own time, but this has not always been the case. Newton, famously, had a lot of magical thinking going on in his successful quest for a theory of gravity. Kepler was a numerologist. Leibniz was ball of mad ideas. One of Newton’s successful bits of thinking, the idea of “particles” of light, which faced off against Huygens’ “waves,” has still not exited the magical realm. The jury is still out in our time about whether quantized fields are phenomenologically “real” or merely a convenient mnemonic-metaphoric motif for some unexpected structure in some unreasonably effective math.

Arguably, none of these thinkers was a phenomenologist, though all had a disciplined empirical streak in their thinking. The history of their ideas suggests that phenomenology is no panacea for philosophical troubles with unruly conceptual universes that refuse to be meekly and rationally “bracketed” away. There is no systematic and magic-free way to march from current truths to better ones via phenomenological disciplines.

The fatal conceit of naive phenomenology (which Paul Feyerabend spotted) is the idea that there is privileged reliable (or meta-reliable) “technique” of relating to your sense experiences, independent of the concepts you hold, whether that “technique” is Husserlian bracketing or vipassana. Understood this way, theories of reality are not that different from physical instruments that extend our senses. Experiment and theory don’t always expose each other’s weaknesses. Sometimes they mutually reinforce them.

In fact, I would go so far as to suggest—and I suspect Dennett would have agreed—that there is no such thing as phenomenology per se. All we ever see is the most invisible of our theories (rational and magical), projected via our senses and instruments (which shape, and are shaped by, those theories), onto the seemingly underdetermined aspects of the universe. There are only incomplete ways of knowing and seeing within which ideas and experiences are inextricably intertwined. No phenomenological method can consistently outperform methodological anarchy.

To deny this is to be a traditional phenomenologist, striving to procedurally separate the realm of ideas and concepts from the realm of putatively unfactored and “directly perceived” (a favorite phrase of meditators) “real” experiences.

Husserlian bracketing — “suspending trust in the objectivity of the world” — is fine in theory, but not so easy in practice. How do you know that you’re setting aside preconceived notions, judgments, and biases and attending to a phenomenon as it truly is? How do you set aside the unconscious “theory” that the Sun revolves around the Earth, and open your mind to the possibility that it’s the other way around? How do you “see” DNA-shaped holes in current ways of seeing, especially if they currently manifest as strange demons that you might sweep away in a spasm of over-eager epistemic hygiene? How do you relate, as a phenomenologist, to intrinsically conceptual things like electrons and positrons that only exist behind layers of mathematics describing experimental data processed through layers of instrumentation conceived by existing theories? If you can’t check the math yourself, how can you trust that the light bulb turning on is powered by those “electrons” tracing arcs through cloud chambers?

In practice, we know how such shifts actually came about. Not because philosophers meditated dispassionately on the “phenomenology” with free minds seeing reality as it “truly is,” but because astronomers and biologists with heads full of weird magical notions looked through telescopes and microscopes, maintained careful notes of detailed measurements, informed by those weird magical theories, and tried to account for discrepancies. Tycho Brahe, for instance, who provided the data that dethroned Ptolemy, believed in some sort of Frankenstein helio-geo-centric Ptolemy++ theory. Instead of explaining the discrepancies, as Kepler did later, Brahe attempted to explain them away using terms of reference he was attached to. He failed to resolve the tension. But he paved the way to Kepler resolving that particular tension (who of course introduced new ones, while lost in his own magical thinking about platonic solids). Formally phenomenological postures were not just absent from the story, but would have arguably retarded it by being too methodologically conservative.

Phenomenology, in other words, is something of a procedural conceit. An uncritical trust in self-certifying ways of seeing based entirely on how compelling they seem to the seer. The self-certification follows some sort of seemingly rational procedure (which might be mystical but still rational in the sense of being coherent and disciplined and internally consistent) but ultimately derives its authority from the intuitive certainties and suspicions of the perceiving subject. Phenomenological procedures are a kind of rule-by-law for governing sense experience in a laissez-faire way, rather than the “objective” rule-of-law they are often presented as. Phenomenology is to empiricism as “socialism with Chinese characteristics” is to liberal democracy.

This is not to say phenomenology is hopelessly unreliable or useless. All methodologies have their conceits, which manifest as blindspots. With phenomenology, the blindspot manifests as an insistence on non-magicality. The phenomenologist fiercely rejects the Cartesian theater and the varied ghosts-in-machines that dance there. The meditator insists he is “directly perceiving” reality in a reproducible way, no magic necessary. I do not doubt that these convictions are utterly compelling to those who hold them; as compelling as the incommunicable reality of perceiving “blue” is to everybody. I have no particular argument with such insistence. What I actually have a problem with is the delegitimization of magical thinking in the process, which I suspect to be essential for progress.

My own solution is to simply add magical thinking back into the picture for my own use, without attempting to defend that choice, and accepting the consequences.

For example, I take Myers-Briggs and the Enneagram seriously (but not literally!). I believe in the hard problem of consciousness, and therefore think “upload” and “simulationism” ideas are not-even-wrong. I don’t believe in Gods or AGIs, and therefore don’t see the point of Pascal’s wager type contortions to avoid heaven/hell or future-simulated-torture scenarios. In each case my commitments rely on chains of thought that are at least partly magical thinking, and decidedly non-phenomenological, which has various social consequences in various places. I don’t attempt to justify any of it because I think all schemes of justification, whether they are labeled “science” or something else, rest on traditional phenomenology and its limits.

Does this mean solipsism is the best we can hope for? This is where we get back to Dennett.

Dennett, to his credit, I don’t think he was a traditional phenomenologist, and he mostly avoided all the traps I’ve pointed out, including the trap of solipsism. Nor was he what one might call a “phenomenologist of language” like most modern analytical philosophers in the West. He was much too interested in technological modernity (and the limits of thought it has been exposing for a century) to be content with such a shrinking, traditionalist philosophical range.

But he was a phenomenologist in the broader sense of rejecting the possible reality of things that currently lack coherent non-magical modes of apprehension.

So how did he operate if not in traditional phenomenological ways?

Demiurge Phenomenology

I believe Dennett was what we might call a demiurge phenomenologist, which is a sort of late modernist version of traditional phenomenology. It will take a bit of work to explain what I mean by that.

I can’t recall if he ever said something like this (I’m definitely not a completist with his work and have only read a fraction of his voluminous output), but I suspect Dennett believed that the human experience of “mind” is itself subject to evolutionary processes (think Jaynes and bicameral mind theories for example — I seem to recall him saying something approving about that in an interview somewhere). He sought to construct philosophy in ways that did not derive authority from an absolute notion of the experience of mind. He tried to do relativity theory for minds, but without descending into solipsism.

It is easiest to appreciate this point by starting with body experience. For example, we are evolved from creatures with tails, but we do not currently possess tails. We possess vestigial “tail bones” and presumably bits of DNA relevant to tails, but we cannot know what it is like to have a tail (or in the spirit of mysterian philosopher Thomas Nagel’s What is it Like to Be a Bat provocation, which I first read in The Mind’s I, what it is like for a tailed creature to have a tail).

We do catch tantalizing Lovecraftian-Ballardian glimpses of our genetic heritage though. For example, the gasping reflex and shot of alertness that accompanies being dunked in water (the mammalian dive reflex) is a remnant of a more aquatic evolutionary past that far predates our primate mode of existence. Now apply that to the experience of “mind.”

Why does Jaynes’ bicameral mind theory sound so fundamentally crackpot to modern minds? It could be that the notion is actually crackpot, but you cannot easily dismiss the idea that it’s actually a well-posed notion that only appears crackpot because we are not currently possessed of bicameral mind-experiences (modulo cognitive marginalia like tulpas and internal family systems — one of my attention/taste biases is to index strongly on typical rather than rare mental experiences; I believe the significance of the latter is highly overstated due to the personal significance they acquire in individual lives).

I hope it is obvious why the possibility that the experience of mind is subject to evolution is fatal to traditional phenomenology. If despite all the sophistication of your cognitive toolchain (bracketing, jhanas, ketamine, whatever), it turns out that you’re only exploring the limits of the evolutionarily transient and arbitrary “variety of mind” that we happen to experience, what does that say about the reliability of the resulting supposedly objective or “direct” perceptions of reality itself that such a mind mediates?

This, by the way, is a problem that evolutionary terms of reference make elegantly obvious, but you can get here in other ways. Darwinian evolution is convenient scaffolding to get there (and the one I think Dennett used), but ultimately dispensable. But however you get there, the possibility that experiences of mind are relative to contingent and arbitrary evolutionary circumstances is fatal to the conceits of traditional phenomenology. It reduces traditional phenomenology in status to any old sort of Cartesian or Platonic philosophizing with made-up bullshit schemas. You might as well make 2x2s all day like I sometimes do.

The Eastern response to this quandary has traditionally been rather defeatist — abandoning the project of trying to know reality entirely. Buddhist and Advaita philosophies in particular, tend to dispense with “objective reality” as an ontologically meaningful characterization of anything. There is only nothing. Or only the perceiving subject. Everything else is maya-moh, a sentimental attachment to the ephemeral unreal. Snap out of it.


I suspect Western philosophy was starting to head that way in the 16th century (through the Spinoza-vs-Leibniz shadowboxing years), but was luckily steered down a less defeatist path to a somewhat uneasy detente between a sort of “probationary reality” accessed through technologically augmented senses, and a subjectivity resolutely bound to that probationary reality via the conceits of traditional phenomenology. This is a long-winded way of saying “science happened” to Western philosophy.

I think that detente is breaking down. One sign is the growing popularity of the relatively pedestrian metaphysics of physicists like Donald Hoffman (leading to a certain amount of unseemly glee among partisans of Eastern philosophies — “omigod you think quantum mechanics shows reality is an illusion? Welcome to advaita lol”).

But despite these marginally interesting conversations, and whether you get there via Husserl, Hoffman, or vipassana, we’re no closer to resolving what we might call the fundamental paradox of phenomenology. If our experience of mind is contingent, how can any notion of justifiable absolute knowledge be sustained? We are essentially stopped clocks trying to tell the time.

Dennett, I think favored one sort of answer: That the experience of mind was too untrustworthy and transient to build on, but that mind’s experience of mathematics was both trustworthy and absolute. Bicameral or monocameral, dolphin-brain or primate-brain, AI-brain or Hoffman-optimal ontological apparatus, one thing that is certain is that a prime number is a prime number in all ways that reality (probationary or not, illusory or not) collides with minds (typical or atypical, bursting with exotic qualia or full of trash qualia). Even the 17 and 13 year cicadas agree. Prime numbers constitute a fixed point in all the ways mind-like things have experience-like things in relation to reality-like things, regardless of whether minds, experiences, and reality are real. Prime numbers are like a motif that shows up in multiple unreliable dreams. If you’re going to build up a philosophy of being, you should only use things like prime numbers.

This is not just the most charitable interpretation of Dennett’s philosophy, but the most interesting and powerful one. It’s not that he thought of the mysterian weakness for ineffable experiences as being particularly “illusory”. As far as he was concerned, you could dismiss the “experience of mind” in its entirety as irrelevant philosophically. Even the idea that it has an epiphenomenal reality need not be seriously entertained because the thing that wants to entertain that idea is not to be trusted.

You see signs of this approach in a lot of his writing. In his collaborative enquires with Hofstadter, in his fundamentally algorithmic-mathematical account of evolution, in his seemingly perverse stances in debates both with reputable philosophers of mind and disreputable intelligent designers. As far as he was concerned, anyone who chose to build any theory of anything on the basis of anything other than mathematical constancy was trusting the experience of mind to an unjustifiable degree.

Again, I don’t know if he ever said as much explicitly (he probably did), but I suspect he had a basic metaphysics similar to that of another simpatico thinker on such matters, Roger Penrose: as a triad of physical/mental/platonic-mathematical worlds projecting on to each other in a strange loop. But unlike Penrose, who took the three realms to be equally real (or unreal) and entangled in an eternal dance of paradox, he chose to build almost entirely on the Platonic-mathematical vertex, with guarded phenomenological forays to the physical world, and strict avoidance of the mental world as a matter of epistemological hygiene.


The guarded phenomenological forays, unlike those of traditional phenomenologists, were governed by an allow list rather than a block list. Instead of trying to “block out” suspect conceptual commitments with bracketing or meditative discipline, he made sure to only work with allowable concepts and percepts that seemed to have some sort of mathematical bones to them. So Turing machines, algorithms, information theory, and the like, all made it into his thinking in load-bearing ways. Everything else was at best narrative flavor or useful communication metaphors. People who took anything else seriously were guilty of deep procedural illusions rather than shallow intellectual confusions.

If you think about it, his accounts of AI, evolution, and the human mind make a lot more sense if you see them as outcomes of philosophical construction processes governed by one very simple rule: Only use a building block if it looks mathematically real.

Regardless of what you believe about the reality of things other than mathematically underwritten ones, this is an intellectually powerful move. It is a kind of computational constructionism applied to philosophical inquiry, similar to what Wolfram does with physics on automata or hypergraphs, or what Grothendieck did with mathematics.

It is also far harder to do, because philosophy aims and claims to speak more broadly and deeply than either physics or mathematics.

I think Dennett landed where he did, philosophically, because he was essentially trying to rebuild the universe out of a very narrow admissible subset of the phenomenological experience of it. Mysterian musings didn’t make it in because they could not ride allowable percepts and concepts into the set of allowable construction materials.

In other words, he practiced demiurge phenomenology. Natural philosophy as an elaborate construction practice based on self-given rules of construction.

In adopting such an approach he was ahead of his time. We’re on the cusp of being able to literally do what he tried to do with words — build phenomenologically immersive virtual realities out of computational matter that seem to be defined by nothing more than mathematical absolutes, and have almost no connection even to physical reality, thanks to the seeming buffering universality of Turing-equivalent computation.

In that almost, I think, lies the key to my fundamental disagreement with Dennett, and my willingness to wander in magical realms of thought where mathematically sure-footed angels fear to tread. There are… phenomenological gaps between mathematical reconstructions of reality by energetic demiurges (whether they work with powerful arguments or VR headsets) and reality itself.

The biggest one, in my opinion, is the experience of time, which seems to oddly resist computational mathematization (though Stephen Wolfram claims to have one… but then he claims to have a lot of things). In an indirect way, disagreeing with Dennett at age 20 led me to my lifelong fascination with the philosophy of time.

Where to Next?

It is something of a cliche that over the last century or two, philosophy has gradually and reluctantly retreated from an increasing number of the domains it once claimed as its own, as scientific and technological advances rendered ungrounded philosophical ideas somewhere between moot and ridiculous. Bergson retreating in the face of the Einsteinian assault, ceding the question of the nature of time to physics, is probably as good a historical marker of the culmination of the process as any.

I would characterize Dennett as a late modernist philosopher in relation to this cliche. Unlike many philosophers, who simply gave up on trying to provide useful accounts of things that science and technology were beginning to describe in inexorably more powerful ways, he brought enormous energy to the task of simply keeping up. His methods were traditional, but his aim was radical: Instead of trying to provide accounts of things, he tried to provide constructions of things, aiming to arrive at a sense of the real through philosophical construction with admissible materials. He was something like Brouwer in mathematics, trying to do away with suspect building blocks to get to desirable places only using approved methods.

This actually worked very well, as far as it went. For example, I think his philosophy of mind was almost entirely correct as far as the mechanics of cognition go, and the findings of modern AI vindicate a lot of the specifics. For example, his idea of a “multiple drafts” model of cognition (where one part of the brain generates a lot of behavioral options in a bottom-up, anarchic way, and another part chooses a behavior from among them) is basically broadly correct, not just as a description of how the brain works, but of how things like LLMs work. But unlike many other so-called philosophers of AI he disagreed with, like Nick Bostrom, Dennett’s views managed to be provocative without being simplistic, opinionated without being dogmatic. He appeared to have a Strong AI stance similar to many people I disagree with, but unlike most of those people, I found his views worth understanding with some care, and hard to dismiss as wrong, let alone not-even-wrong.

I like to think he died believing his philosophies — of mind, AI, and Darwinism — to be on the cusp of a triumphant redemption. There are worse ways to go than believing your ideas have been thoroughly vindicated. And indeed, there was a lot Dennett got right. RIP.

Where do we go next with Dennettian questions about AI, minds, and evolution?

Oddly enough, I think Dennett himself pointed the way: demiurge phenomenology is the way. We just need to get more creative with it, and admit magical thinking into the process.

Dennett, I think, approached his questions the way some mathematicians originally approached Euclid’s fifth postulate: Discard it and try to either do without, or derive it from the other postulates. That led him to certain sorts of demiurgical constructions of AI, mind, and evolution.

There is another, equally valid way. Just as other mathematicians replaced the fifth postulates with alternatives and ended up with consistent non-Euclidean geometries, I think we could entertain different mysterian postulates and end up with a consistent non-Dennettian metaphysics of AI, mind, and evolution. You’d proceed by trying to do your own demiurgical constructing of a reality. An alternate reality.

For instance, what happens if you simply assume that there is human “mind stuff” that ends with death and cannot be uploaded or transferred to other matter, and that can never emerge in silico. You don’t have to try accounting for it (no need to mess with speculations about the pineal gland like Descartes, or worry about microtubules and sub-Planck-length phenomena like Penrose). You could just assume that consciousness is a thing like space or time, and run with the idea and see where you land and what sort of consistent metaphysical geometries are possible. This is in fact what certain philosophers of mind like Ned Block do.

The procedure can be extended to other questions as well. For instance, if you think Darwin is not the whole story with evolution, you could simply assume there are additional mathematical selection factors having to do with fractals or prime numbers, and go look for them, as the Santa Fe biologists have done. Start simple and stupid, for example, by applying a rule that “evolution avoids rectangles” or “evolution cannot get to wheels made entirely of grown organic body parts” and see where you land (for the latter, note that the example in Dark Materials trilogy cheats — that’s an assembled wheel, not an evolved one).

But all these procedures follow the basic Dennettian approach of demiurgical constructionist phenomenology. Start with your experiences. Let in an allow-list of percepts as concepts. Add an arbitrarily constructed magical suspicion or two. Let your computer build out the entailments of those starter conditions. See what sort of realities you can conjure into being. Maybe one of them will be more real than your current experience of reality. That would be progress. Perhaps progress only you can experience, but still, progress.

Would such near-solipsistic activities constitute a collective philosophical search for truth? I don’t know. But then, I don’t know if we have ever been on a coherent collective philosophical search for truth. All we’ve ever had is more or less satisfying descriptions of the primal mystery of our own individual existence.

Why is there something, rather than nothing, it is like, to be me?

Ultimately, Dennett did not seem to find that question to be either interesting or serious. But he pointed the way for me to start figuring out why I do. And that’s why I too am a Dennettian.


footnote  1
I found the book in my uncle’s library, and the only reason I picked it up was because I recognized Hofstadter’s name because Godel, Escher, Bach had recently been recommended to me. I think it’s one of the happy accidents of my life that I read The Mind’s I before I read Hofstadter’s Godel, Escher, Bach. I think that accident of path-dependence may have made me a truly philosophical engineer as opposed to just an engineer with a side interest in philosophy. Hofstadter is of course much better known and familiar in the engineering world, and reading him is something of a rite of passage in the education of the more sentimental sorts of engineers. But Hofstadter’s ideas were mostly entertaining and informative for me, in the mode of popular science, rather than impactful. Dennett on the other hand, was impactful.




Wednesday, March 27, 2024

Brain changes over our lifetime.

This video from The Economist is one of the best I have seen for a popular audience. Hopefully the basic facts presented are slowly seeping throughout our culture..

Wednesday, March 20, 2024

Fundamentally changing the nature of war.

I generally try to keep a distance from 'the real world' and apocalyptic visions of what AI might do, but I decided to pass on some clips from this technology essay in The Wall Street Journal that makes some very plausible predictions about the future of armed conflicts between political entities:

The future of warfare won’t be decided by weapons systems but by systems of weapons, and those systems will cost less. Many of them already exist, whether they’re the Shahed drones attacking shipping in the Gulf of Aden or the Switchblade drones destroying Russian tanks in the Donbas or smart seaborne mines around Taiwan. What doesn’t yet exist are the AI-directed systems that will allow a nation to take unmanned warfare to scale. But they’re coming.

At its core, AI is a technology based on pattern recognition. In military theory, the interplay between pattern recognition and decision-making is known as the OODA loop— observe, orient, decide, act. The OODA loop theory, developed in the 1950s by Air Force fighter pilot John Boyd, contends that the side in a conflict that can move through its OODA loop fastest will possess a decisive battlefield advantage.

For example, of the more than 150 drone attacks on U.S. forces since the Oct. 7 attacks, in all but one case the OODA loop used by our forces was sufficient to subvert the attack. Our warships and bases were able to observe the incoming drones, orient against the threat, decide to launch countermeasures and then act. Deployed in AI-directed swarms, however, the same drones could overwhelm any human-directed OODA loop. It’s impossible to launch thousands of autonomous drones piloted by individuals, but the computational capacity of AI makes such swarms a possibility.

This will transform warfare. The race won’t be for the best platforms but for the best AI directing those platforms. It’s a war of OODA loops, swarm versus swarm. The winning side will be the one that’s developed the AI-based decision-making that can outpace their adversary. Warfare is headed toward a brain-on-brain conflict.

The Department of Defense is already researching a “brain-computer interface,” which is a direct communications pathway between the brain and an AI. A recent study by the RAND Corporation examining how such an interface could “support human- machine decision-making” raised the myriad ethical concerns that exist when humans become the weakest link in the wartime decision-making chain. To avoid a nightmare future with battlefields populated by fully autonomous killer robots, the U.S. has insisted that a human decision maker must always remain in the loop before any AI-based system might conduct a lethal strike.

But will our adversaries show similar restraint? Or would they be willing to remove the human to gain an edge on the battlefield? The first battles in this new age of warfare are only now being fought. It’s easy to imagine a future, however, where navies will cease to operate as fleets and will become schools of unmanned surface and submersible vessels, where air forces will stand down their squadrons and stand up their swarms, and where a conquering army will appear less like Alexander’s soldiers and more like a robotic infestation.

Much like the nuclear arms race of the last century, the AI arms race will define this current one. Whoever wins will possess a profound military advantage. Make no mistake, if placed in authoritarian hands, AI dominance will become a tool of conquest, just as Alexander expanded his empire with the new weapons and tactics of his age. The ancient historian Plutarch reminds us how that campaign ended: “When Alexander saw the breadth of his domain, he wept, for there were no more worlds to conquer.”

Elliot Ackerman and James Stavridis are the authors of “2054,” a novel that speculates about the role of AI in future conflicts, just published by Penguin Press. Ackerman, a Marine veteran, is the author of numerous books and a senior fellow at Yale’s Jackson School of Global Affairs. Admiral Stavridis, U.S. Navy (ret.), was the 16th Supreme Allied Commander of NATO and is a partner at the Carlyle Group.

 


Wednesday, March 06, 2024

Deep learning models reveal sex differences in human functional brain organization that are replicable, generalizable, and behaviorally relevant

Ryali et al. do a massive analysis that argues strongly against the notion of a continuum in male-female brain organization, and underscore the crucial role of sex as a biological determinant in human brain organization and behavior.  I pass on the significance and abstract statements. Motivated readers can obtain a PDF of the article from me.  

Significance

Sex is an important biological factor that influences human behavior, impacting brain function and the manifestation of psychiatric and neurological disorders. However, previous research on how brain organization differs between males and females has been inconclusive. Leveraging recent advances in artificial intelligence and large multicohort fMRI (functional MRI) datasets, we identify highly replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization localized to the default mode network, striatum, and limbic network. Our findings advance the understanding of sex-related differences in brain function and behavior. More generally, our approach provides AI–based tools for probing robust, generalizable, and interpretable neurobiological measures of sex differences in psychiatric and neurological disorders.
Abstract
Sex plays a crucial role in human brain development, aging, and the manifestation of psychiatric and neurological disorders. However, our understanding of sex differences in human functional brain organization and their behavioral consequences has been hindered by inconsistent findings and a lack of replication. Here, we address these challenges using a spatiotemporal deep neural network (stDNN) model to uncover latent functional brain dynamics that distinguish male and female brains. Our stDNN model accurately differentiated male and female brains, demonstrating consistently high cross-validation accuracy (>90%), replicability, and generalizability across multisession data from the same individuals and three independent cohorts (N ~ 1,500 young adults aged 20 to 35). Explainable AI (XAI) analysis revealed that brain features associated with the default mode network, striatum, and limbic network consistently exhibited significant sex differences (effect sizes > 1.5) across sessions and independent cohorts. Furthermore, XAI-derived brain features accurately predicted sex-specific cognitive profiles, a finding that was also independently replicated. Our results demonstrate that sex differences in functional brain dynamics are not only highly replicable and generalizable but also behaviorally relevant, challenging the notion of a continuum in male-female brain organization. Our findings underscore the crucial role of sex as a biological determinant in human brain organization, have significant implications for developing personalized sex-specific biomarkers in psychiatric and neurological disorders, and provide innovative AI-based computational tools for future research.

Monday, February 12, 2024

The Art of Doing Nothing

Deep into the Juniper/Cedar pollen allergy season in Austin TX, I'm being frustrated that I have so little energy to do things. It's as if my batteries can not muster more than a 10% charge. I try to tell myself that it's OK to 'just be", to do nothing, and have not been very successful at this.  So, I enjoyed  stumbling upon a  recent Guardian article, "The art of doing nothing: have the Dutch found the answer to burnout culture?," whose URL I pass on to MindBlog readers.  It describes the concept of 'niksen,' or the Dutch art of doing nothing. It has  has ameliorated my concern over how little I have been getting done, and references a 2019 NYTimes article, "The Case for Doing Nothing" that went viral when it was first published.  Letting go of always finding problems that need to be solved let's one face  a question posed by the meditation guru Loch Kelly: "What is there when there are no problems to be solved?" Variants of this question have been addressed in a thread of numerous MindBlog posts that I have now largely drawn to a close.

Monday, February 05, 2024

Functional human brain tissue produced by layering different neuronal types with 3D bioprinting

A very important advance by Su-Chun Zhang and collaborators at the University of Wisconsin that moves studies of nerve cells connecting in nutrient dishes from two to three dimensions:  

Highlights

  • Functional human neural tissues assembled by 3D bioprinting
  • Neural circuits formed between defined neural subtypes
  • Functional connections established between cortical-striatal tissues
  • Printed tissues for modeling neural network impairment

Summary

Probing how human neural networks operate is hindered by the lack of reliable human neural tissues amenable to the dynamic functional assessment of neural circuits. We developed a 3D bioprinting platform to assemble tissues with defined human neural cell types in a desired dimension using a commercial bioprinter. The printed neuronal progenitors differentiate into neurons and form functional neural circuits within and between tissue layers with specificity within weeks, evidenced by the cortical-to-striatal projection, spontaneous synaptic currents, and synaptic response to neuronal excitation. Printed astrocyte progenitors develop into mature astrocytes with elaborated processes and form functional neuron-astrocyte networks, indicated by calcium flux and glutamate uptake in response to neuronal excitation under physiological and pathological conditions. These designed human neural tissues will likely be useful for understanding the wiring of human neural networks, modeling pathological processes, and serving as platforms for drug testing.
 

 


Sunday, January 21, 2024

Titles and URLs for key MindBlog posts on selves

I pass on a chronological list of titles and URLs of MindBlog posts assembled in preparation for a video chat with a European MindBlog reader:

An "Apostle's Creed" for the humanistic scientific materialist?
https://mindblog.dericbownds.net/2006/03/apostles-creed-for-humanistic.html

Some rambling on "Selves" and “Purpose”
https://mindblog.dericbownds.net/2007/10/some-rambling-on-selves-and-purpose.html

Self, purpose, and tribal mentality as Darwinian adaptations (or…Why why aren’t we all enlightened?)
https://mindblog.dericbownds.net/2020/05/self-purpose-and-tribal-mentality-as.html

MindBlog passes on a note: on the relief of not being yourself
https://mindblog.dericbownds.net/2020/03/mindblog-passes-on-note-on-relief-of.html

Points on having a self and free will.
https://mindblog.dericbownds.net/2021/03/points-on-having-self-and-free-will.html

I am not my problem
https://mindblog.dericbownds.net/2022/06/i-am-not-my-problem.html

The non-duality industry as a panacea for the anxieties of our times?         https://mindblog.dericbownds.net/2022/11/the-non-duality-industry-as-panacea-for.html

Enlightenment, Habituation, and Renewal - Or, Mindfulness as the opiate of the thinking classes?
https://mindblog.dericbownds.net/2023/01/enlightenment-habituation-and-renewal.html

A quick MindBlog riff on what a self is….
https://mindblog.dericbownds.net/2023/01/a-quick-mindblog-riff-on-what-self-is.html

MindBlog paragraphs bloviating on the nature of the self ask Google Bard and Chat GPT 4 for help
https://mindblog.dericbownds.net/2023/05/mindblog-paragraphs-bloviating-on.html

A MindBlog paragraph on non-dual awareness massaged by Bard and ChatGPT-4
https://mindblog.dericbownds.net/2023/07/a-mindblog-paragraph-on-non-dual.html

Constructing Self and World
https://mindblog.dericbownds.net/2023/09/constructing-self-and-world.html  

Anthropic Claude's version of my writing on the Mind - a condensation of my ideas
https://mindblog.dericbownds.net/2023/10/anthropic-claudes-version-of-my-writing.html  

A Materialist's Credo
https://mindblog.dericbownds.net/2023/10/a-materialists-credo.html

How our genes support our illusory selves - the "Baldwin effect"
https://mindblog.dericbownds.net/2023/11/how-our-genes-support-our-illusory.html











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Wednesday, January 10, 2024

If psychedelics heal, how do they do it?

I pass on from a recent issue of PNAS this informative "News Feature" article by Carolyn Beans (open source).  It discusses studies trying to determine the brain mechanisms by which therapeutically effective psychedelics such as MDMA (3,4-methylenedioxymethamphetamine, commonly known as ecstacy), psilocybin, and LSD have their effect.  They all act on serotonin receptors.  

Sunday, December 03, 2023

A flash of clarity on what current LLMs can and can not do. An AI apocalypse does not appear to be eminent...

In his most recent newsletter, Venkatesh Rao pulls up a twitter thread he wrote in 2017 making what he calls an ontological distinction between  boundary intelligence and interior intelligence.  This was before transformers like GPT-1 began to attract more attention. The distinction Rao makes is central to understanding what current large language models (LLMs) can and can not do. Here is his unedited text from 2017:
 
1. I'd like to make up a theory of intelligence based on a 2-element ontology: boundary and interior intelligence

2. Boundary intelligence is how you deal with information flows across the boundary of your processing abilities 

3. Interior intelligence is how you process information. Includes logic, emotional self-regulation, etc.

4. A thesis I've been converging on is that boundary intelligence is VASTLY more consequential once interior intelligence exceeds a minimum

5. Boundary intelligence is by definition meta, since you're tuning your filters and making choices about what to even let hit your attention

6. I think it is highly consequential because almost all risk management happens via boundary intelligence (blindspots, black swans etc)

7. Interior intelligence is your poker skill and strategy. Boundary intelligence is picking which table to sit down at

8. Interior intelligence is reading a book competently, extracting insights and arguments. Boundary intelligence is picking books to read. 

9. Interior intelligence is being a good listener. Boundary intelligence is deciding whom to listen to. 

10. Basically, better input plus mediocre IQ beats bad input and genius IQ every time, so boundary intelligence is leverage

11. And obviously, boundary intelligence is more sensitive to context. The noisier and angrier info streams get, the more BI beats II

12. Most of boundary intelligence has to do with input intelligence, but output intelligence becomes more important with higher agency 

13. Output intelligence is basically the metacognition around when/where/how/to-whom/why to say or do things you are capable of saying/doing

14. We think a lot about external factors in decisions, but output intelligence is about freedom left after you've dealt with external part

Next, from the abstract of a forthcoming paper by Yadlowsky et al. Rao extracts the following:

…when presented with tasks or functions which are out-of-domain of their pretraining data, we demonstrate various failure modes of transformers and degradation of their generalization for even simple extrapolation tasks. Together our results highlight that the impressive ICL abilities of high-capacity sequence models may be more closely tied to the coverage of their pretraining data mixtures than inductive biases that create fundamental generalization capabilities.

And then, in the following selected clips, continues his text:

Translated into the idiom from the fourteen points above, this translates into “It’s all interior intelligence, just within a very large boundary.” There is no boundary intelligence in current machine learning paradigms. There isn’t even an awareness of boundaries; just the ability to spout statements about doubt, unknowns, and boundaries of knowability; a bit like a blind person discussing color in the abstract.

This is not to say AI cannot acquire BI. In fact, it can do so in a very trivial way, through embodiment. Just add robots around current AIs and let them loose in real environments.

The reason people resist this conclusion is is irrational attachment to interior intelligence as a sacred cow (and among computer science supremacists, a reluctance to acknowledge the relevance and power of embodiment and situatedness in understandings of intelligence). If much of the effectual power of intelligence is attributable to boundary intelligence, there is much less room for sexy theories of interior intelligence. Your (cherished or feared) god-like AI is reduced to learning through FAFO (Fuck around and find out) feedback relationships with the rest of the universe, across its boundary, same as us sadsack meatbag intelligences with our paltry 4-GPU-grade interior intelligence.

In their current (undoubtedly very impressive) incarnation, what we have with AI is 100% II, 0% BI. Human and animal intelligences (and I suspect even plant intelligences, and definitely evolutionary process intelligence) are somewhere between 51-49 to 99.9-0.1% BI. They are dominated to varying degrees by boundary intelligence. Evolutionary processes are 100% BI, 0% II.

Wednesday, August 30, 2023

Neuron–astrocyte networks might perform the core computations performed by AI transformer blocks

Fascinating ideas from Kozachkov et al. Their text contains primers on Astrocyte biology and the transformers found in AI Generative Pre-trained Transformers such as ChatGPT.  

Significance

Transformers have become the default choice of neural architecture for many machine learning applications. Their success across multiple domains such as language, vision, and speech raises the question: How can one build Transformers using biological computational units? At the same time, in the glial community, there is gradually accumulating evidence that astrocytes, formerly believed to be passive house-keeping cells in the brain, in fact play an important role in the brain’s information processing and computation. In this work we hypothesize that neuron–astrocyte networks can naturally implement the core computation performed by the Transformer block in AI. The omnipresence of astrocytes in almost any brain area may explain the success of Transformers across a diverse set of information domains and computational tasks.
Abstract
Glial cells account for between 50% and 90% of all human brain cells, and serve a variety of important developmental, structural, and metabolic functions. Recent experimental efforts suggest that astrocytes, a type of glial cell, are also directly involved in core cognitive processes such as learning and memory. While it is well established that astrocytes and neurons are connected to one another in feedback loops across many timescales and spatial scales, there is a gap in understanding the computational role of neuron–astrocyte interactions. To help bridge this gap, we draw on recent advances in AI and astrocyte imaging technology. In particular, we show that neuron–astrocyte networks can naturally perform the core computation of a Transformer, a particularly successful type of AI architecture. In doing so, we provide a concrete, normative, and experimentally testable account of neuron–astrocyte communication. Because Transformers are so successful across a wide variety of task domains, such as language, vision, and audition, our analysis may help explain the ubiquity, flexibility, and power of the brain’s neuron–astrocyte networks.

Wednesday, July 26, 2023

A MindBlog paragraph on non-dual awareness massaged by Bard and ChatGPT-4

This post is using MindBlog to archive a further example example of the sort shown in Monday’s post ...of GPT bots editing  a paragraph I wrote, this one being on non-dual awareness.  The paragraph:

The fact that  there is no there there means it. Empty is empty. The ground state is just the mute body doing its thing. From its depths storylines rise and fall, bonbons to savor or ignore. The body can know that it has found a new ground state, and can be no longer willing to just play nice if the upstairs word salad is dinking around with or threatening its peace. It can withdraw from the fray. It can rest as a calm non-dual self centered within a cosmic perspective of time and space that views the rise and fall of human civilizations during the anthropocene as minor dances in the music of time.

After incorporating some changes suggested by Google Bard and GPT4:

When we say something is empty, we mean just that: it contains nothing. Our natural baseline or 'ground state' is the body performing its automatic functions without our conscious intervention. From the body's inherent depths, various thoughts or experiences emerge and recede, like bonbons we can savor or ignore. The body can grow to know that it has found a new ground state in the present moment, a ground state of awareness that no longer needs to be caught up in word salads of disruptive thoughts that can threaten its peace.  It can withdraw from the fray and can rest as a calm non-dual state of being centered within a cosmic perspective of time and space that can view the rise and fall of human civilizations during the anthropocene as minor dances in the music of time.


Monday, July 24, 2023

The evolution of transhuman forms - a MindBlog paragraph edited by GPT implementations

In this post I am documenting the final result of passing a paragraph I wrote on future human evolution through a number of different GPT (generative pre-trained transformers). This is to archive the results of this exercise for myself, and in case it might be of interest to a few MindBlog readers. The bots were prompted by a request to re-write or edit the paragraph to make it more accessible to a reader with a college education.

The bottom line is that the edited versions with the most depth and coherence were obtained using Open AI’s ChatGPT-4.  Bard.google.com, Google Workspace Labs, and Microsoft’s Bing, respectively, provided increasingly less useful versions.   Here is my original paragraph:

The linguistic capabilities of generative pre-trained transformers based on silicon  - the same capabilities that have enabled human civilization  - will soon exceed those of humans. We have brought our understanding of what human beings as they now exist are about to a plateau level at which further increments of understanding will become increasingly small. The cream has been skimmed, the best cherries have been picked, and it seems appropriate to turn our  attention now to studying the evolution of trans human forms utilizing LLMs that are better able to manage themselves and the planet.  The action is in the the pretraining P of GPT, as the models move beyond their initial human management to the stage of evolving themselves, hopefully into benign versions of Darwin’s  “forms most beautiful and most wonderful.” What is the point of humans other than being self replicating machines that evolve into further forms, hopefully before they extinguish themselves by destroying the ecological environment required for their sustenance? Can they evolve into trans human minds (or mind, singular)?
Here is the result of including some of the edits suggested by ChatGPT4’s regenerated response, a  vers. 3 of the paragraph above:
The linguistic capabilities of generative pre-trained transformers based on silicon - the same capabilities that have enabled human civilization - will soon surpass those of humans.

Our understanding of human nature as it currently exists has reached a plateau level, at which further increments of understanding are becoming increasingly small. The cream has been skimmed, the best cherries have been picked. Consequently it seems the right time to shift our focus toward studying the evolution of 'transhuman' forms. These are beings that could potentially leverage advanced language models (LLMs) to better self-regulate themselves and manage the planet.

The real crux of the matter lies in the pretraining phase of these transformers (symbolized by 'P' in GPT). This is when the models are initially developed and fine-tuned. As they evolve beyond their human programming the optimistic vision is that they might transform into benign versions of what Charles Darwin called “forms most beautiful and most wonderful.”

From a philosophical perspective, one might ask: What is the ultimate purpose of human beings, if not to serve as self-replicating entities that evolve into more advanced forms - hopefully before they extinguish themselves by destroying the ecological environment required for their sustenance? Is it possible for humans to evolve into a collective of transhuman minds or even a singular, transcendent mind?  These are questions worth exploring as we stand on the brink of an AI-enhanced future.


Friday, July 07, 2023

A meta-analysis questions the cognitive benefits of physical activity.

I give up. If anything was supposed to have been proven I would have thought it would be that exercise has a beneficial effect on brain health and cognition. Now Ciria et al. offer the following in Nature Human Biology:
Extensive research links regular physical exercise to an overall enhancement of cognitive function across the lifespan. Here we assess the causal evidence supporting this relationship in the healthy population, using an umbrella review of meta-analyses limited to randomized controlled trials (RCTs). Despite most of the 24 reviewed meta-analyses reporting a positive overall effect, our assessment reveals evidence of low statistical power in the primary RCTs, selective inclusion of studies, publication bias and large variation in combinations of pre-processing and analytic decisions. In addition, our meta-analysis of all the primary RCTs included in the revised meta-analyses shows small exercise-related benefits (d = 0.22, 95% confidence interval 0.16 to 0.28) that became substantially smaller after accounting for key moderators (that is, active control and baseline differences; d = 0.13, 95% confidence interval 0.07 to 0.20), and negligible after correcting for publication bias (d = 0.05, 95% confidence interval −0.09 to 0.14). These findings suggest caution in claims and recommendations linking regular physical exercise to cognitive benefits in the healthy human population until more reliable causal evidence accumulates.
I can not offer an informed opinion on this abstract because my usual access to journals through the University of Wisconsin library does not work with Nature Human Behavior. However, I can point you to an excellent commentary by Claudia Lopez Lloreda that discusses the meta-analysis done by Ciria et al. and gives a summary of several recent studies on exercise and brain health.

Friday, June 02, 2023

Gender inequality is associated with differences between the brains of men and women

Sobering but not surprising analysis by Jugman et al.:  

Significance

Gender inequality is associated with worse mental health and academic achievement in women. Using a dataset of 7,876 MRI scans from healthy adults living in 29 different countries, we here show that gender inequality is associated with differences between the brains of men and women: cortical thickness of the right hemisphere, especially in limbic regions such as the right caudal anterior cingulate and right medial orbitofrontal, as well as the left lateral occipital, present thinner cortices in women compared to men only in gender-unequal countries. These results suggest a potential neural mechanism underlying the worse outcome of women in gender-unequal settings, as well as highlight the role of the environment in the brain differences between women and men.
Abstract
Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women’s worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7,876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women’s brains and provide initial evidence for neuroscience-informed policies for gender equality.

Friday, April 07, 2023

Our heartbeat shapes our perception of time (and a comparison of how ChatGPT and Google Bard condense an account of work showing this)...

Ellen Barry has done a review of work by Sadeghi et al, as well as others, on how heart-brain interactions influence our perception of time. I asked ChatGPT to do a 200 word summary of her article of 795 words. It generated 237 words (below), to which I made a small addition. (I feels like I'm using the language generating mental equivalent of a physical prosthesis.) That is followed by Google Bard's response, very impressive. Microsoft's Bing was unable to deal with the task.
A recent study by psychologists at Cornell University has suggested that our heartbeats may play an important role in shaping our perception of time. The study, published in the journal Psychophysiology, found that some distortions in time perception could be driven by variable heartbeat length. The researchers fitted undergraduates with electrocardiograms to measure the length of each heartbeat and asked them to estimate the length of brief audio tones. After longer heartbeat intervals, subjects tended to perceive the tone as longer, while shorter intervals led to shorter perception. Additionally, the subjects’ heartbeat intervals lengthened after each tone, and a lower heart rate appeared to assist with perception. According to Saeedeh Sadeghi, the study’s lead author, “there is no single part of the brain or body that keeps time — it’s all a network. The brain controls the heart, and the heart, in turn, impacts the brain.”
The study addresses the mechanisms behind how we perceive the passage of milliseconds and may help us manage trauma, in which instantaneous experiences are remembered as drawn out. Intense emotional engagement speeds up perception of time, while depression slows it down. Research into perception of time has traditionally focused on different areas of the brain, but there is now greater appreciation that cognitive functions are intimately linked to the control of the body. Time is closely related to metabolic needs, and the body tries to determine how much energy it has available in the moment, which may affect how we perceive the passage of time.
Here is the response from Google Bard, more impressive, I think:
Sure, here is a 200-word summary [note - it's actually 313 words] of the text:
A recent study published in the journal Psychophysiology suggests that our heartbeat may play an important role in our perception of time. The study found that participants who had longer heartbeat intervals tended to perceive brief audio tones as longer, while participants who had shorter heartbeat intervals tended to perceive the tones as shorter. This suggests that our heart rate may influence how we experience the passage of time.
The study's lead author, Saeedeh Sadeghi, said that the findings provide more evidence that "there is no single part of the brain or body that keeps time — it's all a network." She added that the study "could have implications for understanding how people with certain conditions, such as anxiety or depression, experience time differently."
The study's findings are in line with previous research that has shown that our heart rate can affect our cognitive function. For example, one study found that people who were asked to perform a task while listening to their heartbeat performed worse on the task than those who were not listening to their heartbeat. This suggests that our heart rate may distract us and make it more difficult to focus on tasks.
The study's findings also have implications for our understanding of how we experience time in different situations. For example, the study suggests that time may seem to pass more slowly when we are stressed or anxious, as our heart rate tends to increase in these situations. Conversely, time may seem to pass more quickly when we are relaxed or bored, as our heart rate tends to decrease in these situations.
Overall, the study provides new insights into the complex relationship between our heart rate and our perception of time. The findings could have implications for understanding how people with certain conditions experience time differently, as well as for our understanding of how we experience time in different situations.

Monday, March 13, 2023

Blood-derived signals as potent drivers of both age-related brain dysfunction and brain rejuvenation.

 An open source review from Bieri et al. has some nice graphics and tables summarizing the varieties of pro-aging and rejuvenating interventions.  Here is their abstract.:

Aging induces molecular, cellular and functional changes in the adult brain that drive cognitive decline and increase vulnerability to dementia-related neurodegenerative diseases. Leveraging systemic and lifestyle interventions, such as heterochronic parabiosis, administration of ‘young blood’, exercise and caloric restriction, has challenged prevalent views of brain aging as a rigid process and has demonstrated that aging-associated cognitive and cellular impairments can be restored to more youthful levels. Technological advances in proteomic and transcriptomic analyses have further facilitated investigations into the functional impact of intertissue communication on brain aging and have led to the identification of a growing number of pro-aging and pro-youthful factors in blood. In this review, we discuss blood-to-brain communication from a systems physiology perspective with an emphasis on blood-derived signals as potent drivers of both age-related brain dysfunction and brain rejuvenation.

Monday, February 27, 2023

Possible mechanism of psychedelic therapeutic effects

From the latest issue of Science Magazine:  

The mechanism underlying psychedelic action

Psychedelic compounds promote cortical structural and functional neuroplasticity through the activation of serotonin 2A receptors. However, the mechanisms by which receptor activation leads to changes in neuronal growth are still poorly defined. Vargas et al. found that activation of intracellular serotonin 2A receptors is responsible for the plasticity-promoting and antidepressant-like properties of psychedelic compounds, but serotonin may not be the natural ligand for those intracellular receptors (see the Perspective by Hess and Gould). —PRS
Abstract
Decreased dendritic spine density in the cortex is a hallmark of several neuropsychiatric diseases, and the ability to promote cortical neuron growth has been hypothesized to underlie the rapid and sustained therapeutic effects of psychedelics. Activation of 5-hydroxytryptamine (serotonin) 2A receptors (5-HT2ARs) is essential for psychedelic-induced cortical plasticity, but it is currently unclear why some 5-HT2AR agonists promote neuroplasticity, whereas others do not. We used molecular and genetic tools to demonstrate that intracellular 5-HT2ARs mediate the plasticity-promoting properties of psychedelics; these results explain why serotonin does not engage similar plasticity mechanisms. This work emphasizes the role of location bias in 5-HT2AR signaling, identifies intracellular 5-HT2ARs as a therapeutic target, and raises the intriguing possibility that serotonin might not be the endogenous ligand for intracellular 5-HT2ARs in the cortex.

Monday, February 20, 2023

Fundamentally rethinking what a mind is and how a brain works.

The February Issue of Trends in Cognitive Science has an open source Opinions article from Lisa Feldman Barrett and collaborators that suggests that new research approaches grounded in different ontological commitments will be required to properly describe brain-behavior relationships. Here is a clip of the introductory text and a graphic clip from the article. Finally, I pass on the concluding remarks on fundamentally rethinking what a mind is and how a brain works.
Most brain imaging studies present stimuli and measure behavioral responses in temporal units (trials) that are ordered randomly. Participants’ brain signals are typically aggregated to model structured variation that allows inferences about the broader population from which people were sampled. These methodological details, when used to study any phenomenon of interest, often give rise to brain-behavior findings that vary unexpectedly (across stimuli, context, and people). Such findings are typically interpreted as replication failures, with the observed variation discounted as error caused by less than rigorous experimentation (Box 1). Methodological rigor is of course important, but replication problems may stem, in part, from a more pernicious source: faulty assumptions (i.e., ontological commitments) that mis-specify the psychological phenomena of interest.

In this paper, we review three questionable assumptions whose reconsideration may offer opportunities for a more robust and replicable science: 

 (1) The localization assumption: the instances that constitute a category of psychological events (e.g., instances of fear) are assumed to be caused by a single, dedicated psychological process implemented in a dedicated neural ensemble (see Glossary). 

 (2) The one-to-one assumption: the dedicated neural ensemble is assumed to map uniquely to that psychological category, such that the mapping generalizes across contexts, people, measurement strategies, and experimental designs. 

 (3) The independence assumption: the dedicated neural ensemble is thought to function independently of contextual factors, such as the rest of the brain, the body, and the surrounding world, so the ensemble can be studied alone without concern for those other factors. Contextual factors might moderate activity in the neural ensemble but should not fundamentally change its mapping to the instances of a psychological category. 

 These three assumptions are rooted in a typological view of the mind, brain, and behavior [1. ] that was modeled on 19th century physics and continues to guide experimental practices in much of brain-behavior research to the present day. In this paper, we have curated examples from studies of human functional magnetic resonance imaging (fMRI) and neuroscience research using non-human animals that call each assumption into question. We then sketch the beginnings of an alternative approach to study brain-behavior relationships, grounded in different ontological commitments: (i) a mental event comprises distributed activity across the whole brain; (ii) brain and behavior are linked by degenerate (i.e., many-to-one) mappings; and (iii) mental events emerge as a complex ensemble of weak, nonlinearly interacting signals from the brain, body, and external world.

 

Concluding remarks

Scientific communities tacitly agree on assumptions about what exists (called ontological commitments), what questions to ask, and what methods to use. All assumptions are firmly rooted in a philosophy of science that need not be acknowledged or discussed but is practiced nonetheless. In this article, we questioned the ontological commitments of a philosophy of science that undergirds much of modern neuroscience research and psychological science in particular. We demonstrated that three common commitments should be reconsidered, along with a corresponding course correction in methods. Our suggestions require more than merely improved methodological rigor for traditional experimental design. Such improvements are important, but may aid robustness and replicability only when the ontological assumptions behind those methods are valid. Accordingly, a productive way forward may be to fundamentally rethink what a mind is and how a brain works. We have suggested that mental events arise from a complex ensemble of signals across the entire brain, as well as the from the sensory surfaces of the body that inform on the states of the inner body and outside world, such that more than one signal ensemble maps to a single instance of a single psychological category (maybe even in the same context. To this end, scientists might find inspiration by mining insights from adjacent fields, such as evolution, anatomy, development, and ecology , as well as cybernetics and systems theory. At stake is nothing less than a viable science of how a brain creates a mind through its constant interactions with its body, its physical environment, and with the other brains-in-bodies that occupy its social world.

Friday, December 30, 2022

The pitfalls of defining neural correlates of brain functions

Rust and Le Doux do a useful brief opinion piece from which I pass on two clips, and recommend you read the whole open source text.
...neuroscientists should avoid conflating circuits that control behavior with mental states, especially in the absence of evidence that the two map onto one another. These equivalencies need to be very carefully investigated rather than presumed.
Considerable evidence suggests that circuits involving the amygdala control behavioral and physiological responses to threats. In animal research labs, threats are often recapitulated by pairing a tone with an aversive stimulus such as a weak shock to elicit ‘fear-related behaviors’ such as freezing upon hearing the tone again. The neural circuits that learn the association between the tone and shock and produce freezing behavior are among the best understood in the brain. The problem lies in labeling these circuits with the term ‘fear’, because it presumes that the threat elicits a mental state, a subjective experience, of fear that is caused by activity in the amygdala. However, mounting evidence suggests that the amygdala is not required for the mental state of fear. Instead, the mental state of fear crucially depends, at least in part, on cortical circuits that interpret or conceptualize what is occurring in the social and physical environment and in one’s body. In this framework, amygdala circuits control nonconscious defense behaviors (such as freezing) as opposed to conscious experience. Should this framework be correct, the extensive ongoing efforts devoted to targeting amygdala circuits and rodent behaviors such as freezing and avoidance are unlikely to provide a direct route to treatments for human fear and anxiety disorders. These lines of research can help, but not without recognizing the centrality of subjective experience.

Monday, December 26, 2022

Rigorous study does not find that exercise and mindfulness training improve cognitive function in older adults.

Wow, here is a study by Lenze et al. - not confirming the results of numerous other less rigorous studies reported in MindBlog posts - that is unable to demonstrate that the use of mindfulness training, exercise, or a combination of both can significantly improving cognitive function in older adults with subjective cognitive concerns. In their randomized clinical trial that included 585 participants, mindfulness training, exercise, or both did not result in significant differences in improvement in episodic memory or executive function composite scores at 6 months. Gretchen Reynolds provides context and a summary of the work in a Washington Post article.