Memoirs Of The Twentieth Century was a 1733 work of speculative fiction about the world in 1997. The prediction: technology won’t advance at all over its 18th-century level, but evil Jesuits will control everything. Paging Pope Francis…
This week in pharma company chutzpah: in order to preserve their patent on a popular eyedrop, Allergan transferred the intellectual property rights to the Mohawk Indians, who as a recognized Native American tribe are immune from certain federal intellectual property laws. Will Native American pharmaceutical patent caretaking become the same sort of cultural phenomenon as Native American casinos?
Colombian airline proposes standing-room only flights to pack planes tighter and cut fares. Consumers are outraged at the possibility of getting a completely optional extra choice in the comfort vs. price tradeoff.
Newfound doubts that the paper claiming to have successfully CRISPRed human embryos did anything of the sort. Also, note that the paper saying they did CRISPR the embryos got published in Nature, and the criticism arguing that they didn’t is so far just up on the Biorxiv.
The number one food exporter in the world is the United States. The number two food exporter in the world is the Netherlands, 1/270th the size and mostly urban. How they do it, and how they’re leading the agriculturally sustainable future.
The Kellogg-Briand Pact was a 1928 League of Nations initiative which banned aggressive war. It has a pretty poor reputation today, understandable given what happened after 1928. But a team of political scientists say it made a real and lasting positive difference. See also their data here.
I mentioned last month I didn’t understand Ribbon Farm’s big post on “premium mediocrity”. Zvi writes a summary and response which helps me understand – oh, it’s just another aspect of the whole narcissism thing. Still not sure I get Zvi’s additional analysis, but maybe someone else will write a blog post explaining his explanation.
Anatomy Of A Moral Panic: how Slate, Vice, etc fell for a bogus story that Amazon’s recommendation algorithm is encouraging people to buy terrorist bomb-making supplies together. Equally interesting – Amazon, which undoubtedly knows this makes no sense, just says it will “review” the algorithm; it’s not even worth their energy to defend themselves anymore.
Melting Asphalt challenges the traditional theory of ads where seeing a picture of a guy drinking beer on a beach makes you associate beer with fun, so you go out and buy some beer because of how fun it is. Proposes an alternative theory where ads are about creating shared social context. Not sure if true, but I think it’s important to have people challenging theories about how people are ridiculously stupid / infinitely persuadable, “because psychology”.
American Medical Association releases a statement supporting DACA, pointing out that “our nation’s health care workforce depends on the care provided by physicians and medical students with DACA status”.
America’s First Addiction Epidemic When white explorers first came to America, the Indians had never seen distilled alcohol before, and entire tribes were destroyed by alcoholism before they even knew what was hitting them. Over centuries, entire new institutions and religions evolved to deal with the problem, providing a really neat and well-documented example of cultural evolution and maybe even gene-culture coevolution in real time. Highly recommended.
Artificial intelligence can tell from your face whether you’re gay or straight with about 80% accuracy, much better than humans. But do remember that story a few months back when they thought they could do this with criminals, and turned out to just be distinguishing mugshots from nonmugshots. Also interesting: look at their pictures of the most typically gay vs. most typically straight face. Several people on Tumblr said if they had to guess the axis, they would say it was something like “most liberal looking” vs. “most conservative looking” or possibly “higher class” vs. “lower class”. What do we make of that?
The supposed “Voynich manuscript solution” making the rounds is amateurish and unable to actually predict or decode anything. Also, it might have been a gimmick made for a TV show. Remember, claims that someone has decoded the Voynich manuscript should be met with the same level of skepticism as claims that someone has proven P ? NP; this is something thousands of experts have been trying to do for decades and any declaration of sudden success should be interpreted in that light.
Given the recent Equifax hacking, you might be interested in this guide to dealing with identity theft. Key fact: you have to hit exactly the right legal notes to make banks take action on your identity theft claim, and most ordinary people can’t navigate the process and aren’t able to get their names cleared. Some Good Samaritan created a form letter that hit exactly the right legal notes, everyone started using it, the banks became annoyed that they had to actually respond to identity theft claims now, and they successfully lobbied Congress to prohibit using form letters to report identity theft.
A new advance in open-access science: the arxiv overlay journal. You publish your paper on arxiv and then submit it to the journal; it gets peer-reviewed and officially declared a Published Paper and everything, and then the journal itself is just a set of links to the arxiv.
Second-newest convert to the AI risk movement: Hillary Clinton. “Technologists like Elon Musk, Sam Altman, and Bill Gates, and physicists like Stephen Hawking have warned that artificial intelligence could one day pose an existential security threat…every time I went out to Silicon Valley during the campaign, I came home more alarmed about this. My staff lived in fear that I’d start talking about ‘the rise of the robots’ in some Iowa town hall.”
Newest convert to the AI risk movement: Vladimir Putin. ” Vladimir Putin may secretly be on the side of Elon Musk in their indirect debate over the threat posed by artificial intelligence. As Arkady Volozh, the head of Yandex, pitched him on the technology’s potential, the Russian president inquired about when AI ‘will eat us’. The question seemed to baffle the head of Russia’s biggest tech firm, who was giving Putin a tour on the company’s Moscow HQ on Thursday.”
Suicide rates up by a third over the past ten years, mostly among the less educated.
“Jubal Harshaw” offers a skeptical counternarrative of the opioid crisis – what if we just made the reasonable medical decision to prescribe opioids three times as often for pain, and then a constant rate of death per prescription caused opiate-related deaths to triple? And then also heroin got cheaper so more people started using it?
Every species has a “type specimen” – a single individual, usually the first-discovered or best-known member of that species, who is declared by fiat to be the central example of a member of that species so that if there’s ever a debate about membership of that species the unclear examples can be compared to the type. The type specimen for humans is Carl Linnaeus.
Developmental psychologist Erik Erikson, famous for his eight stages of life, was originally “Erik Homburger”. He changed his last name to Erikson to symbolizethat he was the son of himself, ie had created his own identity.
Rationalist hub Less Wrong has relaunched with a new coat of paint, better moderation, and an improved technical base. Check it out.
The astronomer who discovered Charon named it after his wife Charlene – he thought “Charon” was a scientific-sounding version of her nickname “Char”. It was only later that anybody realized Charon was an appropriate Greek mythological character with a link to Pluto. TINACBNEIAC.
This is the bi-weekly visible open thread. Post about anything you want, ask random questions, whatever. You can also talk at the SSC subreddit or the SSC Discord server. Also:
1. Bay Area SSC meetup today (Sunday September 24th) in San Jose, 3806 Williams Rd, starting at 2. I probably can’t make it but I hope you all have a good time.
2. New advertisement: The Greenfield Guild, a network of independent software contractors you can call for help with various software-related business needs. Free online 60 minute consults available via their website.
If the brain had been designed by an amateur, it would enter a runaway feedback loop the first time it felt an emotion. Think about it. You see a butterfly. This makes you happy. Being happy is an unexpected pleasant surprise. Now you’re happy that you’re happy. This makes you extra happy. Being extra happy is awesome! This makes you extra extra happy. And so on to as much bliss as your neurons are capable of representing. In the real world, either those feedback loops usually don’t happen, or they converge and stop at some finite point. I would not be surprised to learn that a lot of evolutionary innovation and biochemical complexity goes into creating a strong barrier against conditioning on your own internal experience.
“Evolutionary innovation and biochemical complexity”? Haha no, people are just too distractable to keep having the same emotion for more than a couple seconds.
I get this from Leigh Brasington’s excellent Right Concentration, a Buddhist perspective on various advanced meditative states called jhanas. To get to the first of these jhanas (there are eight in all), you become really good at concentration meditation, until you can concentrate on your breath a long time without getting distracted. Then you concentrate on your breath for a long time. Then you take your one-pointed ultra-concentrated mind, and you notice (or generate, or imagine) a pleasant feeling. This produces the first jhana, which the Buddhist scriptures describe as:
One drenches, steeps, saturates, and suffuses one’s body with the rapture and happiness born of seclusion, so that there is no part of one’s body that is not suffused by rapture and happiness.
Brasington backs this up with his own experience and those of other meditators he knows. The first jhana is really, really, really pleasurable; when you hear meditators talk about achieving “bliss states”, it’s probably something like the first jhana.
And here’s the book’s description of why it happens:
When access concentration is firmly established, then you shift your attention from the breath (or whatever your meditation object is) to a pleasant sensation. You put your attention on that sensation, and maintain your attention on that sensation, and do nothing else…
What you are attempting to do is set up a positive feedback loop. An example of a positive feedback loop is that awful noise a speaker will make if a microphone is held too close to it. What’s happening is that the ambient noise in the room goes into the microphone, is amplified by the amplifier, and comes out the speaker louder. It then reenters the microphone, gets amplified even more, comes out louder still, goes into the microphone yet again, and so on. You are trying to do exactly the same thing, except, rather than a positive feedback loop of noise, you are attempting to generate a positive feedback loop of pleasure. You hold your attention on a pleasant sensation. That feels nice, adding a bit more pleasure to your overall experience. That addition is also pleasurable, adding more pleasure, and so on, until, instead of getting a horrible noise, you get an explosion of pleasure.
The book doesn’t come out and say that the other seven jhanas are the same thing, but that seems consistent with the descriptions. For example, the fourth jhana is a state of ultimate calm. Seems like maybe if you become calm, then being so calm is kind of calming, and that’s even more calming, and so on until you’ve maxed out your mental calmness-meter.
And the explanation of why this doesn’t happen all the time is that non-meditators just can’t concentrate hard enough. A microphone-amp system that turns on and off a couple of times each second will never get a really good feedback loop going. A mind that’s always flitting from one thing to another can’t build up enough self-referentiality to reach infinite bliss.
I always wanted to meditate more, but never really got around to it. And (I thought) I had an unimpeachable excuse. The demands of a medical career are incompatible with such a time-consuming practice.
Enter Daniel Ingram MD, an emergency physician who claims to have achieved enlightenment just after graduating medical school. His book is called Mastering The Core Teachings Of The Buddha, but he could also have called it Buddhism For ER Docs. ER docs are famous for being practical, working fast, and thinking everyone else is an idiot. MCTB delivers on all three counts. And if you’ve ever had an attending quiz you on the difference between type 1 and type 2 second-degree heart block, you’ll love Ingram’s taxonomy of the stages of enlightenment.
The result is a sort of perfect antidote to the vague hippie-ism you get from a lot of spirituality. For example, from page 324:
I feel the need to address, which is to say shoot down with every bit of rhetorical force I have, the notion promoted by some teachers and even traditions that there is nothing to do, nothing to accomplish, no goal to obtain, no enlightenment other than the ordinary state of being…which, if it were true, would have been very nice of them, except that it is complete bullshit. The Nothing To Do School and the You Are Already There School are both basically vile extremes on the same basic notion that all effort to attain to mastery is already missing the point, an error of craving and grasping. They both contradict the fundamental premise of this book, namely that there is something amazing to attain and understand and that there are specific, reproducible methods that can help you do that. Here is a detailed analysis of what is wrong with these and related perspectives…
…followed by a detailed analysis of what’s wrong with this position, which he compared to “let[ting] a blind and partially paralyzed untrained stroke victim perform open-heart surgery on your child based on the notion that they are already an accomplished surgeon but just have to realize it”.
This isn’t to say that MCTB isn’t a spiritual book, or that it shies away from mysticism or the transcendent. MCTB is very happy to discuss mysticism and the transcendent. It just quarantines the mystery within a carefully explained structure of rationally-arranged progress, so that it looks something like “and at square 41B in our perfectly rectangular grid you’ll encounter a mind-state which is impossible to explain even in principle, here are a few woefully inadequate metaphors for this mind-state so you’ll know when you’ve found it and should move on to square 41C.”
This is a little jarring. But – Ingram argues – it’s also very Buddhist. If you read the sutras with an open mind, the Buddha sounds a lot more like an ER doctor than a hippie. MCTB has a very Protestant fundamentalist feeling of digging through the exterior trappings of a religion to try to return to the purity of its origins. As far as I can tell, it succeeds – and in succeeding helped me understand Buddhism a whole lot better than anything else I’ve read.
Ingram follows the Buddha in dividing the essence of Buddhism into three teachings: morality, concentration, and wisdom.
Morality seems like the odd one out here. Some Buddhists like to insist that Buddhism isn’t really a “religion”. It’s less like Christianity or Islam than it is like (for example) high intensity training at the gym – a highly regimented form of practice that improves certain faculties if pursued correctly. Talking about “morality” makes this sound kind of hollow; nobody says you have to be a good person to get bigger muscles from lifting weights.
MCTB gives the traditional answer: you should be moral because it’s the right thing to do, but also because it helps meditation. The same things that make you able to sleep at night with a clear mind make you able to meditate with a clear mind:
One more great thing about the first training [morality] is that it really helps with the next training: concentration. So here’s a tip: if you are finding it hard to concentrate because your mind is filled with guilt, judgment, envy or some other hard and difficult thought pattern, also work on the first training, kindness. It will be time well spent.
That leaves concentration (samatha) and wisdom (vipassana). You do samatha to get a powerful mind; you get a powerful mind in order do to vipassana.
Samatha meditation is the “mindfulness” stuff you’re always hearing about: concentrate on the breath, don’t let yourself get distracted, see if you can just attend to the breath and nothing else for minutes or hours. I read whole books about this before without understanding why it was supposed to be good, aside from vague things like “makes you feel more serene”. MCTB gives two reasons: first, it gets you into jhanas. Second, it prepares you for vipassana.
Jhanas are unusual mental states you can get into with enough concentration. Some of them are super blissful. Others are super tranquil. They’re not particularly meaningful in and of themselves, but they can give you heroin-level euphoria without having to worry about sticking needles in your veins. MCTB says, understatedly, that they can be a good encouragement to continue your meditation practice. It gives a taxonomy of eight jhanas, and suggests that a few months of training in samatha meditation can get you to the point where you can reach at least the first.
But the main point of samatha meditation is to improve your concentration ability so you can direct it to ordinary experience. Become so good at concentrating that you can attain various jhanas – but then, instead of focusing on infinite bliss or whatever other cool things you can do with your new talent, look at a wall or listen to the breeze or just try to understand the experience of existing in time.
This is vipassana (“insight”, “wisdom”) meditation. It’s a deep focus on the tiniest details of your mental experience, details so fleeting and subtle that without a samatha-trained mind you’ll miss them entirely. One such detail is the infamous “vibrations”, so beloved of hippies. Ingram notes that every sensation vibrates in and out of consciousness at a rate of between five and forty vibrations per second, sometimes speeding up or slowing down depending on your mental state. I’m a pathetic meditator and about as far from enlightenment as anybody in this world, but with enough focus even I have been able to confirm this to be true. And this is pretty close to the frequency of brain waves, which seems like a pretty interesting coincidence.
But this is just an example. The point is that if you really, really examine your phenomenological experience, you realize all sorts of surprising things. Ingram says that one early insight is a perception of your mental awareness of a phenomenon as separate from your perception of that phenomenon:
This mental impression of a previous sensation is like an echo, a resonance. The mind takes a crude impression of the object, and that is what we can think about, remember, and process. Then there may be a thought or an image that arises and passes, and then, if the mind is stable, another physical pulse. Each one of these arises and vanishes completely before the other begins, so it is extremely possible to sort out which is which with a stable mind dedicated to consistent precision and not being lost in stories. This means the instant you have experienced something, you know that it isn’t there any more, and whatever is there is a new sensation that will be gone in an instant. There are typically many other impermanent sensations and impressions interspersed with these, but, for the sake of practice, this is close enough to what is happening to be a good working model.
Engage with the preceding paragraphs. They are the stuff upon which great insight practice is based. Given that you know sensations are vibrating, pulsing in and out of reality, and that, for the sake of practice, every sensation is followed directly by a mental impression, you now know exactly what you are looking for. You have a clear standard. If you are not experiencing it, then stabilize the mind further, and be clearer about exactly when and where there are physical sensations.
With enough of this work, you gain direct insight into what Buddhists call “the three characteristics”. The first is impermanence, and is related to all the stuff above about how sensations flicker and disappear. The second is called “unsatisfactoriness”, and involves the inability of any sensation to be fulfilling in some fundamental way. And the last is “no-self”, an awareness that these sensations don’t really cohere into the classic image of a single unified person thinking and perceiving them.
The Buddha famously said that “life is suffering”, and placed the idea of suffering – dukkha – as the center of his system. This dukkha is the same as the “unsatisfactoriness” above.
I always figured the Buddha was talking about life being suffering in the sense that sometimes you’re poor, or you’re sick, or you have a bad day. And I always figured that making money or exercising or working to make your day better sounded like a more promising route to dealing with this kind of suffering than any kind of meditative practice. Ingram doesn’t disagree that things like bad days are examples of dukkha. But he explains that this is something way more fundamental. Even if you were having the best day of your life and everything was going perfectly, if you slowed your mind down and concentrated perfectly on any specific atomic sensation, that sensation would include dukkha. Dukkha is part of the mental machinery.
MCTB acknowledges that all of this sounds really weird. And there are more depths of insight meditation, all sorts of weird things you notice when you look deep enough, that are even weirder. It tries to be very clear that nothing it’s writing about is going to make much sense in words, and that reading the words doesn’t really tell you very much. The only way to really make sense of it is to practice meditation.
When you understand all of this on a really fundamental level – when you’re able to tease apart every sensation and subsensation and subsubsensation and see its individual components laid out before you – then at some point your normal model of the world starts running into contradictions and losing its explanatory power. This is very unpleasant, and eventually your mind does some sort of awkward Moebius twist on itself, adopts a better model of the world, and becomes enlightened.
The rest of the book is dedicated to laying out, in detail, all the steps that you have to go through before this happens. In Ingram’s model – based on but not identical to the various models in various Buddhist traditions – there are fifteen steps you have to go through before “stream entry” – the first level of enlightenment. You start off at the first step, after meditating some number of weeks or months or years you pass to the second step, and so on.
A lot of these are pretty boring, but Ingram focuses on the fourth step, Arising And Passing Away. Meditators in this step enter what sounds like a hypomanic episode:
In the early part of this stage, the meditator’s mind speeds up more and more quickly, and reality begins to be perceived as particles or fine vibrations of mind and matter, each arising and vanishing utterly at tremendous speed…As this stage deepens and matures, meditators let go of even the high levels of clarity and the other strong factors of meditation, perceive even these to arise and pass as just vibrations, not satisfy, and not be self. They may plunge down into the very depths of the mind as though plunging deep underwater to where they can perceive individual frames of reality arise and pass with breathtaking clarity as though in slow motion […]
Strong sensual or sexual feelings and dreams are common at this stage, and these may have a non-discriminating quality that those attached to their notion of themselves as being something other than partially bisexual may find disturbing. Further, if you have unresolved issues around sexuality, which we basically all have, you may encounter aspects of them during this stage. This stage, its afterglow, and the almost withdrawal-like crash that can follow seem to increase the temptation to indulge in all manner of hedonistic delights, particularly substances and sex. As the bliss wears off, we may find ourselves feeling very hungry or lustful, craving chocolate, wanting to go out and party, or something like that. If we have addictions that we have been fighting, some extra vigilance near the end of this stage might be helpful.
This stage also tends to give people more of an extroverted, zealous or visionary quality, and they may have all sorts of energy to pour into somewhat idealistic or grand projects and schemes. At the far extreme of what can happen, this stage can imbue one with the powerful charisma of the radical religious leader.
Finally, at nearly the peak of the possible resolution of the mind, they cross something called “The Arising and Passing Event” (A&P Event) or “Deep Insight into the Arising and Passing Away”…Those who have crossed the A&P Event have stood on the ragged edge of reality and the mind for just an instant, and they know that awakening is possible. They will have great faith, may want to tell everyone to practice, and are generally evangelical for a while. They will have an increased ability to understand the teachings due to their direct and non-conceptual experience of the Three Characteristics. Philosophy that deals with the fundamental paradoxes of duality will be less problematic for them in some way, and they may find this fascinating for a time. Those with a strong philosophical bent will find that they can now philosophize rings around those who have not attained to this stage of insight. They may also incorrectly think that they are enlightened, as what they have seen was completely spectacular and profound. In fact, this is strangely common for some period of time, and thus may stop practicing when they have actually only really begun.
This is a common time for people to write inspired dharma books, poetry, spiritual songs, and that sort of thing. This is also the stage when people are more likely to join monasteries or go on great spiritual quests. It is also worth noting that this stage can look an awful lot like a manic episode as defined in the DSM-IV (the current diagnostic manual of psychiatry). The rapture and intensity of this stage can be basically off the scale, the absolute peak on the path of insight, but it doesn’t last. Soon the meditator will learn what is meant by the phrase, “Better not to begin. Once begun, better to finish!”
If this last part sounds ominous, it probably should. If the fourth stage looks like a manic episode, the next five or six stages all look like some flavor of deep clinical depression. Ingram discusses several spiritual traditions and finds that they all warn of an uncanny valley halfway along the spiritual path; he himself adopts St. John’s phrase “Dark Night Of The Soul”. Once you have meditated enough to reach the A&P Event, you’re stuck in the (very unpleasant) Dark Night Of The Soul until you can meditate your way out of it, which could take months or years.
Ingram’s theory is that many people have had spiritual experiences without deliberately pursuing a spiritual practice – whether this be from everyday life, or prayer, or drugs, or even things you do in dreams. Some of these people accidentally cross the A&P Event, reach the Dark Night Of The Soul, and – not even knowing that the way out is through meditation – get stuck there for years, having nothing but a vague spiritual yearning and sense that something’s not right. He says that this is his own origin story – he got stuck in the Dark Night after having an A&P Event in a dream at age 15, was low-grade depressed for most of his life, and only recovered once he studied enough Buddhism to realize what had happened to him and how he could meditate his way out:
When I was about 15 years old I accidentally ran into some of the classic early meditation experiences described in the ancient texts and my reluctant spiritual quest began. I did not realize what had happened, nor did I realize that I had crossed something like a point of no return, something I would later call the Arising and Passing Away. I knew that I had had a very strange dream with bright lights, that my entire body and world had seemed to explode like fireworks, and that afterwards I somehow had to find something, but I had no idea what that was. I philosophized frantically for years until I finally began to realize that no amount of thinking was going to solve my deeper spiritual issues and complete the cycle of practice that had already started.
I had a very good friend that was in the band that employed me as a sound tech and roadie. He was in a similar place, caught like me in something we would later call the Dark Night and other names. He also realized that logic and cognitive restructuring were not going to help us in the end. We looked carefully at what other philosophers had done when they came to the same point, and noted that some of our favorites had turned to mystical practices. We reasoned that some sort of nondual wisdom that came from direct experience was the only way to go, but acquiring that sort of wisdom seemed a daunting task if not
I [finally] came to the profound realization that they have actually worked all of this stuff out. Those darn Buddhists have come up with very simple techniques that lead directly to remarkable results if you follow instructions and get the dose high enough. While some people don’t like this sort of cookbook approach to meditation, I am so grateful for their recipes that words fail to express my profound gratitude for the successes they have afforded me. Their simple and ancient practices revealed more and more of what I sought. I found my experiences filling in the gaps in the texts and teachings, debunking the myths that pervade the standard Buddhist dogma and revealing the secrets meditation teachers routinely keep to themselves. Finally, I came to a place where I felt comfortable writing the book that I had been looking for, the book you now hold in your hands.
Once you meditate your way out of the Dark Night, you go through some more harrowing experiences, until you finally reach the fifteenth stage, Fruiition, and achieve “stream entry” – the first level of enlightenment. Then you do it all again on a higher level, kind of like those video games where when you beat the game you get access to New Game+ . Traditionally it takes four repetitions of the spiritual path before you attain complete perfect enlightenment, but Ingram suggests this is metaphorical and says it took him approximately twenty-seven repetitions over seven years.
He also says – and here his usual lucidity deserted him and I ended up kind of confused – that once you’ve achieved stream entry, you’re going to be going down paths whether you like it or not – the “stream” metaphor is apt insofar as it suggests being borne along by a current. The rest of your life – even after you achieve complete perfect enlightenment – will be spent cycling through the fifteen stages, with each stage lasting a few days to months.
This seems pretty bad, since the stages look a lot like depression, mania, and other more arcane psychiatric and psychological problems. Even if you don’t mind the emotional roller coaster, a lot of them sound just plain exhausting, with your modes of cognition and perception shifting and coming into question at various points. MCTB offers some tips for dealing with this – you can always slow your progress down the path by gorging on food, refusing to meditate, and doing various other unspiritual things, but the whole thing lampshades a question that MCTB profoundly fails at giving anything remotely like an answer to:
Why would you want to do any of this?
The Buddha is supposed to have said: “I gained nothing whatsoever from Supreme Enlightenment, and for that reason it is called Supreme Enlightenment”. And sure, that’s the enigmatic Zen-sounding sort of statement we expect from our spiritual leaders. But if Buddhist practice is really difficult, and makes you perceive every single sensation as profoundly unsatisfactory in some hard-to-define way, and can plunge you into a neverending depression which you might get out of if you meditate hard enough, and then gives you a sort of permanent annoying low-grade bipolar disorder even if you succeed, then we’re going to need something better than pithy quotes.
Ingram dedicates himself hard to debunking a lot of the things people would use to fill the gap. Pages 261-328 discuss the various claims Buddhist schools have made about enlightenment, mostly to deny them all. He has nothing but contempt for the obviously silly ones, like how enlightened people can fly around and zap you with their third eyes. But he’s equally dismissive of things that sort of seem like the basics. He denies claims about how enlightened people can’t get angry, or effortlessly resist temptation, or feel universal unconditional love, or things like that. Some of this he supports with stories of enlightened leaders behaving badly; other times he cites himself as an enlightened person who frequently experiences anger, pain, and the like. Once he’s stripped everything else away, he says the only thing one can say about enlightenment is that it grants a powerful true experience of the non-dual nature of the world.
But still, why would we want to get that? I am super in favor of knowledge-for-knowledge’s-sake, but I’ve also read enough Lovecraft to have strong opinions about poking around Ultimate Reality in ways that tend to destroy your mental health.
The best Ingram can do is this:
I realize that I am not doing a good job of advertising enlightenment here, particularly following my descriptions of the Dark Night. Good point. My thesis is that those who must find it will, regardless of how it is advertised. As to the rest, well, what can be said? Am I doing a disservice by not selling it like nearly everyone else does? I don’t think so. If you want grand advertisements for enlightenment, there is a great stinking mountain of it there for you partake of, so I hardly think that my bringing it down to earth is going to cause some harmful deficiency of glitz in the great spiritual marketplace.
[Meditation teacher] Bill Hamilton had a lot of great one-liners, but my favorite concerned insight practices and their fruits, of which he said, “Highly recommended, can’t tell you why.” That is probably the safest and most accurate advertisement for enlightenment that I have ever heard.
I was reading MCTB at the same time I read Surfing Uncertainty, and it was hard not to compare them. Both claim to be guides to the mysteries of the mind – one from an external scientific perspective, the other from an internal phenomenological perspective. Is there any way to link them up?
Remember this quote from Surfing Uncertainty?:
Plausibly, it is only because the world we encounter must be parsed for action and intervention that we encounter, in experience, a relatively unambiguous determinate world at all. Subtract the need for action and the broadly Bayesian framework can seem quite at odds with the phenomenal facts about conscious perceptual experience: our world, it might be said, does not look as if it is encoded in an intertwined set of probability density distributions. Instead, it looks unitary and, on a clear day, unambiguous…biological systems, as mentioned earlier, may be informed by a variety of learned or innate “hyperpriors” concerning the general nature of the world. One such hyperprior might be that the world is usually in one determinate state or another.
Taken seriously, it suggests that some of the most fundamental factors of our experience are not real features of the sensory world, but very strong assumptions to which we fit sense-data in order to make sense of them. And Ingram’s theory of vipassana meditation looks a lot like concentrating really hard on our actual sense-data to try to disentangle them from the assumptions that make them cohere.
In the same way that our priors “snap” phrases like “PARIS IN THE THE SPRINGTIME” to a more coherent picture with only one “the”, or “snap” our saccade-jolted and blind-spot-filled visual world into a reasonable image, maybe they snap all of this vibrating and arising and passing away into something that looks like a permanent stable image of the world.
And in the same way that concentrating on “PARIS IN THE THE SPRINGTIME” really hard without any preconceptions lets you sniff out the extra “the”, so maybe enough samatha meditation lets you concentrate on the permanent stable image of the world until it dissolves into whatever the brain is actually doing. Maybe with enough dedication to observing reality as it really is rather than as you predict it to be, you can expose even the subjective experience of an observer as just a really strong hyperprior on all of the thought-and-emotion-related sense-data you’re getting.
That leaves dukkha, this weird unsatisfactoriness that supposedly inheres in every sensation individually as well as life in general. If the goal of the brain is minimizing prediction error, if all of our normal forms of suffering like hunger and thirst and pain are just special cases of predictive error in certain inherent drives, then – well, this is a very fundamental form of badness which is inherent in all sensation and perception, and which a sufficiently-concentrated phenomenologist might be able to notice directly. Relevant? I’m not sure.
Mastering The Core Teachings Of The Buddha is a lucid guide to issues surrounding meditation practice and a good rational introduction to the Buddhist system. Parts of it are ultimately unsatisfactory, but apparently this is true of everything, so whatever.
This is the…monthly? bimonthly? occasional?…classified thread. Post advertisements, personals, and any interesting success stories from the last thread. Also:
1. Iacta_Procul, who posted about some of her life/mental health problems on the subreddit a few weeks ago, and who lots of people said they wanted a way to help, has decided to quit her dead-end job and try to start a math tutoring company. She has a Masters in math and offers to tutor any non-statistics undergrad mathematics, or any necessary test prep for the SAT/ACT/GRE/GMAT (including English/vocabulary/non-math sections). If you’re interested, contact her on Wyzant.
2. Isak – who doesn’t comment here much but is pretty active on Rationalist Tumblr Discord – is homeless right now, having trouble getting his disability check, and asking for some money to help stay afloat and get his life back on track. See his Fundly campaign page for more information.
3. An old friend of mine is looking for AI/data science people in North Carolina who he can ask questions about the opportunities there. If that describes you, email me at scott [at] shireroth [dot] org and I can get you in touch. (got enough responses; thanks to everyone who emailed)
Chekroud (2015) has a paper trying to apply the model to depression. It’s scholarly enough, and I found it helpful in figuring out some aspects of the theory I hadn’t yet understood, but it’s pretty unambitious. The overall thesis is something like “Predictive processing says high-level beliefs shape our low-level perceptions and actions, so maybe depressed people have some high-level depressing beliefs.” Don’t get me wrong, CBT orthodoxy is great and has cured millions of patients – but in the end, this is just CBT orthodoxy with a neat new coat of Bayesian paint.
There’s something more interesting in Section 7.10 of Surfing Uncertainty, “Escape From The Darkened Room”. It asks: if the brain works to minimize prediction error, isn’t its best strategy to sit in a dark room and do nothing forever? After all, then it can predict its sense-data pretty much perfectly – it’ll always just stay “darkened room”.
Section 7.10 gives a kind of hand-wave-y answer here, saying that of course organisms have some drives, and probably it makes sense for them to desire novelty and explore new options, and so on. Overall this isn’t too different from PCT’s idea of “intrinsic error”, and as long as we remember that it’s not really predicting anything in particular it seems like a fair response.
But I notice that this whole “sit in a dark room and never leave” thing sounds a lot like what depressed people say they wish they could do (and how the most severe cases of depression actually end up). Might there be a connection? Either a decrease in the mysterious intrinsic-error-style factors that counterbalance the dark room scenario, or an increase in the salience of prediction error that makes failures less tolerable?
(also, there’s one way to end all prediction error forever, and it’s something depressed people think about a lot)
Corlett, Fritch, and Fletcher claim that an amphetamine-induced mania-like state may involve pathologically high confidence in neural predictions. I don’t remember if they took the obvious next step and claimed that depression was the opposite, but that sounds like another fruitful avenue to explore. So: what if depression is pathologically low confidence in neural predictions?
Chekroud’s theory of depression as high-level-depressing-beliefs bothers me because there are so many features of depression that aren’t cognitive or emotional or related to any of these higher-level functions at all. Depressed people move more slowly, in a characteristic pattern called “psychomotor retardation”. They display perceptual abnormalities. They’re more likely to get sick. There are lots of results like this.
Depression has to be about something more than just beliefs; it has to be something fundamental to the nervous system. And low confidence in neural predictions would do it. Since neural predictions are the basic unit of thought, encoding not just perception but also motivation, reward, and even movement – globally low confidence levels would have devastating effects on a whole host of processes.
Perceptually, they would make sense-data look less clear and distinct. Depressed people describe the world as gray, washed-out, losing its contrast. This is not metaphorical. You can do psychophysical studies on color perception in depressed people, you can stick electrodes on their eyeballs, and all of this will tell you that depressed people literally see the world in washed-out shades of gray. Descriptions of their sensory experience sound intuitively like the sensory experience you would get if all your sense organs were underconfident in their judgments.
Mechanically, they would make motor movements less forceful. Remember, in PP movements are “active inferences” – the body predicts that the limb it wants to move is somewhere else, then counts on the motor system’s drive toward minimizing prediction error to do the rest. If you predictions are underconfident, your movements are insufficiently forceful and you get the psychomotor retardation that all the pathologists describe in depressed people. And what’s the closest analog to depressive psychomotor retardation? Parkinsonian bradyphrenia. What causes Parkinsonian bradyphrenia? We know the answer to this one – insufficient dopamine, where dopamine is known to encode the confidence level of motor predictions.
Motivationally – well, I’m less certain, I still haven’t found a good predictive processing account of motivation I understand on an intuitive level. But if we draw the analogy to perceptual control theory, some motivations (like hunger) are probably a kind of “intrinsic error” that can be modeled as higher-level processes feeding reference points to lower-level control systems. If we imagine the processes predicting eg hunger, then predicting with low confidence sure sounds like the sort of thing where you should be less hungry. If they’re predicting “you should get out of bed”, then predicting that with low confidence sure sounds like the sort of thing where you don’t feel a lot of motivation to get out of bed.
I’m hesitant to take “low self-confidence” as a gimme – it seems relying too much on a trick of the English language. But I think there really is a connection. Suppose that you’re taking a higher-level math class and you’re really bad at it. No matter how hard you study, you always find the material a bit confusing and are unsure whether you’re applying the concepts correctly. Eventually you start feeling kind of like a loser, you decide the math class isn’t for you, and you move on to something else where you’re more talented. Your low confidence in your beliefs (eg answers to test questions) and actions (eg problem-solving strategies) create general low self-confidence and feelings of worthlessness. Eventually you decide math isn’t for you and decide to drop the class.
If you have global low confidence, the world feels like a math class you don’t understand that you can’t escape from. This feeling might be totally false – you might be getting everything right – but you still feel that way. And there’s no equivalent to dropping out of the math class – except committing suicide, which is how far too many depressed people end up.
One complicating factor – how do we explain depressed people’s frequent certainty that they’ll fail? A proper Bayesian, barred from having confident beliefs about anything, will be maximally uncertain about whether she’ll fail or succeed – but some depressed people have really strong opinions on this issue. I’m not really sure about this, and admit it’s a point against this theory. I can only appeal to the math class example again – if there was a math class where I just had no confidence about anything I thought or said, I would probably be pretty sure I’d fail there too.
(just so I’m not totally just-so-storying here, here’s a study of depressed people’s probability calibration, which shows that – yup – they’re underconfident!)
This could tie into the “increased salience of prediction error” theory in Part I. If for some reason the brain became “overly conservative” – if it assigned very high cost to a failed prediction relative to the benefit of a successful prediction – then it would naturally lower its confidence levels in everything, the same way a very conservative better who can’t stand losing money is going to make smaller bets.
But why would low confidence cause sadness?
Well, what, really, is emotion?
Imagine the world’s most successful entrepreneur. Every company they found becomes a multibillion-dollar success. Every stock they pick shoots up and never stops. Heck, even their personal life is like this. Every vacation they take ends out picture-perfect and creates memories that last a lifetime; every date they go on leads to passionate soul-burning love that never ends badly.
And imagine your job is to advise this entrepreneur. The only advice worth giving would be “do more stuff”. Clearly all the stuff they’re doing works, so aim higher, work harder, run for President. Another way of saying this is “be more self-confident” – if they’re doubting whether or not to start a new project, remind them that 100% of the things they’ve ever done have been successful, odds are pretty good this new one will too, and they should stop wasting their time second-guessing themselves.
Now imagine the world’s most unsuccessful entrepreneur. Every company they make flounders and dies. Every stock they pick crashes the next day. Their vacations always get rained-out, their dates always end up with the other person leaving halfway through and sticking them with the bill.
What if your job is advising this guy? If they’re thinking of starting a new company, your advice is “Be really careful – you should know it’ll probably go badly”. If they’re thinking of going on a date, you should warn them against it unless they’re really sure. A good global suggestion might be to aim lower, go for low-risk-low-reward steady payoffs, and wait on anything risky until they’ve figured themselves out a little bit more.
Corlett, Frith and Fletcher linked mania to increased confidence. But mania looks a lot like being happy. And you’re happy when you succeed a lot. And when you succeed a lot, maybe having increased confidence is the way to go. If happiness were a sort of global filter that affected all your thought processes and said “These are good times, you should press really hard to exploit your apparent excellence and not worry too much about risk”, that would be pretty evolutionarily useful. Likewise, if sadness were a way of saying “Things are going pretty badly, maybe be less confidence and don’t start any new projects”, that would be useful too.
Depression isn’t normal sadness. But if normal sadness lowers neural confidence a little, maybe depression is the pathological result of biological processes that lower neural confidence. To give a total fake example which I’m not saying is what actually happens, if you run out of whatever neurotransmitter you use to signal high confidence, that would give you permanent pathological low confidence and might look like depression.
One problem with this theory is the time course. Sure, if you’re eternally successful, you should raise your confidence. But eternally successful people are rarely eternally happy. If we’re thinking of happiness-as-felt-emotion,itt seems more like they’re happy for a few hours after they win an award or make their first million or whatever, then go back down to baseline. I’m not sure it makes sense to start lots of new projects in the hour after you win an award.
One way of resolving this: maybe happiness is the derivative of neural confidence? It’s the feeling of your confidence levels increasing, the same way acceleration is the feeling of your speed increasing?
Of course, that’s three layers of crackpot – its own layer, under the layer of emotions as confidence level, under the layer of depression as change in prediction strategies. Maybe I should dial back my own confidence levels and stop there.
This is the bi-weekly visible open thread. Post about anything you want, ask random questions, whatever. You can also talk at the SSC subreddit or the SSC Discord server. Also:
1. New sidebar ad for Relationship Hero, a phone-in help line for social interaction related questions. Liron Shapira – whom many of you probably know from Quixey/CFAR/etc – is a co-founder, which makes me think they’re probably pretty reasonable and above-board.
2. In fact, thanks to everyone who’s emailed me about sidebar ads recently. I’m trying to walk a careful line here, where I’m neither so selective that it looks like I’m endorsing them, nor so unselective that actually bad or scammy companies make it in. If you ever feel like I’m erring on one side or the other, let me know.
3. Several good comments from last week’s thread on developmental genetics vs. evolutionary psychology. See eg Sam Reuben on how different animals implement instincts, TheRadicalModerate on the connectome, and Catherio on how across different individual animals, novel concepts seem to always get encoded in the same brain areas for some reason. Several people also brought up claims that some animals seem innately afraid of eg snakes, or innately susceptible to learning those fears, suggesting that genetics has managed to find a way to connect to the concept “snake” somehow. But it confuses me that this can be true at the same time as eg the experiment where kittens were raised in an artificial environment with no horizontal lines and weren’t able to see horizontal lines when grown up. I know there’s a difference between having a hard-coded concept and having a biased ability to learn a concept, and I know it makes sense that some hard-coded-ish concepts might need data before they “activate”, but it still seems weird to both have “snake” hard-coded enough to produce behavioral consequences, and “horizontal line” so un-hard-coded that you just might not learn it.
(also weird: trap innocent kittens in a freaky bizarro-dimension without horizontal lines and you win a Nobel, but try to give people one fricking questionnaire…)
[Epistemic status: I guess instincts clearly exist, so take this post more as an expression of confusion than as a claim that they don’t.]
Predictive processing isn’t necessarily blank-slatist. But its focus on building concepts out of attempts to generate/predict sense data poses a problem for theories of innate knowledge. PP is more comfortable with deviations from a blank slate that involve the rules of cognition than with those that involve the contents of cognition.
For example, the theory shouldn’t mind the existence of genes for IQ. If the brain works on Bayesian math, some brains might be able to do the calculations more effectively than others. It shouldn’t even mind claims like “girls are more emotional than boys” – that’s just a question of how different hormones affect the Bayesian weighting of logical vs. emotional input.
But evolutionary psychologists make claims like “Men have been evolutionarily programmed to like women with big breasts, because those are a sign of fertility.” Forget for a second whether this is politically correct, or cross-culturally replicable, or anything like that. From a neurological point of view, how could this possibly work?
In Clark’s version of PP, infants laboriously construct all their priors out of sensory evidence. Object permanence takes months. Sensory coordination – the belief that eg the auditory and visual streams describe the same world, so that the same object might be both visible and producing sound – is not assumed. Clark even flirts with the possibility that some really basic assumptions might be learned:
Plausibly, it is only because the world we encounter must be parsed for action and intervention that we encounter, in experience, a relatively unambiguous determinate world at all. Subtract the need for action and the broadly Bayesian framework can seem quite at odds with the phenomenal facts about conscious perceptual experience: our world, it might be said, does not look as if it is encoded in an intertwined set of probability density distributions. Instead, it looks unitary and, on a clear day, unambiguous…biological systems, as mentioned earlier, may be informed by a variety of learned or innate “hyperpriors” concerning the general nature of the world. One such hyperprior might be that the world is usually in one determinate state or another.
I realize he’s not coming out and saying that maybe babies see the world as a probability distribution over hypotheses and only gradually “figure out” that a determinate world is more pragmatic. But he’s sure coming closer to saying that than anybody else I know.
In any case, we work up from these sorts of deep hyperpriors to testing out new models and ideas. Presumably we eventually gain concepts like “breast” after a lot of trial-and-error in which we learn that they generate successful predictions about the sensory world.
In this model, the evolutionary psychological theory seems like a confusion of levels. How do our genes reach out and grab this particular high-level category in the brain, “breast”, to let us know that we’re programmed to find it attractive?
To a first approximation, all a gene does is code for a protein. How, exactly, do you design a protein that makes men find big-breasted women attractive? I mean, I can sort of imagine that if you know what neurons carry the concept of “breast”, you can sort of wire them up to whatever region of the hypothalamus handles sexual attraction, so that whenever you see breasts you feel attraction. But number one, are you sure there’s a specific set of neurons that carry the concept “breast”? And number two, how do you get those neurons (and no others) to express a certain gene?
And if you want to posit an entire complicated breast-locating system made up of hundreds of genes, remember that we only have about 20,000 genes total. Most of these are already involved in doing things like making the walls of lysosomes flexible enough or something really boring like that. Really it’s a miracle that a mere 20,000 genes can make a human at all. So how many of these precious resources do you want to take up constructing some kind of weird Rube-Goldbergesque breast-related brain circuit?
The only excuse I can think of for the evo psych perspective is that it obviously works sometimes. Animals do have instincts; it can’t be learning all the way down.
Sometimes when we really understand those instincts, they do look like weird Rube Goldberg contraptions made of brain circuits. The classic example is baby gulls demanding food from their mother. Adult gulls have a red dot on their beaks, and the baby bird algorithm seems to be “The first thing you see with a red dot is your mother; demand food from her.” Maybe “red dot” is primitive enough that it’s easier to specify genetically than “thing that looks like a mother bird”?
The clearest example I can think of where animals clearly have an instinctive understanding of a high level concept is sex/gender – a few gay humans and penguins aside, Nature seems pretty good at keeping its creatures heterosexual. But this is one of the rare cases where evolution might really want to devote some big fraction of the 20,000 genes it has to work with to building a Rube Goldberg circuit.
Also, maybe we shouldn’t set those few gender-nonconforming humans aside. Remember, autistic people have some kind of impairment in top-down prior-based processing relative to the bottom-up evidence-based kind, and they’re about eight times more likely to be trans than the general population. It sure looks like there’s some kind of process in which people have to infer their gender. And even though evolution seems to be shouting some really loud hints, maybe if you weigh streams of evidence in unusual ways you can end up somewhere unexpected. Evolution may be able to bias the process or control its downstream effects, but it doesn’t seem able to literally hard-code it.
Someone once asked me how to distinguish between good and bad evolutionary psychology. One heuristic might be to have a strong prior against any claim in which genes can just reach into the level of already-formed concepts and tweak them around, unless there’s a really strong reason for evolution to go through a lot of trouble to make it happen.
Yesterday’s review of Surfing Uncertainty mentioned how predictive processing attributes movement to strong predictions about proprioceptive sensations. Because the brain tries to minimize predictive error, it moves the limbs into the positions needed to produce those sensations, fulfilling its own prophecy.
This was a really difficult concept for me to understand at first. But there were a couple of passages that helped me make an important connection. See if you start thinking the same thing I’m thinking:
To make [bodily] action come about, the motor plant behaves (Friston, Daunizeau, et al, 2010) in ways that cancel out proprioceptive prediction errors. This works because the proprioceptive prediction errors signal the difference between how the bodily plant is currently disposed and how it would be disposed were the desired actions being performed. Proprioceptive prediction error will yield (moment-by-moment) the projected proprioceptive inputs. In this way, predictions of the unfolding proprioceptive patterns that would be associated with the performance of some action actually bring that action about. This kind of scenario is neatly captured by Hawkins and Blakeslee (2004), who write that: “As strange as it sounds, when your own behavior is involved, your predictions not only precede sensation, they determine sensation.”
PP thus implements the distinctive circular dynamics described by Cisek and Kalaska using a famous quote from the American pragmatist John Dewey. Dewey rejects the ‘passive’ model of stimuli evoking responses in favour of an active and circular model in which ‘the motor response determines the stimulus, just as truly as sensory stimulus determines movement’
Still not getting it? What about:
According to active inference, the agent moves body and sensors in ways that amount to actively seeking out the sensory consequences that their brains expect.
Clark knows this. A few pages after all these quotes, he writes:
One signature of this kind of grip-based non-reconstructive dance is that it suggests a potent reversal of our ordinary way of thinking about the relations between perception and action. Instead of seeing perception as the control of action, it becomes fruitful to think of action as the control of perception [Powers 1973, Powers et al, 2011].
But I feel like this connection should be given more weight. Powers’ perceptual control theory presages predictive processing theory in a lot of ways. In particular, both share the idea of cogntitive “layers”, which act at various levels (light-intensity-detection vs. edge-detection vs. object-detection, or movements vs. positions-in-space vs. specific-muscle-actions vs. specific-muscle-fiber-tensions). Upper layers decide what stimuli they want lower levels to be perceiving, and lower layers arrange themselves in the way that produce those stimuli. PCT talks about “set points” for cybernetic systems, and PP talks about “predictions”, but they both seem to be groping at the same thing.
I was least convinced by the part of PCT which represented the uppermost layers of the brain as control systems controlling various quantities like “love” or “communism”, and which sometimes seemed to veer into self-parody. PP offers an alternative by describing those layers as making predictions (sometimes “active predictions” of the sort that guide behavior) and trying to minimize predictive error. This allows lower level systems to “control for” deviation from a specific plan, rather than just monitoring the amount of some scalar quantity.
My review of Behavior: The Control Of Perception ended by saying:
It does seem like there’s something going on where my decision to drive activates a lot of carefully-trained subsystems that handle the rest of it automatically, and that there’s probably some neural correlate to it. But I don’t know whether control systems are the right way to think about this… I think maybe there are some obvious parallels, maybe even parallels that bear fruit in empirical results, in lower level systems like motor control. Once you get to high-level systems like communism or social desirability, I’m not sure we’re doing much better than [strained control-related metaphors].
I think my instincts were right. PCT is a good model, but what’s good about it is that it approximates PP. It approximates PP best at the lower levels, and so is most useful there; its thoughts on the higher levels remain useful but start to diverge and so become less profound.
The Greek atomists like Epicurus have been totally superseded by modern atomic theory, but they still get a sort of “how did they do that?” award for using vague intuition and good instincts to cook up a scientific theory that couldn’t be proven or universally accepted until centuries later. If PP proves right, then Will Powers and PCT deserve a place in the pantheon besides them. There’s something kind of wasteful about this – we can’t properly acknowledge the cutting-edgeness of their contribution until it’s obsolete – but at the very least we can look through their other work and see if they’ve got even more smart ideas that might be ahead of their time.
(Along with his atomic theory, Epicurus gathered a bunch of philosophers and mathematicians into a small cult around him, who lived together in co-ed group houses preaching atheism and materialism and – as per the rumors – having orgies. If we’d just agreed he was right about everything from the start, we wouldn’t have had to laboriously reinvent his whole system.)
Sometimes I have the fantasy of being able to glut myself on Knowledge. I imagine meeting a time traveler from 2500, who takes pity on me and gives me a book from the future where all my questions have been answered, one after another. What’s consciousness? That’s in Chapter 5. How did something arise out of nothing? Chapter 7. It all makes perfect intuitive sense and is fully vouched by unimpeachable authorities. I assume something like this is how everyone spends their first couple of days in Heaven, whatever it is they do for the rest of Eternity.
And every so often, my fantasy comes true. Not by time travel or divine intervention, but by failing so badly at paying attention to the literature that by the time I realize people are working on a problem it’s already been investigated, experimented upon, organized into a paradigm, tested, and then placed in a nice package and wrapped up with a pretty pink bow so I can enjoy it all at once.
The predictive processing model is one of these well-wrapped packages. Unbeknownst to me, over the past decade or so neuroscientists have come up with a real theory of how the brain works – a real unifying framework theory like Darwin’s or Einstein’s – and it’s beautiful and it makes complete sense.
Surfing Uncertainty isn’t pop science and isn’t easy reading. Sometimes it’s on the border of possible-at-all reading. Author Andy Clark (a professor of logic and metaphysics, of all things!) is clearly brilliant, but prone to going on long digressions about various esoteric philosophy-of-cognitive-science debates. In particular, he’s obsessed with showing how “embodied” everything is all the time. This gets kind of awkward, since the predictive processing model isn’t really a natural match for embodiment theory, and describes a brain which is pretty embodied in some ways but not-so-embodied in others. If you want a hundred pages of apologia along the lines of “this may not look embodied, but if you squint you’ll see how super-duper embodied it really is!”, this is your book.
It’s also your book if you want to learn about predictive processing at all, since as far as I know this is the only existing book-length treatment of the subject. And it’s comprehensive, scholarly, and very good at giving a good introduction to the theory and why it’s so important. So let’s be grateful for what we’ve got and take a look.
Stanislas Dehaene writes of our senses:
We never see the world as our retina sees it. In fact, it would be a pretty horrible sight: a highly distorted set of light and dark pixels, blown up toward the center of the retina, masked by blood vessels, with a massive hole at the location of the “blind spot” where cables leave for the brain; the image would constantly blur and change as our gaze moved around. What we see, instead, is a three-dimensional scene, corrected for retinal defects, mended at the blind spot, stabilized for our eye and head movements, and massively reinterpreted based on our previous experience of similar visual scenes. All these operations unfold unconsciously—although many of them are so complicated that they resist computer modeling. For instance, our visual system detects the presence of shadows in the image and removes them. At a glance, our brain unconsciously infers the sources of lights and deduces the shape, opacity, reflectance, and luminance of the objects.
Predictive processing begins by asking: how does this happen? By what process do our incomprehensible sense-data get turned into a meaningful picture of the world?
The key insight: the brain is a multi-layer prediction machine. All neural processing consists of two streams: a bottom-up stream of sense data, and a top-down stream of predictions. These streams interface at each level of processing, comparing themselves to each other and adjusting themselves as necessary.
The bottom-up stream starts out as all that incomprehensible light and darkness and noise that we need to process. It gradually moves up all the cognitive layers that we already knew existed – the edge-detectors that resolve it into edges, the object-detectors that shape the edges into solid objects, et cetera.
The top-down stream starts with everything you know about the world, all your best heuristics, all your priors, everything that’s ever happened to you before – everything from “solid objects can’t pass through one another” to “e=mc^2” to “that guy in the blue uniform is probably a policeman”. It uses its knowledge of concepts to make predictions – not in the form of verbal statements, but in the form of expected sense data. It makes some guesses about what you’re going to see, hear, and feel next, and asks “Like this?” These predictions gradually move down all the cognitive layers to generate lower-level predictions. If that uniformed guy was a policeman, how would that affect the various objects in the scene? Given the answer to that question, how would it affect the distribution of edges in the scene? Given the answer to that question, how would it affect the raw-sense data received?
Both streams are probabilistic in nature. The bottom-up sensory stream has to deal with fog, static, darkness, and neural noise; it knows that whatever forms it tries to extract from this signal might or might not be real. For its part, the top-down predictive stream knows that predicting the future is inherently difficult and its models are often flawed. So both streams contain not only data but estimates of the precision of that data. A bottom-up percept of an elephant right in front of you on a clear day might be labelled “very high precision”; one of a a vague form in a swirling mist far away might be labelled “very low precision”. A top-down prediction that water will be wet might be labelled “very high precision”; one that the stock market will go up might be labelled “very low precision”.
As these two streams move through the brain side-by-side, they continually interface with each other. Each level receives the predictions from the level above it and the sense data from the level below it. Then each level uses Bayes’ Theorem to integrate these two sources of probabilistic evidence as best it can. This can end up a couple of different ways.
First, the sense data and predictions may more-or-less match. In this case, the layer stays quiet, indicating “all is well”, and the higher layers never even hear about it. The higher levels just keep predicting whatever they were predicting before.
Second, low-precision sense data might contradict high-precision predictions. The Bayesian math will conclude that the predictions are still probably right, but the sense data are wrong. The lower levels will “cook the books” – rewrite the sense data to make it look as predicted – and then continue to be quiet and signal that all is well. The higher levels continue to stick to their predictions.
Third, there might be some unresolvable conflict between high-precision sense-data and predictions. The Bayesian math will indicate that the predictions are probably wrong. The neurons involved will fire, indicating “surprisal” – a gratuitiously-technical neuroscience term for surprise. The higher the degree of mismatch, and the higher the supposed precision of the data that led to the mismatch, the more surprisal – and the louder the alarm sent to the higher levels.
When the higher levels receive the alarms from the lower levels, this is their equivalent of bottom-up sense-data. They ask themselves: “Did the even-higher-levels predict this would happen?” If so, they themselves stay quiet. If not, they might try to change their own models that map higher-level predictions to lower-level sense data. Or they might try to cook the books themselves to smooth over the discrepancy. If none of this works, they send alarms to the even-higher-levels.
All the levels really hate hearing alarms. Their goal is to minimize surprisal – to become so good at predicting the world (conditional on the predictions sent by higher levels) that nothing ever surprises them. Surprise prompts a frenzy of activity adjusting the parameters of models – or deploying new models – until the surprise stops.
All of this happens several times a second. The lower levels constantly shoot sense data at the upper levels, which constantly adjust their hypotheses and shoot them down at the lower levels. When surprise is registered, the relevant levels change their hypotheses or pass the buck upwards. After umpteen zillion cycles, everyone has the right hypotheses, nobody is surprised by anything, and the brain rests and moves on to the next task. As per the book:
To deal rapidly and fluently with an uncertain and noisy world, brains like ours have become masters of prediction – surfing the waves and noisy and ambiguous sensory stimulation by, in effect, trying to stay just ahead of them. A skilled surfer stays ‘in the pocket’: close to, yet just ahead of the place where the wave is breaking. This provides power and, when the wave breaks, it does not catch her. The brain’s task is not dissimilar. By constantly attempting to predict the incoming sensory signal we become able – in ways we shall soon explore in detail – to learn about the world around us and to engage that world in thought and action.
The result is perception, which the PP theory describes as “controlled hallucination”. You’re not seeing the world as it is, exactly. You’re seeing your predictions about the world, cashed out as expected sensations, then shaped/constrained by the actual sense data.
Enough talk. Let’s give some examples. Most of you have probably seen these before, but it never hurts to remind:
This demonstrates the degree to which the brain depends on top-down hypotheses to make sense of the bottom-up data. To most people, these two pictures start off looking like incoherent blotches of light and darkness. Once they figure out what they are (spoiler) the scene becomes obvious and coherent. According to the predictive processing model, this is how we perceive everything all the time – except usually the concepts necessary to make the scene fit together come from our higher-level predictions instead of from clicking on a spoiler link.
This demonstrates how the top-down stream’s efforts to shape the bottom-up stream and make it more coherent can sometimes “cook the books” and alter sensation entirely. The real picture says “PARIS IN THE THE SPRINGTIME” (note the duplicated word “the”!). The top-down stream predicts this should be a meaningful sentence that obeys English grammar, and so replaces the the bottom-up stream with what it thinks that it should have said. This is a very powerful process – how many times have I repeated the the word “the” in this paragraph alone without you noticing?
A more ambiguous example of “perception as controlled hallucination”. Here your experience doesn’t quite deny the jumbled-up nature of the letters, but it superimposes a “better” and more coherent experience which appears naturally alongside.
Next up – this low-quality video of an airplane flying at night. Notice how after an instant, you start to predict the movement and characteristics of the airplane, so that you’re no longer surprised by the blinking light, the movement, the other blinking light, the camera shakiness, or anything like that – in fact, if the light stopped blinking, you would be surprised, even though naively nothing could be less surprising than a dark portion of the night sky staying dark. After a few seconds of this, the airplane continuing on its (pretty complicated) way just reads as “same old, same old”. Then when something else happens – like the camera panning out, or the airplane making a slight change in trajectory – you focus entirely on that, the blinking lights and movement entirely forgotten or at least packed up into “airplane continues on its blinky way”. Meanwhile, other things – like the feeling of your shirt against your skin – have been completely predicted away and blocked from consciousness, freeing you to concentrate entirely on any subtle changes in the airplane’s motion.
In the same vein: this is Rick Astley’s “Never Going To Give You Up” repeated again and again for ten hours (you can find some weird stuff on YouTube). The first hour, maybe you find yourself humming along occasionally. By the second hour, maybe it’s gotten kind of annoying. By the third hour, you’ve completely forgotten it’s even on at all.
But suppose that one time, somewhere around the sixth hour, it skipped two notes – just the two syllables “never”, so that Rick said “Gonna give you up.” Wouldn’t the silence where those two syllables should be sound as jarring as if somebody set off a bomb right beside you? Your brain, having predicted sounds consistent with “Never Gonna Give You Up” going on forever, suddenly finds its expectations violated and sends all sorts of alarms to the higher levels, where they eventually reach your consciousness and make you go “What the heck?”
Okay. You’ve read a lot of words. You’ve looked at a lot of pictures. You’ve listened to “Never Gonna Give You Up” for ten hours. Time for the payoff. Let’s use this theory to explain everything.
1. Attention. In PP, attention measures “the confidence interval of your predictions”. Sense-data within the confidence intervals counts as a match and doesn’t register surprisal. Sense-data outside the confidence intervals fails and alerts higher levels and eventually consciousness.
This modulates the balance between the top-down and bottom-up streams. High attention means that perception is mostly based on the bottom-up stream, since every little deviation is registering an error and so the overall perceptual picture is highly constrained by sensation. Low attention means that perception is mostly based on the top-down stream, and you’re perceiving only a vague outline of the sensory image with your predictions filling in the rest.
There’s a famous experiment which you can try below – if you’re trying it, make sure to play the whole video before moving on:
About half of subjects, told to watch the players passing the ball, don’t notice the gorilla. Their view of the ball-passing is closely constrained by the bottom-up stream; they see mostly what is there. But their view of the gorilla is mostly dependent on the top-down stream. Their confidence intervals are wide. Somewhere in your brain is a neuron saying “is that a guy in a gorilla suit?” Then it consults the top-down stream, which says “This is a basketball game, you moron”, and it smooths out the anomalous perception into something that makes sense like another basketball player.
But if you watch the video with the prompt “Look for something strange happening in the midst of all this basketball-playing”, you see the gorilla immediately. Your confidence intervals for unusual things are razor-thin; as soon as that neuron sees the gorilla it sends alarms to higher levels, and the higher levels quickly come up with a suitable hypothesis (“there’s a guy in a gorilla suit here”) which makes sense of the new data.
There’s an interesting analogy to vision here, where the center of your vision is very clear, and the outsides are filled in in a top-down way – I have a vague sense that my water bottle is in the periphery right now, but only because I kind of already know that, and it’s more of a mental note of “water bottle here as long as you ask no further questions” than a clear image of it. The extreme version of this is the blind spot, which gets filled in entirely with predicted imagery despite receiving no sensation at all.
2. Imagination, Simulation, Dreaming, Etc. Imagine a house. Now imagine a meteor crashing into the house. Your internal mental simulation was probably pretty good. Without even thinking about it, you got it to obey accurate physical laws like “the meteor continues on a constant trajectory”, “the impact happens in a realistic way”, “the impact shatters the meteorite”, and “the meteorite doesn’t bounce back up to space like a basketball”. Think how surprising this is.
In fact, think how surprising it is that you can imagine the house at all. This really high level concept – “house” – has been transformed in your visual imaginarium into a pretty good picture of a house, complete with various features, edges, colors, et cetera (if it hasn’t, read here). This is near-miraculous. Why do our brains have this apparently useless talent?
PP says that the highest levels of our brain make predictions in the form of sense data. They’re not just saying “I predict that guy over there is a policeman”, they’re generating the image of a policeman, cashing it out in terms of sense data, and colliding it against the sensory stream to see how it fits. The sensory stream gradually modulates it to fit the bottom-up evidence – a white or black policeman, a mustached or clean-shaven policeman. But the top-down stream is doing a lot of the work here. We are able to imagine the meteor, using the same machinery that would guide our perception of the meteor if we saw it up in the sky.
All of this goes double for dreaming. If “perception is controlled hallucination” caused by the top-down drivers of perception constrained by bottom-up evidence, then dreams are those top-down drivers playing around with themselves unconstrained by anything at all (or else very weakly constrained by bottom-up evidence, like when it’s really cold in your bedroom and you dream you’re exploring the North Pole).
A lot of people claim higher levels of this – lucid dreaming, astral projection, you name it, worlds exactly as convincing as our own but entirely imaginary. Predictive processing is very sympathetic to these accounts. The generative models that create predictions are really good; they can simulate the world well enough that it rarely surprises us. They also connect through various layers to our bottom-level perceptual apparatus, cashing out their predictions in terms of the lowest-level sensory signals. Given that we’ve got a top-notch world-simulator plus perception-generator in our heads, it shouldn’t be surprising when we occasionally perceive ourselves in simulated worlds.
3. Priming. I don’t mean the weird made-up kinds of priming that don’t replicate. I mean the very firmly established ones, like the one where, if you flash the word “DOCTOR” at a subject, they’ll be much faster and more skillful in decoding a series of jumbled and blurred letters into the word “NURSE”.
This is classic predictive processing. The top-down stream’s whole job is to assist the bottom-up stream in making sense of complicated fuzzy sensory data. After it hears the word “DOCTOR”, the top-down stream is already thinking “Okay, so we’re talking about health care professionals”. This creeps through all the lower levels as a prior for health-care related things; when the sense organs receive data that can be associated in a health-care related manner, the high prior helps increase the precision of this possibility until it immediately becomes the overwhelming leading hypothesis.
4. Learning. There’s a philosophical debate – which I’m not too familiar with, so sorry if I get it wrong – about how “unsupervised learning” is possible. Supervised reinforcement learning is when an agent tries various stuff, and then someone tells the agent if it’s right or wrong. Unsupervised learning is when nobody’s around to tell you, and it’s what humans do all the time.
PP offers a compelling explanation: we create models that generate sense data, and keep those models if the generated sense data match observation. Models that predict sense data well stick around; models that fail to predict the sense data accurately get thrown out. Because of all those lower layers adjusting out contingent features of the sensory stream, any given model is left with exactly the sense data necessary to tell it whether it’s right or wrong.
PP isn’t exactly blank slatist, but it’s compatible with a slate that’s pretty fricking blank. Clark discusses “hyperpriors” – extremely basic assumptions about the world that we probably need to make sense of anything at all. For example, one hyperprior is sensory synchronicity – the idea that our five different senses are describing the same world, and that the stereo we see might be the source of the music we hear. Another hyperprior is object permanence – the idea that the world is divided into specific objects that stick around whether or not they’re in the sensory field. Clark says that some hyperpriors might be innate – but says they don’t have to be, since PP is strong enough to learn them on its own if it has to. For example, after enough examples of, say, seeing a stereo being smashed with a hammer at the same time that music suddenly stops, the brain can infer that connecting the visual and auditory evidence together is a useful hack that helps it to predict the sensory stream.
I can’t help thinking here of Molyneux’s Problem, a thought experiment about a blind-from-birth person who navigates the world through touch alone. If suddenly given sight, could the blind person naturally connect the visual appearance of a cube to her own concept “cube”, which she derived from the way cubes feel? In 2003, some researchers took advantage of a new cutting-edge blindness treatment to test this out; they found that no, the link isn’t intuitively obvious to them. Score one for learned hyperpriors.
But learning goes all the way from these kinds of really basic hyperpriors all the way up to normal learning like what the capital of France is – which, if nothing else, helps predict what’s going to be on the other side of your geography flashcard, and which high-level systems might keep as a useful concept to help it make sense of the world and predict events.
5. Motor Behavior. About a third of Surfing Uncertainty is on the motor system, it mostly didn’t seem that interesting to me, and I don’t have time to do it justice here (I might make another post on one especially interesting point). But this has been kind of ignored so far. If the brain is mostly just in the business of making predictions, what exactly is the motor system doing?
Based on a bunch of really excellent experiments that I don’t have time to describe here, Clark concludes: it’s predicting action, which causes the action to happen.
This part is almost funny. Remember, the brain really hates prediction error and does its best to minimize it. With failed predictions about eg vision, there’s not much you can do except change your models and try to predict better next time. But with predictions about proprioceptive sense data (ie your sense of where your joints are), there’s an easy way to resolve prediction error: just move your joints so they match the prediction. So (and I’m asserting this, but see Chapters 4 and 5 of the book to hear the scientific case for this position) if you want to lift your arm, your brain just predicts really really strongly that your arm has been lifted, and then lets the lower levels’ drive to minimize prediction error do the rest.
Under this model, the “prediction” of a movement isn’t just the idle thought that a movement might occur, it’s the actual motor program. This gets unpacked at all the various layers – joint sense, proprioception, the exact tension level of various muscles – and finally ends up in a particular fluid movement:
Friston and colleagues…suggest that precise proprioceptive predictions directly elicit motor actions. This means that motor commands have been replaced by (or as I would rather say, implemented by) proprioceptive predictions. According to active inference, the agent moves body and sensors in ways that amount to actively seeking out the sensory consequences that their brains expect. Perception, cognition, and action – if this unifying perspective proves correct – work together to minimize sensory prediction errors by selectively sampling and actively sculpting the stimulus array. This erases any fundamental computational line between perception and the control of action. There remains [only] an obvious difference in direction of fit. Perception here matches hural hypotheses to sensory inputs…while action brings unfolding proprioceptive inputs into line with neural predictions. The difference, as Anscombe famously remarked, is akin to that between consulting a shopping list (thus letting the list determine the contents of the shopping basket) and listing some actually purchased items (thus letting the contents of the shopping basket determine the list). But despite the difference in direction of fit, the underlying form of the neural computations is now revealed as the same.
6. Tickling Yourself. One consequence of the PP model is that organisms are continually adjusting out their own actions. For example, if you’re trying to predict the movement of an antelope you’re chasing across the visual field, you need to adjust out the up-down motion of your own running. So one “hyperprior” that the body probably learns pretty early is that if it itself makes a motion, it should expect to feel the consequences of that motion.
There’s a really interesting illusion called the force-matching task. A researcher exerts some force against a subject, then asks the subject to exert exactly that much force against something else. Subjects’ forces are usually biased upwards – they exert more force than they were supposed to – probably because their brain’s prediction engines are “cancelling out” their own force. Clark describes one interesting implication:
The same pair of mechanisms (forward-model-based prediction and the dampening of resulting well-predicted sensation) have been invoked to explain the unsettling phenomenon of ‘force escalation’. In force escalation, physical exchanges (playground fights being the most common exemplar) mutually ramp up via a kind of step-ladder effect in which each person believes the other one hit them harder. Shergill et al describe experiments that suggest that in such cases each person is truthfully reporting their own sensations, but that those sensations are skewed by the attenuating effects of self-prediction. Thus, ‘self-generated forces are perceived as weaker than externally generated forces of equal magnitude.’
This also explains why you can’t tickle yourself – your body predicts and adjusts away your own actions, leaving only an attenuated version.
7. The Placebo Effect. We hear a lot about “pain gating” in the spine, but the PP model does a good job of explaining what this is: adjusting pain based on top-down priors. If you believe you should be in pain, the brain will use that as a filter to interpret ambiguous low-precision pain signals. If you believe you shouldn’t, the brain will be more likely to assume ambiguous low-precision pain signals are a mistake. So if you take a pill that doctors assure you will cure your pain, then your lower layers are more likely to interpret pain signals as noise, “cook the books” and prevent them from reaching your consciousness.
Psychosomatic pain is the opposite of this; see Section 7.10 of the book for a fuller explanation.
8. Asch Conformity Experiment. More speculative, and not from the book. But remember this one? A psychologist asked subjects which lines were the same length as other lines. The lines were all kind of similar lengths, but most subjects were still able to get the right answer. Then he put the subjects in a group with confederates; all of the confederates gave the same wrong answer. When the subject’s turn came, usually they would disbelieve their eyes and give the same wrong answer as the confederates.
The bottom-up stream provided some ambiguous low-precision bottom-up evidence pointing toward one line. But in the final Bayesian computation, those were swamped by the strong top-down prediction that it would be another. So the middle layers “cooked the books” and replaced the perceived sensation with the predicted one. From Wikipedia:
Participants who conformed to the majority on at least 50% of trials reported reacting with what Asch called a “distortion of perception”. These participants, who made up a distinct minority (only 12 subjects), expressed the belief that the confederates’ answers were correct, and were apparently unaware that the majority were giving incorrect answers.
9. Neurochemistry. PP offers a way to a psychopharmacological holy grail – an explanation of what different neurotransmitters really mean, on a human-comprehensible level. Previous attempts to do this, like “dopamine represents reward, serotonin represents calmness”, have been so wildly inadequate that the whole question seems kind of disreputable these days.
But as per PP, the NMDA glutamatergic system mostly carries the top-down stream, the AMPA glutamatergic system mostly carries the bottom-up stream, and dopamine mostly carries something related to precision, confidence intervals, and surprisal levels. This matches a lot of observational data in a weirdly consistent way – for example, it doesn’t take a lot of imagination to think of the slow, hesitant movements of Parkinson’s disease as having “low motor confidence”.
10. Autism. Various research in the PP tradition has coalesced around the idea of autism as an unusually high reliance on bottom-up rather than top-down information, leading to “weak central coherence” and constant surprisal as the sensory data fails to fall within pathologically narrow confidence intervals.
Autistic people classically can’t stand tags on clothing – they find them too scratchy and annoying. Remember the example from Part III about how you successfully predicted away the feeling of the shirt on your back, and so manage never to think about it when you’re trying to concentrate on more important things? Autistic people can’t do that as well. Even though they have a layer in their brain predicting “will continue to feel shirt”, the prediction is too precise; it predicts that next second, the shirt will produce exactly the same pattern of sensations it does now. But realistically as you move around or catch passing breezes the shirt will change ever so slightly – at which point autistic people’s brains will send alarms all the way up to consciousness, and they’ll perceive it as “my shirt is annoying”.
Or consider the classic autistic demand for routine, and misery as soon as the routine is disrupted. Because their brains can only make very precise predictions, the slightest disruption to routine registers as strong surprisal, strong prediction failure, and “oh no, all of my models have failed, nothing is true, anything is possible!” Compare to a neurotypical person in the same situation, who would just relax their confidence intervals a little bit and say “Okay, this is basically 99% like a normal day, whatever”. It would take something genuinely unpredictable – like being thrown on an unexplored continent or something – to give these people the same feeling of surprise and unpredictability.
This model also predicts autistic people’s strengths. We know that polygenic risk for autism is positively associated with IQ. This would make sense if the central feature of autism was a sort of increased mental precision. It would also help explain why autistic people seem to excel in high-need-for-precision areas like mathematics and computer programming.
11. Schizophrenia. Converging lines of research suggest this also involves weak priors, apparently at a different level to autism and with different results after various compensatory mechanisms have had their chance to kick in. One especially interesting study asked neurotypicals and schizophrenics to follow a moving light, much like the airplane video in Part III above. When the light moved in a predictable pattern, the neurotypicals were much better at tracking it; when it was a deliberately perverse video specifically designed to frustrate expectations, the schizophrenics actually did better. This suggests that neurotypicals were guided by correct top-down priors about where the light would be going; schizophrenics had very weak priors and so weren’t really guided very well, but also didn’t screw up when the light did something unpredictable. Schizophrenics are also famous for not being fooled by the “hollow mask” (below) and other illusions where top-down predictions falsely constrain bottom-up evidence. My guess is they’d be more likely to see both ‘the’s in the “PARIS IN THE THE SPRINGTIME” image above.
The exact route from this sort of thing to schizophrenia is really complicated, and anyone interested should check out Section 2.12 and the whole of Chapter 7 from the book. But the basic story is that it creates waves of anomalous prediction error and surprisal, leading to the so-called “delusions of significance” where schizophrenics believe that eg the fact that someone is wearing a hat is some sort of incredibly important cosmic message. Schizophrenics’ brains try to produce hypotheses that explain all of these prediction errors and reduce surprise – which is impossible, because the prediction errors are random. This results in incredibly weird hypotheses, and eventually in schizophrenic brains being willing to ignore the bottom-up stream entirely – hence hallucinations.
All this is treated with antipsychotics, which antagonize dopamine, which – remember – represents confidence level. So basically the medication is telling the brain “YOU CAN IGNORE ALL THIS PREDICTION ERROR, EVERYTHING YOU’RE PERCEIVING IS TOTALLY GARBAGE SPURIOUS DATA” – which turns out to be exactly the message it needs to hear.
An interesting corollary of all this – because all of schizophrenics’ predictive models are so screwy, they lose the ability to use the “adjust away the consequences of your own actions” hack discussed in Part 5 of this section. That means their own actions don’t get predicted out, and seem like the actions of a foreign agent. This is why they get so-called “delusions of agency”, like “the government beamed that thought into my brain” or “aliens caused my arm to move just now”. And in case you were wondering – yes, schizophrenics can tickle themselves.
12. Everything else. I can’t possibly do justice to the whole of Surfing Uncertainty, which includes sections in which it provides lucid and compelling PP-based explanations of hallucinations, binocular rivalry, conflict escalation, and various optical illusions. More speculatively, I can think of really interesting connections to things like phantom limbs, creativity (and its association with certain mental disorders), depression, meditation, etc, etc, etc.
The general rule in psychiatry is: if you think you’ve found a theory that explains everything, diagnose yourself with mania and check yourself into the hospital. Maybe I’m not at that point yet – for example, I don’t think PP does anything to explain what mania itself is. But I’m pretty close.
This is a really poor book review of Surfing Uncertainty, because I only partly understood it. I’m leaving out a lot of stuff about the motor system, debate over philosophical concepts with names like “enactivism”, descriptions of how neurons form and unform coalitions, and of course a hundred pages of apologia along the lines of “this may not look embodied, but if you squint you’ll see how super-duper embodied it really is!”. As I reread and hopefully come to understand some of this better, it might show up in future posts.
But speaking of philosophical debates, there’s one thing that really struck me about the PP model.
Voodoo psychology suggests that culture and expectation tyrannically shape our perceptions. Taken to an extreme, objective knowledge is impossible, since all our sense-data is filtered through our own bias. Taken to a very far extreme, we get things like What The !@#$ Do We Know?‘s claim that the Native Americans literally couldn’t see Columbus’ ships, because they had no concept of “caravel” and so the percept just failed to register. This sort of thing tends to end by arguing that science was invented by straight white men, and so probably just reflects straight white maleness, and so we should ignore it completely and go frolic in the forest or something.
Predictive processing is sympathetic to all this. It takes all of this stuff like priming and the placebo effect, and it predicts it handily. But it doesn’t give up. It (theoretically) puts it all on a sound mathematical footing, explaining exactly how much our expectations should shape our reality, and in which ways our expectation should shape our reality. I feel like someone armed with predictive processing and a bit of luck should have been able to predict that placebo effect and basic priming would work, but stereotype threat and social priming wouldn’t. Maybe this is total retrodictive cheating. But I feel like it should be possible.
If this is true, it gives us more confidence that our perceptions should correspond – at least a little – to the external world. We can accept that we may be misreading “PARIS IN THE THE SPRINGTIME” while remaining confident that we wouldn’t misread “PARIS IN THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE THE SPRINGTIME” as containing only one “the”. Top-down processing very occasionally meddles in bottom-up sensation, but (as long as you’re not schizophrenic), it sticks to an advisory role rather than being able to steamroll over arbitrary amounts of reality.
The rationalist project is overcoming bias, and that requires both an admission that bias is possible, and a hope that there’s something other than bias which we can latch onto as a guide. Predictive processing gives us more confidence in both, and helps provide a convincing framework we can use to figure out what’s going on at all levels of cognition.
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