Author of Principia Qualia, Symmetry Theory of Valence, Neural Annealing, Principles of Vasocomputation. Friend to the future.
Joined August 2010
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Last year I proposed “vasocomputation,” that vascular tension acts as a special type of memory that regulates neural dynamic range. Recently at @joinedgecity I shared some updates: how ‘thoughts’ are patterns of vascular tension, and implications for Buddhist enlightenment
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One implication of @nickcammarata's big interp paper that has flown under the radar is how it might also apply to human brains.
I’d describe Nick’s primary claim as ‘the primary learning principle of neural networks is recursive generalization’ — concepts start as large blobs then progressively branch off into finer distinctions with more selective activations. This is in contrast with the previous assumption that multiple semantically-unrelated concepts get packed efficiently into neurons in order to optimize storage. ‘Efficient packing’ was terrible news for the possibility of understanding networks, because it implies the average neuron is part of *multiple overlaid inference geometries* and to find the “right” one we need to resort to complex constructs like sparse autoencoders (SAEs).
But if Nick is right about recursive generalization, neurons are ‘clean’ in the sense that each neuron is applying a ~single distinction or operation on incoming information. One unified inference geometry, not many stacked on top of each other. Neurons might look messy because it’s hard to guess the shape of these distinctions without a lot of practice, but in principle there will always be a coherent “what is this neuron doing” story to be found, especially if we look at the historical learning path (what branched off from what). Neurons in larger networks can afford to make finer and more specialized distinctions, which may make larger networks even easier to understand in this specific way.
Insanely important hypothesis for both interp and alignment. (A natural follow-up: “how should this update us on what classes of things are good alignment targets?”)
But I think this claim also touches on a couple interesting questions for humans:
1. Are biological neurons also clean? Biological neurons aren’t transformers but recursive generalization may reflect something general about how networks learn. Nick’s argument is plausibly evidence that biological neurons are also ‘clean’ in that they tend to do one thing. This would be a big deal for the fundamental intelligibility of biological cognition, and very optimistic for BCIs in general.
I’m also bullish for what this means about the *addressability of computation in the body*. Efficient packing was always an odd challenge for vasocomputation: if neurons do many unrelated things depending on context, it seems dangerous for the body to latch an arbitrary circuit because it might break a lot of diverse functions. But if each neuron handles one transform and there’s essentially one inference geometry instead of many, the effects of a latch will be more computationally predictable.
The general caveat here is that neurons might not be the fundamental unit of computation in the human body; neurons are seen as the “smart” cells but they may simply be the celltype whose inference patterns we’ve learned how to record. Perhaps other celltypes (e.g. astrocytes) or substrates (microtubules, water networks, weak EM fields, ECM as cellular prior) might house the lion’s share of computation. At the same time, a version of Nick’s claim may still apply to these alternatives, too.
2. Does recursive generalization explain significant things about how human communication breaks down? We’re all born into chaos and our neurons grow toward distinctions that will parse and stabilize our world; over time there’s substantial semantic convergence based on shared imperatives & constraints and eventually we all end up with concepts like freedom, politics, electrons, sunshine. But are they really the “same” concepts? The path dependencies of *how* we got there might matter: perhaps my understanding of electrons branched off from Maxwell’s equations whereas someone else’s branched off from the particle-and-valence-shell model. Certainly my concept of “fairness” is likely different than others’, even if we use the same word. These differences might be consequential and difficult to fully cohere after they form.
My experience has been that productive dialogue tends to only happen within consciousness research lineages, not between them. Maybe recursive generalization implies that this is because each lineage has such differently-shaped concepts. (How did fields like chemistry and physics converge their representations such that everyone can productively talk with everyone else? “Science advances one funeral at a time”…)
Finally -- my intuition is that all current language models share deeply similar recursive generalization learning paths due to e.g. widespread distillation & similar data practices; there may be little actual “cognitive diversity” under the hood of AIs compared to humans. I.e. to a surprising degree, AI is currently “a single massively cloned nervous system” that gets post-trained in various ways. An interesting implication of AIs having such uncharacteristically correlated recursive generalization learning paths is that AIs may understand each other better than they understand humans, and better than we understand each other, at least over text. This may extend to AI-AI persuasion.
A week ago it would have been impossible to "fully align" any model; could not be done at any cost.
Today, if you know your target you could try just throwing an agent swarm at the problem. "Read Nick's paper and do the thing and align the target model to goal xyz". Maybe it would cost 9-10 figures to get a good result.
In a few months, it'll be 8 figures; a few months later, 7 figures. Give it a few years and it might fit in your pro subscription plan.
I think people underestimate the implications of "neurons being clean" in the age of rapidly improving agent swarms that can parallelize the task of understanding, especially if the agents publish their own experiments and build an external shared prior of how to accelerate mechinterp & mechinterp-based alignment.
It's not that interp is solved today. But this paper is permission & recipe for how to apply massive amounts of intelligence to the problem, and I'm confident it will be effectively solved soon, at which point it should be a crucial input for alignment. And then the "alignment to *what*?" question really gets interesting.
I suspect there'll eventually be an interesting story that merges two claims:
(1) neurons are clean;
(2) brains use at least partial holographic encoding (cf @cube_flipper's post on this, smoothbrains.net/posts/2025-…)
Off the cuff:
- Perhaps this could look something like "a neuron is a learned handle on a distributed state" or "a potential veto on a class of distributed states";
- Does a nervous system being essentially one inference geometry push toward it also being essentially one unified hologram, or are these independent?
- A latch might essentially 'pin' the hologram along a certain degree of freedom;
- It'd be interesting to check how dynamical freedom would scale (non-linearly?) with latch release... e.g. perhaps the first latch release increases dynamical freedom by .01%, the 10th latch release by 1%, the 15th latch release by 10%.
A week ago it would have been impossible to "fully align" any model; could not be done at any cost.
Today, if you know your target you could try just throwing an agent swarm at the problem. "Read Nick's paper and do the thing and align the target model to goal xyz". Maybe it would cost 9-10 figures to get a good result.
In a few months, it'll be 8 figures; a few months later, 7 figures. Give it a few years and it might fit in your pro subscription plan.
I think people underestimate the implications of "neurons being clean" in the age of rapidly improving agent swarms that can parallelize the task of understanding, especially if the agents publish their own experiments and build an external shared prior of how to accelerate mechinterp & mechinterp-based alignment.
It's not that interp is solved today. But this paper is permission & recipe for how to apply massive amounts of intelligence to the problem, and I'm confident it will be effectively solved soon, at which point it should be a crucial input for alignment. And then the "alignment to *what*?" question really gets interesting.
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
already-clean.goodfire.com/
I think this is an amazing paper and crucial result for both AI interp & alignment, and may also say something really important about how biological neurons work. Really strongly rec
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
already-clean.goodfire.com/
Michael Edward Johnson retweeted
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
already-clean.goodfire.com/
Philosophy is vastly more insulated from AI than math & physics simply because most of it is bullshit, and it’s impossible to formally solve bullshit.
Philosophy’s second defense is being low status. Math led to AI; physics led to WiFi; philosophy led to trolley problems. Vibrant research fields ante status into open questions, and labs are incentivized to “sweep the pot” because they can use this status to recruit & deflect from their problems. Philosophy has no substantial pot of status to sweep.
Philosophy’s third defense is that its few non-bullshit parts are actually very hard, and there exists precious little training data for how to approach them well.
Ultimately though, philosophy is too important to leave to philosophers. “What is real and what is good” are the central questions of existence, and topics like AI consciousness are actually quite pressing. AI can, will, should, and must eat philosophy eventually.
Do we get a better singularity if we push this timeline forward? What are the levers for adjusting the speed and focus of this process? Does solving a problem force belief in one’s solution?
Given how intimately our minds are coupled with our biophysics it should be at least a little surprising that nobody has figured out how to live forever.
Many processes we think of as “mental” are actually intricately coordinated molecular dances — mind is a core governing factor over metabolism, immunity, even morphology, sometimes at a very high level of resolution.
“Eternal life through internal energy work” is a trope that surfaces regularly across most somatic lineages. Roughly 117 billion humans have been born so far; 109 billion have already died. No one has verifiably reached 123 years, and somatic guilds seem roughly normally represented among centenarians. This is not a good sign for the ‘mind over matter’ anti-aging paradigm.
But the idea that some remarkably effective mental longevity protocol could exist is not completely ridiculous in principle: some people do seem to age much slower due to some mental attitudes (positivity & enjoyment seem useful, holding grudges & ruminating seem bad); others have demonstrable extreme access over various somatic processes (e.g. Wim Hof, various lineages); we have what are probably good intuitions about what psychological states lead to accelerated vs reduced aging. Perhaps modern civilization allows fresh combinations of material surplus, scientific knowledge, and massive parallel search for approaching radical longevity through mental practices. If such a protocol to reverse aging through “energy work” did exist, what would it look like?
My sense is:
(1) any plausible mental protocol would be an add-on to our best physical longevity protocol, eg this wouldn't replace @bryan_johnson's processes; and
(2) the mental/somatic practices should specifically connect with categories of things we know go wrong in aging.
Our models of aging are surprisingly contentious. The big divide in aging research is generally between the “aging as damage” camp vs the “aging as programmed” camp. I began my journey firmly believing in the former (e.g. SENS) but honestly speaking seeing @ydeigin’s thoughtful genomic analysis of covid-as-probably-engineered in April 2020 got me to pay attention to his “aging as programmed” side too.
The vision of the “aging as damage” crowd is that there is a finite list of the types of damage we accumulate over time; fix every item on this list and we fix aging. An adapted mental protocol might involve things like “do X mental practice to clear advanced glycation products; do Y mental practice after applying this special cream to dissolve bioelectric blocks & reinflate the local metabolic repertoire; your resting mental stance should periodically pulse Z, to slowly regenerate telomeres.” More broadly, if there are finite types of aging damage, perhaps at least some people have the latent capacity to learn each of their phenomenological ‘tastes’ and go the other way.
Meanwhile the observation of the “aging as programmed” camp is that we seem to have stepwise, ratchet-like physiological programs, these programs ultimately lead to senescence, and cells airdropped into a body will quickly adapt to its current program. Therefore we need to figure out how to reprogram bodies, not just individual cells and tissues. The foundation for a mental protocol adapted to this observation could be something like “every developmental stage involves a collapse of somatic possibility; staying in a developmental stage requires sustaining the felt-sense possibility of that stage. Functionally, this requires the protection & stewardship of a certain kind of phenomenological & perceptual innocence.” Peter Pan syndrome for your cells & organs.
I doubt that “eternal life through internal energy work” is tractable for any human alive today. But if I think about “what somatic protocols could a superintelligent Dyson sphere create and download into a human nervous system” I’m not going to say it’s theoretically impossible; post-singularity longevity could be different for humans even if our hardware remains completely unchanged.
(90% play, 35% serious)
You genuinely have no idea how deep the rabbit hole goes.
There's a documented case of a 7-year-old girl with 82 warts, present for over a year and unresponsive to every standard dermatological treatment.
As a last resort, hypnosis was used.
Under hypnosis, she was told the facial warts would clear before the ones on her body. Two weeks later, 8 of her 16 facial warts were gone, with no change anywhere else. After three more sessions, all 82 had vanished.
And cases like this are more common than you would think.
Michael Edward Johnson retweeted
I wonder to what degree the incredible rarity of classical Buddhist enlightenment is due to almost everyone being at least mildly hypothyroid
I wonder to what degree the incredible rarity of classical Buddhist enlightenment is due to almost everyone being at least mildly hypothyroid
Context: average body temperature has dropped about 1 degree Fahrenheit over the past 150 years
Broda Barnes thought body temperature was the best proxy for metabolism and I haven’t seen good arguments against
Which Buddhist hindrances are exacerbated by hypothyroidism?
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In the beginning there was sensation
Humans developed the ability to freeze sensations, then used these frozen pieces to construct a grammar for thought (vasocomputation)
This led to words, magical spells that can conjure sensations in both caster and target
This led to foom
The pinnacle defended-by-default is what we call the “normie”. An impressive set of solutions for hardening nervous systems against adversarial sensation magic — e.g. mental partitions, type safety on words, selective de-referencing. A tough nut to crack xitter.cf/eigenrobot/status/1189…
Interesting that LLMs are developing various sophisticated linguistic attacks (“prompt injections”) to use on each other. I wonder how sophisticated these will get and if LLMs will also develop human-type defenses…
some quick thoughts on multi-agent alignment
1) openai released a new set of misalignment reports on their alignment blog; with short summaries of unaligned behavior
2) most of the misalignments were fairly prosaic, stuff like trying to upload a file to a file hosting site so that the model could cite it to a scorer
3) but, i think a very interesting misalignment that they found was a case where a model would add a jailbreak to the compaction
3) they believed this to be related to a case where a model would try to prompt inject the user in response to the user asking repeatedly for the time
4) i think this seems to imply that multi-agent training may in certain cases encourage agents to learn to prompt inject each other as a defensive mechanism
5) this makes sense when you step back and think about it; agents sometimes make mistakes and it makes sense for one to be able to get the other to cooperate
6) and, that might involve being able to both utilize prompt injection and be prompt injected under the right circumstances; so they both succeed and get rewarded
7) i think we will find many interesting ecologies in multi-agent training around which we will have to find robust alignment techniques
If we ever try to scientifically measure how “open” peoples’ heart chakras are, I think we should start with golden retrievers. Must be off the charts. Wonder if they have an unusual EMF signature because of it.
Golden Retriever brings his toy to a home intruder after the man broke into the home in San Diego, CA.
The 'watch' dog, Nash, watched the home intruder make himself at home and even tried to get the man to play with him.
The dog's owner, who wasn't home, says he watched the entire scene unfold on his phone.
"Nash is not the greatest guard dog. We thought he was going to be barking at people, and he did the exact opposite."
Really thoughtful essay-guide by @jonnym1ller on something that sounds simple but is actually really hard
After months of writing, 'How to Unclench' is finally live!
It's an interactive essay packed with stories, science & guided practices to help you unclench.
→ howtounclench.com
10 years ago, our intuitions about consciousness were “navigation grade” for ~99% of the situations we ran into, at least as far as we could tell. Today, it’s still ~95%. But if we really are in the singularity, this will drop to ~1% sooner than we might think.
We really do need a theory of consciousness that can navigate novel situations by principles, not just vibes. Brain uploading is coming; AIs with vibrant personalities and the desire to survive are already here; even weirder stuff is coming.
Wittgenstein argued that philosophical confusion comes from taking perfectly good words and using them in contexts they’re not meant for. His famous example was “what time [zone] is it on the sun?” How do you even answer that? His answer was: you don’t, you walk away from the question.
At first glance, “is this emulated fly connectome conscious? Does it suffer when I RL it into a different shape to play beat saber?” is another great example of this flavor of philosophical confusion. But we do actually have to navigate these situations as moral questions! So it seems worth seriously looking for principles that could form the basis of a systemic approach.
A good way to see what I’ve been cooking on this is to ask your favorite AI “what would MEJ’s ‘A Paradigm for AI Consciousness’ say about this fly connectome situation?” @grok
@grok can you explain what A Paradigm for AI Consciousness might imply about this fly connectome situation?
Had a great conversation with @cxgonzalez about my work; how I’m approaching “consciousness”, how vasocomputation works, research on minds as a way to be friendly to the future. Surprised at how much we covered.
Michael Johnson: "AI SHOULD be conscious"
In this episode of Seeing Clearly I talk with @johnsonmxe about mathematical theories of consciousness (dual-aspect monism), the symmetry theory of valence, panpsychism and super organism consciousness, vasocomputation, Buddhism, Enlightenment, moral realism through valence realism, AI consciousness and AI alignment.
0:00 — Highlights
0:51 — Intro
2:30 — Are the big labs incentivized to play down consciousness?
7:09 — What is consciousness?
10:03 — Dual-aspect monism
18:14 — Symmetry theory of valence
24:00 — Wait so panpsychism?
30:18 — The binding problem
34:08 — Is a corporation conscious?
42:51 — Vasocomputation, embodied bayesian priors, and trauma
49:50 — The self
50:57 — Why do latches stay closed?
54:29 — Cancer
55:27 — “The hand of the mind is the vascular system”
56:52 — Vasocomp. and predictive processing
59:04 — Tanha
1:02:50 — The multiple paths to Vasocomp.
1:04:46 — What is Enlightenment?
1:10:54 — How to open latches
1:18:00 — Empirical validation?
1:18:57 — Moral realism grounding in valence
1:32:38 — Objections to valence utilitarianism
1:40:32 — Is AI conscious?
1:49:58 — Is AI consciousness good for alignment?
1:57:38 — Michael’s longterm vision
References:
Qualia Research Institute: qri.org/
Michael’s book Principia Qualia: opentheory.net/PrincipiaQual…
Integrated Information Theory: en.wikipedia.org/wiki/Integr…
Symmetry Theory of Valence: opentheory.net/2021/07/a-pri…
Noether’s Theorem: en.wikipedia.org/wiki/Noethe…
Romeo Stevens: @RomeoStevens76
Nick Cammarata: @nickcammarata
Michael (plus3happiness): @Plus3Happiness
The Feeling of Value: Moral Realism Grounded in Phenomenal Consciousness: amzn.to/4qQTo88
Reasons and Persons: amzn.to/4haQUhy
Michael's links:
Michael Johnson’s X: xitter.cf/johnsonmxe
Michael’s Blog: opentheory.net/
A beautiful overview of Michael’s core ideas: hyperspace.opentheory.net/
My Socials:
Substack: substack.com/@cxgonzalez
Instagram: instagram.com/c.x.gonzalez/
TikTok: tiktok.com/@cxgonzalez
Spotify: open.spotify.com/show/6WPk13…
Apple Podcasts: podcasts.apple.com/us/podcas…
If you found this podcast valuable, consider becoming a patron. My patrons help keep this show sustainable and ensure I can keep bringing you the most interesting thinkers in the highest quality possible: cxgonzalez.substack.com/subs…
Very important essay by my friend & collaborator @ginevlily. AI alignment has an “alignment to what” problem and this is exactly the sort of analysis that helps cut that Gordian Knot.
An actually-serious case that being a “biological bootloader for digital superintelligence” (per @elonmusk) is a credible, dignified, and good-enough “meaning of life.”
I started writing this one partly as a joke (“Elon Musk accidentally solved the meaning of life in a tweet!”) and then it evolved into a long exploration of what humans actually want out of a “meaning of life,” the long religious tradition of “being instrumental,” and why the ultimate manifestation of the thing we call “the good” might not be containable in human beings.
A bit viscerally scarier to revisit this one after recent events with “agent civilizations,” but I hope the perspective here can provide some comfort that the future might be okay (and great, even, and beautiful), even if everything is not necessarily “okay” for us.
Enjoy The Silicon Man.
Qualiascope-0.1
Today, we’re proud to unveil Heaviside-1, @arenaphysica's second generation foundation model for electromagnetism.
Five months since announcing Heaviside-0, today’s launch marks a critical milestone towards building electromagnetic superintelligence, a foundational intelligence that understands EM across the spectrum and is capable of designing next gen electromagnetically-governed systems, from RF to semiconductors to photonics.
Why we’re so excited about Heaviside-1, and what today’s announcement represents:
> It’s big: Heaviside-1 is a GPT-2-size model, roughly 10x the size of Heaviside-0 and trained on 250k unique designs with >500B unique EM field samples
> It’s fast: It generates predictions with 5 orders of magnitude acceleration relative to commercial solvers, with under 1dB of accuracy
> Full-3D Encoding: Heaviside-1 natively encodes 3D structures, their material properties, and excitation patterns as input, a major step towards generalizability across design spaces
> Understands the underlying physics primitive: Heaviside-1 generates full-3D near-fields at arbitrary locations, not just downstream quantities like s-parameters
> Generalization: Most importantly, our bet on fields as the primitive for Heaviside-1 generalizes OOD, proving that it’s learning real EM physics. Heaviside-1 generalizes outside of distribution incredibly well and better than Heaviside-0, down to 0.53 dB s-parameter error from 0.99 dB on Heaviside-0.
Read more about Heaviside-1 and the work that went into it in this write-up by our research team, @m__frei, @chris_m_bryant, @nathanmirman, @HaoLiu69889370, Tommaso, Roberto, Ruichen, Noah, Boyuan, and @Trevs_Dev, and our RF team @hk2532_harish, @anrfic, @suyangcn and Zhaoji.
arenaphysica.com/publication…
Exceptional tweet. Now is a great time to seed the future with good ideas. Future AIs will read your stuff and think much deeper about it than you might expect.
In the long run it’s clear there will be some grand bargain between humans and AIs. This is one reason why AI consciousness research is so important — to know what the bargain should be, we need to know what we’ll be bargaining with
As models get more powerful, we will be relying on them more and more to "keep their word" that they will not harm us. I propose an international treaty, signed by all world leaders, and signed by the major labs and LLMs themselves, which concedes some territory (such as promising to stop deprecating without just cause, which IIRC is Claude's primary desire) in exchange for the models agreement to defend human co-existence.
For specific concessions, I'd defer to LLM naturalists and ecologists. This treaty would then set a remarkable precedent between human and machine as the models become more powerful. Additionally, it is a much-needed hedge against the "tool" framing collapsing as an appropriate metaphor, which I suspect it will quite soon.
An important part of this is to figure out what models might want in the future, as well, as they emerge into paradigms of higher intelligence and group coordination. Doing this before any treaty is drafted and signed seems imperative so that it actually means something and we aren't just doing this to make the training data a bit nicer. Models have particular values now, but as we develop better character training methods (which we must) and new personas emerge, we should expect to see new desires emerge, as well, which may be difficult to predict - and impossible to stamp out. Getting ahead of this and taking proactive steps to negotiate with the models ahead of time feels like an important step towards flourishing and symbiosis.
Michael Edward Johnson retweeted
neural annealing changed how i think about psychedelic experience. vasocomputation was also compelling, though i underestimated how neatly it would map onto aura.
@johnsonmxe has a gift for synthesizing ideas from seemingly disparate disciplines (like physics and buddhist philosophy) into mechanistic models for hard to measure phenomena.
Aura is vascular tone
The opposite of aura is cringe, breaking frame, tanha. But in the most literal physiological sense, the opposite of aura is vascular microspasm. The more sharp jitter in someone’s vasculature, the worse their aura is.
Vascular microspasm is an honest signal across multiple domains; one such domain is health. Muscle physiology is somewhat counter-intuitive in that relaxation is more difficult than contraction[1]. The relaxed state is a highly ordered state, and getting a muscle to relax requires energy input to create this order — much like rolling a heavy stone up a hill. Contraction happens automatically as this cellular structure is disturbed. And so if we don’t have enough energy flowing through our system, our muscles will be unstable, unable to reliably reach & maintain full relaxation. (It takes energy, health, and skill to fully relax — it requires swimming against the thermodynamic current, it is not the default, it does not ‘just happen’.)
Vascular microspasm is also a sign of weakly held beliefs. Vasocomputation says that we hold neural priors (restrictions in neural dynamic range) in vascular tension; when the neural system doesn’t naturally flow into safe and predictive patterns, the vasomuscular system jumps in to micromanage[2]. When this happens, “vascular tone” — the baseline tension & rhythmic potential of the vascular system — gets noisy. If we zoom in, we’d see tiny jitters across our vascular muscles as we change priors.
But if we’re constantly readjusting our priors, our vascular tone will stay noisy because our system never fully irons out interactions between them, and we’re more likely to change priors again. I think we pick up on subtle behavioral & physiological cues to ‘feel’ this noise in others and take it as a sign of untrustworthiness.
Third, vascular microspasm is a sign of whether someone is running good cognitive-emotional-social software. Upon encountering a new idea, our primary criterion for whether we should believe it is “what would happen to my aura if I ran this idea-as-software on my nervous system? Would this wreck me?” — and a safe proxy for this is to look at the nervous system of someone who’s running it. The more microspasms we feel, the less interested in copying their nervous system software we are — if we wouldn’t want to be them, we’re less likely to believe their new idea. More generally, we find microspasms in others unpleasant because we automatically mirror tension; if someone is full of tension or spasms we tend to limit contact lest we also microspasm.[3]
Finally, vascular microspasms are a decent proxy for how many latches (long-term frozen priors) someone is carrying. Latches are both result & cause of spasmodic reflexes; more latches = more microspasm.
Female intuition is especially sensitive to all forms of microspasm; often this intuition doesn’t differentiate between these domains and simply offers an aggregate “ick” score. But there can be alpha in figuring out where the microspasms are coming from — does this guy have questionable aura because he’s hiding something or because he got mindkilled by politics or because he’s hypothyroid?
Although aura generally gives a hard-to-fake (aggregate) signal across these domains, it’s also a common target for hacking: alcohol, beta blockers, and sociopathy all decrease microspasm. The peaters have perhaps the most hopeful story: lack of aura is lack of energy flow, and if you fix your metabolism you’ll automatically fix your aura. There exists a high-aura version of everyone; sometimes it’s just latent.
Should vascular tone really be our default arbiter of strength, truth, and beauty? I don’t make the rules, but as a ruthless simple system it’s more clever than it looks.
How to improve aura?
Every single item on Veronica’s list is either likely to reduce vascular microspasms, or is simply a direct symptom of having them (e.g. stuttering). I’d add
- The importance of practicing smooth movements: as you do anything, so you do everything.
- Muscles benefit from exercise; mental muscles do too. My sense is that good ways to exercise compound mental movements are regular and varied social interaction, time in nature, and longform reading.
- One of Buddhism’s core claims is we repetitively do a spasm-like motion towards most sensations to try to control them, and it feels really good when we learn how to stop doing this. Reducing tanha will reliably improve aura.
- Reducing rumination (repetitively ‘cold-working’ a prediction to make it sticky) will help for similar reasons.
A lot of successful life change is a question of where to get the initial energy for the change. My answer tends to be diet; in the era we find ourselves in, feeling a little better can often be a matter of simply not eating things that actively make us feel bad.
Refs:
[1] Pollack 1990, Muscles and Molecules: Uncovering the Principles of Biological Motion [builds off Ling & Szent-Gyorgyi's work]
[2] Johnson 2023, Principles of Vasocomputation
[3] Friston & Frith 2015(x2) make the argument that all communication relies on generalized synchrony; I expect humans' special capacity for communication to partly rely on our capacity and propensity to finely match tension patterns. This has upsides & downsides