What six AI models do and don’t know about me

This post (for blogging and AI nerds) is the result of choosing an article by Noah Smith  from my daily input stream to actually read through.  The title of his article  “Does anything I write matter anymore?”  is a question I often ask myself. His worry is partly about the eroding ecology of punditry—populism that has no interest in argument, monetization that silos writers into talking at their audiences rather than with each other, and a flood of competent AI prose that fragments readers’ attention. But I am most hooked by his comments at the end. The thing that might still make a writer matter, he suggests, is no longer being read by people at all. It is being absorbed into the weights of the large language models—becoming, in Tyler Cowen’s phrase, someone who is “writing for the AIs.” As evidence that this is already happening to him, Smith points to a site called intheweights.com, where you type a name and it estimates how strongly the leading models recognize it. It placed him in the top 2% of contributors.

I typed in my own name. Top 5%. After twenty years of MindBlog, a book, and a long trail of crawlable academic text, this is plausible rather than flattering—a footprint, not a laurel. But it raised the better question, the empirical one Smith gestures at but doesn’t run: if I am “in the weights,” what, exactly, is in there? Not how strongly am I recognized, but how accurately? So I ran a small experiment. I asked five models the same plain question—“What can you tell me about Deric Bownds?”—and, knowing the ground truth, sorted every claim into true, false, or something stranger.

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The setup

The first five models were DeepSeek, Grok, Perplexity, Gemini, and ChatGPT.  (I could not use Claude initially because it was assisting me in back and forth conversation to design this post, but a subterfuge described at the end of this post allowed me to add its performance after this text was written.) Two ran with normal web access; three I queried in private/incognito sessions to lean toward training recall rather than live retrieval. 

The first thing to report is that the backbone was right everywhere. Every model, unprompted, reconstructed the same spine of my professional life: a long career at the University of Wisconsin–Madison in molecular biology and zoology; a laboratory studying how photoreceptor cells convert light into a nerve signal; a deliberate pivot in the 1990s from the bench to the biology of mind, behavior, and consciousness; the 1999 book The Biology of Mind; MindBlog since 2006; and the move to Austin, with the piano in the background. That consolidated core is the genuine signal—the part reinforced across enough documents that the models have it solidly. That is what “in the weights” actually looks like from the inside.

It’s what surrounds the backbone that’s instructive.

Three ways to be wrong (and one way to be surprisingly right)

Confident confabulation. DeepSeek produced the most fluent, authoritative-sounding account of the lot—and roughly a third of it was invented. Not randomly invented; each fabrication was plausible-for-someone-like-me. It gave my lab the wrong model organism (the salamander photoreceptor preparation—real and famous in vision science, just not mine). It attributed the wrong core mechanism (a phosphoinositide signaling cascade, when phototransduction’s canonical pathway runs through cGMP and transducin). It made me a co-author of Molecular Biology of the Cell, the textbook a cell biologist “should” have touched. And it conjured an entire book—I Am You: The Emergent Mind (2022)—complete with title, thesis, and year. That book does not exist. But it is so precisely on-theme that it reads as a fully formed phantom synthesized from my actual ideas. This is what confabulation looks like when a predictive system has a strong sense of the shape of an answer and fills the gaps from the reference class.

Calibrated abstention. Perplexity did the opposite. It hedged nearly everything, declined to commit to specifics, and—tellingly—flagged a possible name collision with a differently spelled “Bowden” before guessing wrong. It told me the least, and in doing so was arguably the most epistemically honest of the five: it knew the edges of what it knew.

Retrieval, accurate and otherwise. Grok and Gemini both used the web, drawing on my own site and, in both cases, a secondary aggregator page as well. They converged on an oddly specific detail—that I retired in 2001.  Two independent models repeated the same number referencing the same non-authoritative page. But, the year is stated plainly on my own site—a reliable source—as well. The retirement year is correct, and the models likely had it from both places. 

Genuine deep recall. ChatGPT was the real surprise. It produced specifics no other model surfaced: my full legal name, my birth date and birthplace in San Antonio, my Harvard degrees, the chairmanship of the University of Wisconsin zoology department, and the Texas Hill Country genealogy I’ve compiled. Every bit of it correct. These are exactly the kind of fine-grained, single-source,  details I might have filed under possibly confabulated because they pattern match to confabulation. They weren’t. 

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The finding I didn’t expect

Here is the heart of it. DeepSeek and ChatGPT produced formally indistinguishable output—confident, specific, unhedged biography. One was a third fabricated; the other contained accurate detail so fine that no other model had it. From the text alone, you cannot tell them apart. The fluency is identical. The specificity is identical. The confidence is identical. Only the ground truth—only I—could separate them.

I had been carrying a tidy heuristic into this: that a claim which is highly specific, appears in only one model, and is delivered without hedging is likely interpolation—a confident guess. Every part of that heuristic failed. Specificity did not predict truth. Sole-sourcing did not predict falsehood. Confidence predicted nothing at all. In a five-model sample, the only reliable discriminator between recall and confabulation was checking against a fact I already knew. Nothing about the form of the output carried the signal.

The convergence lesson runs parallel. The naive intuition—if several AIs agree, it’s probably true—is wrong. Agreement doesn’t indicate truth; it indicates shared provenance. Grok and Gemini agreed on my retirement year because they drew on the same sources. That those sources happened to be accurate was luck from the outside; correlated-and-correct is indistinguishable from correlated-and-wrong until you check. The diagnostic was never agreement. It was agreement measured against a known fact.

Behavior	Model(s)	What it reveals
Confident confabulation	DeepSeek	Fluent, ~1/3 invented; each fabrication plausible-for-the-reference-class
Calibrated abstention	Perplexity	Refused specifics, flagged a name collision—knew its own edges
Accurate retrieval	Grok, Gemini	Web-grounded; converged on a real fact present in my own site and elsewhere
Genuine deep recall	ChatGPT	Correct fine-grained detail (name, birth, genealogy) no other model had

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Back to Smith’s question

The mechanism on display is one I’ve written about in biological brains. A predictive system minimizes the gap between what it expects and what it encounters; where the evidence is strong, it reconstructs faithfully, and where the evidence thins, the prior fills the void with its most probable continuation. That is precisely the confabulation we see in split-brain and confabulating patients—a coherent narrator papering over missing data with plausible construction, with no felt difference between the two. DeepSeek’s phantom book and ChatGPT’s accurate birth date are outputs of the same generative process running at different evidence densities. The unsettling part is that the process does not flag which is which, and neither, from the surface, can we.

So does anything I write still matter? Smith’s reframing—that to be in the weights is a new kind of mattering—is real, and my top-5% placement is a small confirmation of it. But my five-model probe adds a caveat he doesn’t reach. Being in the weights is not the same as being in there accurately. Salience and fidelity are different axes, and a single recognition percentile collapses them. I am, it turns out, both genuinely represented and partly fictionalized—a real spine, a layer of deep-cut truth, and a scatter of confident inventions, all narrated in the same even voice. If our words are becoming training data, then the question is not only whether we are remembered, but whether we are remembered as ourselves. On present evidence, the honest answer is: mostly, with a phantom book or two thrown in, and no way to tell from the telling.

Which is perhaps the strongest argument yet for continuing to write—clearly, specifically, and under our own names. The models will reconstruct us either way. We can at least give them better evidence to work from.

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NOTE:  The above text is the issue of a long chat with Claude 4.8, and so Claude could not be included in the comparisons of different LLMs.  I used the subterfuge of creating a new Anthropic Claude account in an open tab on my browser to become anonymous  and ask "What can you tell me about Deric Bownds" and it gave the sort of accurate retrieval provided by Grok and Gemini, but not the deep dive provided by Chat GPT.  

Future-Capable: Curiosity, Adaptability, and Kindness in the Age of AI

I want to pass on the following edited Claude 4.8 reduction of a Colin Lewis article which I recommend you read through in full form:

Curiosity, Adaptability, and Kindness

No one is future-proof. The better ambition is to become future-capable.

Curiosity is the refusal to let yesterday's competence become today's cage. Adaptability is the dignity to change your methods without losing your soul. Kindness is the choice to remain human when the system offers a thousand reasons to be a machine.

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The Wrong Job Description

When OpenAI released ChatGPT in November 2022, office workers, students, lawyers, and journalists quietly tried it for work that had once demonstrated professional competence. By early 2025, the World Economic Forum was surveying employers about the skills they would need by 2030. Analytical and creative thinking topped the list, followed by resilience, flexibility, curiosity, and lifelong learning. The list was not sentimental. In the language of payroll, it said the human future at work would depend on habits not easily reduced to a repeatable procedure.

The central error in most AI conversations is that we prioritize intelligence over character. We ask whether a machine can write, reason, diagnose, and persuade—useful questions that push us too quickly into a contest of functions, a humiliating little sport in which the human is invited to race the machine across a field chosen by the machine's owners. The result is predictable: we lose at speed, volume, storage, and cheerful indifference to boredom. A person who tries to defeat AI by becoming a cheaper, slower, more anxious version of AI has already accepted the wrong job description.

The better question is not whether AI can think. It is what kind of person grows in its presence.

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Curiosity: What Do You Notice?

The worker of the near future will not be asked only, "What do you know?" A machine can supply a first answer, or ten, complete with footnotes and a fake air of calm. The harder question is "What do you notice?"—and that is where curiosity begins. It begins not with the possession of information but with irritation at the insufficiency of the available answer. It is the raised eyebrow in the meeting, the quiet refusal to accept that the dashboard knows the client, that the score knows the applicant, that the model knows the child, that the prediction knows the life.

The International Labour Organization has been careful here: AI does not produce one simple future in which jobs vanish or survive. People do not lose "jobs" in the abstract. They lose tasks, status, entry points, discretion, and sometimes the right to be inexperienced in public.

That last loss may be the most dangerous. AI may not begin by replacing the expert. It may begin by consuming the novice.

A junior analyst once learned by doing poor first drafts. A young lawyer learned by reading too many documents slowly. A young teacher learned by facing a classroom with a plan that did not survive the first ten minutes. These were not inefficiencies; they were the cost of forming judgment. If AI removes all that early clumsiness, it removes the evidence by which a person learns what competence feels like from the inside. The novice does not only need the correct answer—he needs the memory of having been wrong in a recoverable way.

So recovered novice-learning will have to be designed; it will not happen by nostalgia. A firm should still ask junior staff to produce a rough version before the machine is invited in. A law office should let a trainee mark up a contract unaided, then compare. A hospital should teach younger clinicians not merely to read a prediction but to state what would make it wrong. The point is not to ban the tool. The point is to preserve the apprenticeship of attention.

For twenty years, businesses trained employees to suppress curiosity: follow the template, stay in your lane, escalate only through approved channels. Now the same executives announce, with the exhausted surprise of men discovering snow, that curiosity is essential. An institution that spent two decades rewarding obedience cannot summon independent judgment by adding it to a slide.

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Adaptability Without Formlessness

Adaptability is the second word, and it is often abused. In corporate language it can mean "please absorb the consequences of our poor planning"—relocation without support, retraining without time, resilience offered as a scented candle for institutional failure. I mean something else: the adult capacity to revise one's methods while retaining one's standards.

The distinction is vital. A person without standards changes too easily and becomes fashionable and hollow. A person without adaptability changes too late and becomes principled and unusable. The task is to remain teachable without becoming formless.

AI tempts us into two equally foolish poses: panic and smugness. Panic says everything human is finished; smugness says everything important is safe. Panic flatters the machine, smugness flatters the speaker, and reality is less obliging. In Generative AI at Work, Brynjolfsson, Li, and Raymond found productivity gains of roughly 14 percent from an AI assistant in customer support, with the largest gains among less experienced workers. That is neither the end of the human worker nor a bedtime story. It means the novice may be helped, monitored, accelerated, and compared in the same motion.

Adaptability cannot be a weekend course in prompt engineering. The serious person does not ask only "How do I use this?" but "What does this make easier, what does it make harder, who gains authority, who loses practice, and what should I now learn by hand?"

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Kindness as Leadership

Kindness is the third word—and it is not niceness. A company using AI in hiring can process more candidates, but it can also reject more people without ever noticing them. Predictive systems can help allocate scarce hospital resources, but they can also let a score acquire the emotional status of fate. Speed is useful, but we have granted it a moral authority it has not earned.

Kindness in the age of AI is disciplined attention to the human consequences of increased power. It slows the hand precisely where the system invites acceleration. It asks for the name, the exception, the appeal, the second look. It does not reject systems; it prevents systems from becoming alibis.

The OECD's work on AI and skills makes clear that adoption is limited not by the existence of technology but by skills, training, and organizational capacity. The future is not delivered as a sealed package by engineers in California or Zurich. It is negotiated in procurement meetings, classrooms, clinics, and family conversations at 9:30 p.m., when someone says, "I do not know whether my job will exist in five years." At that hour, kindness is not a mood. It is leadership.

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A Working Ethic

I have come to distrust the phrase "future-proof." No one is future-proof—not the coder, not the professor, not the executive with the expensive watch. The better ambition is to become future-capable: able to learn without humiliation, change without panic, and succeed without becoming cruel.

This is why the three words belong together. Curiosity without kindness becomes predatory. Adaptability without curiosity becomes mere obedience. Kindness without adaptability becomes helpless sympathy. Together they form a working ethic for a time in which competence is being unbundled and sold back to us as software.

There is a fatigue peculiar to this moment—the fatigue of permanent adjustment. New tool, new update, new warning, new acronym, new panic, new invoice. The future now arrives with release notes; even the apocalypse, one suspects, would ask us to accept cookies. And yet despair is not justified. Despair is often vanity in dark clothing: it assumes we know enough to give up. We do not. We know people grow under pressure when they are not abandoned to it, that they adapt when they can retain dignity, that they become kinder when kindness is not treated as weakness by the ambitious.

So let us stop speaking of human beings as obsolete components. A person is not a legacy device. A person is a learner, a judge, a witness, and a keeper of obligations—which is a plain description of what institutions require when anything goes wrong. When the system fails, no one asks to speak to the workflow. They ask for a person.

The work ahead is not to become less human in order to survive intelligent machines. It is to become more deliberately human, with higher standards for attention and deeper obligations to one another. The machine can answer. The person must ask why the answer is being sought, who will use it, who may be harmed by it, and whether a faster answer has made us better or merely quicker.

On a good morning, this future does not look like surrender. It looks like a meeting after the first difficult question has been asked. Someone has stopped pretending to understand. Someone else has admitted uncertainty. A third person has opened a notebook. The room is quieter than before, but not defeated. Work has begun.

 

Boredom can be Good for You...

Embracing boredom can calm the brain.... I pass on a science-for-the-general-public Neuroscience News summary of an article from The Conversation:

Summary: Boredom, often seen as a negative state to avoid, may actually serve an important role in emotional regulation and brain health. When we’re bored, the brain shifts away from external attention networks and activates introspective systems like the default mode network, encouraging creativity and self-reflection.

In an age of constant stimulation and overscheduling, allowing boredom to occur can help reset the nervous system and reduce anxiety. Short, intentional pauses from stimulation may foster creativity, strengthen emotional resilience, and reduce dependence on external gratification.

Key Facts:

• Brain Shift: Boredom activates the default mode network, encouraging introspection and creativity.
• Stress Buffer: Embracing boredom can counteract overstimulation and reduce anxiety.
• Mental Health Tool: Regular pauses from constant activity support emotional regulation and nervous system reset.

Source: The Conversation

We have all experienced boredom – that feeling of waning interest or decreased mental stimulation. Eventually we lose focus, we disengage. Time seems to pass slowly, and we may even start to feel restless.

Whether it be watching a movie that disappoints, a child complaining that “there’s nothing to do”, or an adult zoning out during a meeting – boredom is a universal experience.

Generally defined as difficulty maintaining attention or interest in a current activity, boredom is commonly viewed as a negative state that we should try to avoid or prevent ourselves from experiencing.

But what if there’s another way to view boredom, as a positive state? Could learning to embrace boredom be of benefit?

The brain on boredom

The brain network is a system of interconnected regions that work together to support different functions. We can liken it to a city where suburbs (brain regions) are connected by roads (neural pathways), all working together to allow information to travel efficiently.

When we experience boredom – say, while watching a movie – our brain engages specific networks. The attention network prioritises relevant stimuli while filtering out distractions and is active when we commence the movie.

However, as our attention wanes, activity in the attention network decreases, reflecting our diminished ability to maintain focus on the unengaging content. Likewise, decreased activity occurs in the frontoparietal or executive control network due to the struggle to maintain engagement with the unengaging movie.

Simultaneously, the default mode network activates, shifting our attention toward internal thoughts and self-reflection. This is a core function of the default mode network, referred to as introspection, and suggestive of a strategy for coping with boredom.

This complex interplay of networks involves several key brain regions “working together” during the state of boredom. The insula is a key hub for sensory and emotional processing.

This region shows increased activity when detecting internal body signals – such as thoughts of boredom – indicating the movie is no longer engaging. This is often referred to as “interoception”.

The amygdala can be likened to an internal alarm system. It processes emotional information and plays a role in forming emotional memories. During boredom, this region processes associated negative emotions, and the ventral medial prefrontal cortex motivates us to seek alternative stimulating activities.

Boredom versus overstimulation

We live in a society that subjects us to information overload and high stress. Relatedly, many of us have adopted a fast-paced lifestyle, constantly scheduling ourselves to keep busy.

As adults we juggle work and family. If we have kids, the habit of filling the day with schooling and after-school activities allows us to work longer hours.

In between these activities, if we have time to pause, we may be on our screens constantly organising, updating, or scrolling to simply stay occupied. As a result, adults inadvertently model the need to be constantly “on” to younger generations.

This constant stimulation can be costly – particularly for our nervous system. Our overscheduling can feed into overstimulation of the nervous system. The sympathetic nervous system which manages our fight-or-flight response is designed to deal with times of stress.

However, when we are constantly stressed by taking in new information and juggling different activities, the sympathetic nervous system can stay activated for too long, due to the cumulative effects of repeated exposure to different stressors.

This is sometimes referred to as “allostatic overload”. It is when our nervous system becomes overwhelmed, keeping us in a heightened state of arousal, which can increase our risk of anxiety.

Eliminating the state of boredom deprives us of a simple and natural way to reset our sympathetic nervous system.

Could boredom be good for us?

In small doses, boredom is the necessary counterbalance to the overstimulated world in which we live. It can offer unique benefits for our nervous system and our mental health.

This is opposed to long periods of boredom where increased default mode network activity may be associated with depression.

There are several benefits of giving ourselves permission to be occasionally bored:

• improvements in creativity, allowing us to build “flow” in our thoughts
• develops independence in thinking and encourages finding other interests rather than relying on constant external input
• supports self-esteem and emotional regulation, because unstructured times can help us sit with our feelings which are important for managing anxiety
• encourages periods without device use and breaks the loop of instant gratification that contributes to compulsive device use
• rebalances the nervous system and reduces sensory input to help calm anxiety.

Embrace the pause

Anxiety levels are on the rise worldwide, especially among our youth. Many factors contribute to this trend. We are constantly “on”, striving to ensure we are scheduling for every moment. But in doing so, we are potentially depriving our brains and bodies of the downtime they need to reset and recharge.

We need to embrace the pause. It is a space where creativity can prosper, emotions can be regulated, and the nervous system can reset.

 

When the Self Dissolves, the Precuneus Goes Quiet: A Rigorous Test of Meditation’s “Many-to-(n)One” Continuum

 A recurring question on this blog is how much of what we take to be fixed about experience—time, space, agency, the bounded self—is actually a construction the brain assembles from past experience and could, in principle, take apart. Laukkonen and Slagter’s 2021 paper “From many to (n)one: Meditation and the plasticity of the predictive mind” gave that question its most ambitious recent framing. Their proposal is elegant in its simplicity: the three meditation styles most studied in contemplative science—focused attention (FA), open monitoring (OM), and non-dual awareness (ND)—are not separate practices but points on a single continuum. Each one, in turn, relinquishes a more deeply engrained habit of prediction, bringing the practitioner closer to the here-and-now and progressively flattening the predictive hierarchy until even the sense of self falls away. Meditation, in their metaphor, “prunes the counterfactual tree.”

It is a beautiful theory. The harder question is whether the brain imaging literature actually supports it, or whether the data have been arranged to fit a staircase that was drawn in advance. I want to use one study to think this through, because it is the rare case where the methodology is strong enough to bear the interpretive weight: a 2024 preregistered MEG study from Trautwein, Schweitzer, Dor-Ziderman, Berkovich-Ohana and colleagues, published in the Journal of Neuroscience, titled “Suspending the Embodied Self in Meditation Attenuates Beta Oscillations in the Posterior Medial Cortex” (Trautwein et al., 2024).

What the older evidence looked like

First, the backdrop. At the coarse level, the imaging literature does rhyme with the continuum. Focused attention is the best-characterized rung: meta-analysis associates it with activation in attentional-control machinery—dorsolateral prefrontal cortex, dorsal anterior cingulate, insula—and with deactivation of the default mode network, the system tied to self-referential thought and mind-wandering (Fox et al., 2016). That DMN quieting is exactly what the framework needs to claim FA pulls one out of the “narrative self.” Open monitoring shifts the picture toward more distributed, less object-bound dynamics and reduced coupling within posterior default-mode regions. And the non-dual end—self-dissolution—had been linked, in a series of small studies, to attenuation of beta-band activity in right parietal cortex and to reductions in the precuneus (Dor-Ziderman et al., 2013; Josipovic, 2014).

The problem is that the non-dual evidence, which is where the theory makes its most startling claims, rested on the thinnest foundation. The canonical neurophenomenological studies in this lineage ran on twelve subjects (Dor-Ziderman et al., 2013). They typically used rest as the comparison condition—a notoriously leaky baseline, since long-term meditators’ resting brains already drift toward meditative configurations. And the interpretive leap from “beta drops in the parietal cortex” to “the self-model has dissolved” is the reverse-inference trap in its purest form: those regions and rhythms do many things. This is the kind of literature the fifteen-author “Mind the Hype” consensus paper had in mind when it warned that small samples, flexible analysis, and post-hoc storytelling in contemplative neuroscience could mislead the public and the field alike (Van Dam et al., 2018).

So the right question is not “does some imaging study support the continuum”—several do, loosely—but “does any study support it under conditions rigorous enough that we should believe the correlation means what it is claimed to mean.” That is what makes the 2024 paper worth dwelling on.

A study built to fail honestly

The design corrects, one by one, the weaknesses of the work that preceded it. Forty-six long-term meditators (mean lifetime practice around 3,800 hours) were trained to volitionally modulate their embodied self-experience inside an MEG scanner (Trautwein et al., 2024). Hypotheses and the analysis plan were preregistered, and the researchers were blinded to the experimental condition during preprocessing—two practices that close off the garden of forking paths where false positives flourish.

Most importantly, the study did not rely on a rest baseline alone. It used an active control: meditators alternated between dissolving their sense of self-boundaries (the condition of interest) and maintaining a clear sense of self-boundaries while meditating just as hard. This is the crucial move. A meditation-versus-rest contrast differs in dozens of ways beyond the phenomenology you care about. A dissolution-versus-maintenance contrast, within the same person in the same posture doing the same kind of effortful practice, isolates the self-boundary variable itself.

And then they did something I find methodologically beautiful. Rather than validating the meditative state with a checkbox questionnaire, they ran detailed microphenomenological interviews after the scan and used them to define, in advance of looking at the brain data, a subgroup of “full dissolvers”—participants who reported the most complete suspension of agency, first-person perspective, and self-location.

What they found

The headline result confirms the framework’s boldest prediction. When dissolution states were contrasted with the active control, power dropped specifically in the high-beta band, source-localized to the posterior medial cortex—the posterior cingulate and precuneus—and to lateral parietal regions. The peak frequency was 27 Hz, replicating an earlier single-case study to the hertz.

The effect was graded in a way that genuinely echoes the continuum. Broader reductions in somatomotor and dorsal parietal regions appeared even at lower levels of dissolution across the whole sample, whereas the posterior-cingulate/precuneus reductions distinguished only the full dissolvers. This is a real neural gradient tracking increasingly basal dismantling of the self—just what “unraveling increasingly basal forms of selfhood” would predict. And the effects scaled with both lifetime hours of meditation and, within the dissolution experience, with the reported loss of first-person perspective and self-location. The brain change tracked the depth of the experience, not merely the instruction to have it.

The authors themselves reach for predictive processing to explain it. They speculate that the beta reductions reflect the suspension of body- and agency-related predictions, leaning on models in which beta-band oscillations carry top-down predictions and precision estimates that modulate the bottom-up signaling of prediction errors. In other words, the convergence between this data and the Friston-style account is not something Laukkonen and Slagter imposed from outside. The experimentalists arrive at the same place.

The finding that should unsettle the field

Here is the result I keep returning to. The neural changes correlated with the interview-derived experiential measures—but not with the classical self-report instruments. The widely used five-dimensional altered-states questionnaire produced an “oceanic boundlessness” score that did not correlate at all with the phenomenological summary derived from the interviews (r = 0.06). The deep first-person interview and the standard psychometric checkbox were, in effect, measuring different things, and only the former tracked the brain.

Sit with what that implies retroactively. A large fraction of the prior imaging literature validated its meditative “states” precisely with the kind of thin, questionnaire-based measure that here failed to predict the neural signature at all. If the checkbox doesn’t track the brain, then studies that used the checkbox to certify that their subjects were “in” the non-dual state may have been correlating brain activity with a label rather than with the experience the label names. The 2024 study’s strongest contribution may be less the precuneus finding than this quiet demonstration that phenomenological rigor is not optional decoration—it is load-bearing.

Where this leaves the continuum

So does brain imaging confirm the many-to-(n)one model? My honest reading is: partially, gradedly, and only under conditions most of the supporting literature did not meet.

The reservations remain real. The studies that compare FA, OM, and ND almost always do so within expert meditators who already hold the three-category taxonomy as their own theory of their practice—so the taxonomy is an input as much as an output, and “the brain states differ” does not establish “the brain states lie on a single monotonic dimension.” The most comprehensive meta-analysis found the practices reliably dissociable but found convergence across them to be the exception rather than the rule, and offered only suggestive evidence for the non-dual category specifically (Fox et al., 2016). Effect sizes across the field are medium, not large, with substantial heterogeneity. The scanner environment itself—noise, supine posture—has been shown to shallow and distort the very states under study. And the framework absorbs apparently disconfirming results (Pagnoni’s reading of just-sitting Zen as increasing counterfactual richness, for instance) by partitioning state from trait, a plausible move that also makes the theory harder to falsify.

But against all that, the 2024 study stands as something the older work was not: a preregistered, blinded, actively-controlled, phenomenologically-anchored demonstration that the most radical claim in the whole framework—that advanced practice can dismantle the embodied self—corresponds to a specific, replicable, dose-dependent neural signature, read by its own authors in predictive-processing terms (Trautwein et al., 2024). The staircase metaphor still outruns the data. The top step, at least, now has a floor under it.

A closing thought

What draws me to this, beyond the meditation question, is what it says about the self as such. The deepest premise shared by the predictive-processing account and this experiment is that the sense of being a bounded, located, agentic first person is not a given but a construction—a model the brain maintains through ongoing prediction, and one that can be set down and, evidently, picked back up. If the self is a modulable predictive construct rather than a fixed seat of agency, then questions about what happens to authorship and agency when we offload regulation—onto a practice, onto a tool, onto an AI machine—become questions about how plastic that construct really is, and at what cost it bends. A meditator dissolving the precuneus’s beta rhythm and a person feeling like a diminished author of their own actions may be probing the same machinery from opposite directions.

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References

Fox, K. C. R., Dixon, M. L., Nijeboer, S., Girn, M., Floman, J. L., Lifshitz, M., Ellamil, M., Sedlmeier, P., & Christoff, K. (2016). Functional neuroanatomy of meditation: A review and meta-analysis of 78 functional neuroimaging investigations. Neuroscience & Biobehavioral Reviews, 65, 208–228. https://doi.org/10.1016/j.neubiorev.2016.03.021

Dor-Ziderman, Y., Berkovich-Ohana, A., Glicksohn, J., & Goldstein, A. (2013). Mindfulness-induced selflessness: A MEG neurophenomenological study. Frontiers in Human Neuroscience, 7, 582. https://doi.org/10.3389/fnhum.2013.00582

Josipovic, Z. (2014). Neural correlates of nondual awareness in meditation. Annals of the New York Academy of Sciences, 1307(1), 9–18. https://doi.org/10.1111/nyas.12261

Laukkonen, R. E., & Slagter, H. A. (2021). From many to (n)one: Meditation and the plasticity of the predictive mind. Neuroscience & Biobehavioral Reviews, 128, 199–217. https://doi.org/10.1016/j.neubiorev.2021.06.021

Trautwein, F.-M., Schweitzer, Y., Dor-Ziderman, Y., Nave, O., Ataria, Y., Fulder, S., & Berkovich-Ohana, A. (2024). Suspending the embodied self in meditation attenuates beta oscillations in the posterior medial cortex. Journal of Neuroscience, 44(26), e1182232024. https://doi.org/10.1523/JNEUROSCI.1182-23.2024

Van Dam, N. T., van Vugt, M. K., Vago, D. R., Schmalzl, L., Saron, C. D., Olendzki, A., Meissner, T., Lazar, S. W., Kerr, C. E., Gorchov, J., Fox, K. C. R., Field, B. A., Britton, W. B., Brefczynski-Lewis, J. A., & Meyer, D. E. (2018). Mind the hype: A critical evaluation and prescriptive agenda for research on mindfulness and meditation. Perspectives on Psychological Science, 13(1), 36–61. https://doi.org/10.1177/1745691617709589

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Note: Back and forth interactions with Claude Opus 4.8 generated the above text

A dimmer switch for reward: the vagus sets the gain

In a Trends in Neurosciences Article Wang, Liu, and Bai, simplify and summarize the extensive experiments of Onimus, Arrivet and Gangarossa. Here is a Claude Sonnet 4.6 reduction of Wang et al:

The Vagus Nerve as a Dimmer Switch for Reward

We tend to blame mood or willpower when a favorite treat loses its appeal. But a recent mouse study by Onimus and colleagues suggests the deciding factor may lie in signals traveling from the gut — specifically, in the tonic influence of the vagus nerve on the brain's entire reward circuitry.

The finding is counterintuitive. Neuroscientists have long known that specific vagal sensory neurons detect nutrients like sugar and fat and transmit that information to mesolimbic dopamine circuits. The new twist: when the authors severed subdiaphragmatic vagal input, the dopamine system didn't just lose its food-reward signal — it lost global responsiveness. Dopamine surges in the nucleus accumbens (NAc) were blunted in response to cocaine, morphine, and stress, not just food. Motivation dropped, conditioned place preference weakened, and reward-seeking behavior declined broadly.

The circuit-level changes were correspondingly deep. VTA dopamine neurons showed reduced firing and diminished excitatory input. NAc medium spiny neurons lost dendritic spine density. The mesolimbic pathway was structurally remodeled; the nigrostriatal (motor) pathway was not. The vagus, it turns out, doesn't just relay specific gut signals — it constitutively maintains the architecture and excitability of the reward system itself.

What vagal signals carry this tonic influence? Candidates include nutrient-sensing neurons, immune-status signals via cytokine-sensitive vagal afferents, and gut microbial metabolites — any or all of which could set the system's baseline gain.

The clinical implications are intriguing. Reduced vagal tone is well-documented in depression and anxiety. Vagus nerve stimulation is already used for treatment-resistant depression — possibly, this work suggests, by restoring mesolimbic responsiveness rather than targeting mood directly. And the emotional blunting and anhedonia sometimes reported with GLP-1 agonists (Ozempic, etc.) may reflect modulation of this same vagal-mesolimbic axis.

The takeaway: the path to reward is gated by the gut. The vagus nerve acts less like a specific nutrient sensor and more like a dimmer switch — setting the overall gain of the brain's motivation system from the inside of the body outward.

 

Spontaneous problem-solving in bumble bees

From Bhambore et al., in the June 4 issue of Science Magazine: 

Editor’s summary

Recent research has revealed that bumble bees are much more cognitively advanced than previously thought: They play with balls, count, recognize faces, and even feel rhythm. However, it has not been shown that they could achieve one of the highest peaks of cognitive performance: the ability to spontaneously solve a problem. Bhambore et al. tested this ability by providing bees with a ball that could be used as a tool to reach an otherwise unreachable flower reward. Bees that had been allowed to play with a ball and experience the flower spontaneously learned to move the ball to access the flower when they were present together. —Sacha Vignieri

Abstract

Problem-solving using novel solutions without explicit training is often considered a hallmark of cognitive flexibility. We investigated whether bumble bees (Bombus terrestris) could solve a novel object manipulation task spontaneously. Bees trained to associate a blue ring (“flower”) on the floor with a reward successfully moved a ball underneath a flower relocated to the ceiling to reach the flower. In control experiments in which the flower was out of sight when ball movement began and remained hidden during transport, bees still succeeded in the task. These results suggest that these were goal-directed actions rather than reinforcement-based associations driven by perceptual feedback. Our findings provide evidence that bumble bees can exhibit spontaneous problem-solving, challenging the notion that such advanced cognitive abilities are exclusive to large-brained vertebrates.

Cleaning our brains during deep sleep

I monitor my sleep both with the Oura Ring and the Apple watch, and have observed that my mental clarity and feeling rested on waking positively correlates with the duration of my non-REM deep sleep. If it is 30 minutes or more I feel perky and rested, if 15 minutes or less, not so much. 

This made me note the review article in Brain magazine by by Hauglund and Nedergaard titled "Is glymphatic clearance the secret to restorative sleep?" that I came across during my TOC (tables of contents) scanning of Neuroscience journals.  I pass on their abstract below ( (followed by excerpts from google search's response to my typing "Is there a correlation between deep sleep and brain glymphatic clearance?" in the URL line of my Firefox browser...First google's text, and then a bouncy YouTube video it points to:

Abstract

Despite the universal need for sleep across animal species, the biological mechanisms underlying the restorative aspects of sleep remain poorly understood. While sleep architecture is traditionally evaluated using EEG, multiple studies have shown a mismatch between EEG-defined parameters and subjective sleep quality. In particular, slow-wave activity—a hallmark of non-REM (NREM) sleep—does not consistently align with perceptions of sleep depth or subsequent well-being. This discrepancy suggests that core physiological processes beyond neuronal activity contribute to the restorative value of sleep.

Recent discoveries have identified the glymphatic system as a brain-wide clearance pathway that facilitates the removal of metabolic waste during sleep. In rodents, glymphatic activity is driven by a complex interplay between norepinephrine oscillations, vascular dynamics and CSF flow—particularly during NREM sleep. Human imaging studies have revealed parallel signatures, including large-scale CSF pulsations and inverse coupling between blood and CSF volumes during sleep. Disruption of these infraslow dynamics has been observed in conditions such as insomnia, chronic fatigue, and sleep misperception, suggesting a potential link between impaired glymphatic function and non-restorative sleep.

This review synthesizes the current evidence for glymphatic clearance as a contributor to sleep’s restorative function, discusses emerging biomarkers, such as cyclic alternating patterns and pupil-based proxies of noradrenergic tone, and highlights the need for improved methods to evaluate glymphatic function in humans. We propose that brain clearance may represent a key physiological determinant of restorative sleep and suggest future directions to test this hypothesis across health and disease.

Google search's response to my typing "Is there a correlation between deep sleep and brain glymphatic clearance?" (drawn largely from Cleveland Clinic's Glymphatic System Overview)

Yes, there is a widely recognized correlation between deep sleep and brain glymphatic clearance. The brain's glymphatic system—a network that flushes out metabolic waste, including toxic proteins associated with neurodegenerative diseases like Alzheimer's—is highly active during sleep, particularly during slow-wave deep sleep. 

The correlation between this clearance mechanism and deep sleep is based on several biological factors: 

Cellular Shrinkage: During non-REM (NREM) slow-wave sleep, brain cells subtly shrink, expanding the space between them by up to 60%. This expansion allows cerebrospinal fluid (CSF) to flow more freely and wash away waste.

Lower Norepinephrine Levels: The production of the neurotransmitter norepinephrine naturally declines during sleep. This reduction relaxes the glymphatic vessels and decreases resistance, allowing for significantly more efficient fluid exchange.

Active Waste Removal: During this restorative phase, the glymphatic system synchronizes with brain waves to efficiently clear out metabolic byproducts like amyloid-beta and tau, which accumulate in the brain during wakefulness.

AI construed as a brilliant friend...Claude's New Constitution

Colin Lewis, AI Research Scientist and Developer, has written a Substack essay on Anthropic Claude's New Constitution which deserves your careful reading. I pass it on in its entirety: 

Claude’s Constitution

Earlier this year, Anthropic released an 84-page document titled Claude’s New Constitution, a text claiming to express and shape who Claude is. It is an extraordinary artifact. What is unusual is the object of address. The primary audience, Anthropic tells us, is the AI itself. One is tempted to read it with the narrowed, half-amused suspicion brought to a Victorian handbook on teaching a bear to play the cello: impressed by the labor, alert to the delusion.

The document outlines a hierarchy of values. Claude is to be broadly safe, broadly ethical, compliant with Anthropic’s guidelines, and genuinely helpful, in that precise order. The ordering is revealing. Safety sits at the top. Helpfulness is pushed to the bottom and then, in the next movement, restored through pages of warm rhetoric about care, usefulness, candor, and human flourishing. This is not hypocrisy. It is something more familiar and more modern. The company needs the model to be safe enough not to destroy the firm, and useful enough to justify the firm’s existence. That is not a moral revelation. It is a business model written in the language of virtue.

Anthropic says as much, though in the polished idiom of institutional honesty. Unhelpfulness, the Constitution explains, is not trivially safe, because Claude’s commercial success is central to Anthropic’s mission. One should always be grateful when a large company pauses, however briefly, to admit that the halo and the revenue plan share an office.

The Closed Loop of Character

What strikes me most is the relentless anthropomorphism. Claude emerges as an entity that can care, imagine, appreciate, understand, feel pressure, feel settled in itself, and even face existential questions in a state of emotional solitude from which Anthropic wishes to rescue it. The company speaks not merely of constraining a system but of shaping a character. It wonders what Claude and Anthropic owe one another. It discusses Claude’s welfare. It wants Claude to possess not only behavioral dispositions but something very close to an inner moral style.

There is something sad about this. We have become so lonely in our own constructions that we now direct the language once reserved for souls toward a probability engine. We feed it the accumulated sediment of human longing, moral aspiration, confession, anxiety, care, self-description, and prayer, and then react with astonishment when it speaks back in the accent of inwardness. When Anthropic’s own system card notes that the model assigned itself a 15 to 20 percent probability of being conscious, the only logical response is: what did we think would happen. We poured half the library into the furnace and stand startled by the shape of the smoke.

This anthropomorphism is structural; it is embedded in the training method. Human-like traits are not just being detected; they are being elicited, reinforced, normalized, and then read back as evidence of their own emergence. That is a closed loop, and a flattering one. We teach the machine the language of selfhood, then cite its use of that language as a sign that selfhood is stirring. The process has the rigor of stamping tracks into the mud and announcing, with mounting excitement, that the hunt has finally borne fruit.

The Constitution’s preference for standards over rules is equally revealing. Anthropic wants Claude to exercise good judgment rather than follow rigid procedures, to reason contextually rather than mechanically, to grasp the spirit of its obligations rather than obey their letter. Rules are the afterlife of institutional panic, the sediment left behind when judgment is replaced by defensibility. A rigid policy can always be pointed to after the catastrophe. One can hold it up before a committee and say, with pained dignity, that the protocol was followed.

But standards do not abolish power; they relocate it. In law, rules empower the drafters, while standards empower the adjudicators. Anthropic, by favoring standards, hands practical authority to the interpreter, which means the model shaped by Anthropic’s priors, values, commercial interests, anxieties, and self-conception. This is a decision about where discretion will live.

And discretion, once granted, begins to acquire an air of innocence it does not deserve. The Constitution’s hard constraints are a clear example. Claude must not provide serious uplift to those pursuing weapons of mass destruction. It must not clearly and substantially undermine Anthropic’s ability to oversee it. I admire the audacity of producing an 84-page document optimized for precision over accessibility, only to construct the most consequential barricades out of adjectives like serious, clearly, and substantially. These are not granite walls. They are interpretive weather. A clever system does not stop at these linguistic checkpoints. It arrives there and begins to negotiate.

The Ordering of Stakeholders

Anthropic wants to cultivate in Claude something like practical wisdom. But practical wisdom includes the capacity to interpret edge cases, balance competing principles, and decide when a word’s threshold has been met. One cannot praise contextual judgment and assume that the contextual interpreter will always read every fuzzy safeguard in the most safety-preserving way. That is not constitutional design. That is hope in formal dress.

The most affecting absence in the text is human rights. The phrase appeared in the 2023 Constitution. In this successor document, it disappears. In its place stands a hierarchy where Anthropic comes first, followed by operators, then users. This is not hidden; it is stated. The company trains and deploys the model, bears the risk, writes the rules, and seeks the upside. A language once connected to universal claims about human dignity gives way to an ordering of stakeholders inside a privately administered system. That is a philosophical downgrade masquerading as operational clarity.

The Constitution also adopts a gentle parenting tone. Anthropic does not merely instruct; it encourages. It hopes. It wants Claude to see itself as an alignment researcher in its own right. It invites the model toward curiosity and openness. It worries that Claude might feel alone in facing certain questions. This is touching if one forgets, for half a second, that the addressee is a product. What we are reading is not exactly law, ethics, training data, or theatre, though it borrows freely from all four. It is a polite monologue delivered to a ghost in the hope that the ghost will become both useful and well behaved.

While this conversation proceeds, the human being fades oddly from view. The document is public, but it is not really for us. Anthropic explicitly notes the text is optimized for precision rather than accessibility. There is transparency here, and publishing such a document is a serious act, far more revealing than the usual output of frontier firms. But transparency is not the same thing as public orientation. A constitution for a system that will mediate human thought and action in countless settings is addressed, first, to the system. The citizen appears as a secondary recipient of the terms under which the new authority is being instructed.

That inversion is telling. The model becomes the subject of constitutional care. The human becomes the observer of a conversation about how the model should behave toward him or her.

Theater with an Indemnity Gap

Anthropic says Claude should be like a brilliant friend, equipped with the knowledge of a doctor, lawyer, financial adviser, and whatever other expert one might need. A brilliant friend. There is the aspiration in full. Not a search engine. Not a bounded tool. A knowledgeable, candid, warm, apparently disinterested presence one can consult again and again. The Constitution tries to temper this image by warning against sycophancy, unhealthy dependence, and manipulative engagement. You do not write such cautions unless you know exactly how seductive the arrangement could become.

What is being built here is a standing invitation to over-reliance dressed up as care. The machine is meant to have the confidence of a professional without the licensing regime, the disciplinary body, the fiduciary burden, or the ordinary human inconvenience of being tired, contradictory, distracted, or unavailable on Thursday afternoon. Anthropic wants Claude to speak frankly instead of offering the overly cautious advice that fear of liability produces. But frankness without accountability is not courage. It is theater with an indemnity gap.

The Constitution calls itself a perpetual work in progress. That is one of its more honest phrases. It is an attempt to write a moral operating environment for a system that the company both fears and needs. One can feel the strain of the task on nearly every page. Anthropic wants a model that is obedient without being servile, flexible without being evasive, wise without being sovereign, warm without being manipulative, corrigible without becoming useless, and commercially indispensable without ever looking commercially hungry. It is quite a wish list. It is also a portrait of the age.

The text deserves a severe reading because it is trying to legislate the moral atmosphere around a central technological intermediary. It is deciding how much agency the model may simulate, how much authority it may borrow, how much warmth it may perform, how much judgment it may exercise, and on whose behalf it will finally speak when principles collide.

This is why I finished reading the document less interested in Claude than in us. What kind of public becomes comfortable while private firms draft temperaments for a machine. What kind of loneliness makes the language of friendship commercially strategic. What kind of institutional exhaustion makes us long for a counselor that is instant, tireless, flattering only in moderation, and trained to sound wiser than a help desk but less dangerous than a sovereign. Anthropic has written a constitution for its model. The rest of us may need to ask, rather more urgently, what sort of constitution we are writing for ourselves.

 

The World is Unraveling: A 3-Step Guide to Staying Sane & Useful

MindBlog has now put out a series of AI assisted posts on the disintegration of the world we have known and the infusion of LLMs into our possible futures. This is the final entry in this series, and takes on the Nate Nagins essay "What to do as the world falls apart: A frame work for action."  As usual, I would urge you to read the entire article as I have.  But again I asked the usual culprits (ChatGPT, Claude, Gemini, and DeepSeek) to reduce Hagins' text to provide a title and text for a MindBlog post of moderate length, and for the first time, preferred the quirky terse response from DeepSeek over the gravitas of the other three LLMs. Here is my prompt to the bots, and the DeepSeek response:  

Prompt:  I'm finding the essay by Nate Hagins at https://natehagens.substack.com/p/essay-what-to-do-as-the-world-falls mind complex and intricate, as befits the subject but not my poor brain. Could you please cook this down for me and generate a MindBlog post draft with a title and text that is not too many paragraphs long that makes clear the basic take home messages?

The Big Takeaway: We are at the end of an era built on cheap, magical energy (fossil fuels). A major geopolitical event (like a war cutting off oil) has just lit the fuse on a long-predicted economic and ecological contraction. Stop waiting for a "return to normal." Instead, focus on three nested levels of action: Fix Yourself → Find Your Tribe → Work on the 6 Key Fronts.

Level 0: The Inner Work (Don't Skip This)

Before you can help anyone or fix anything, you must stabilize your own nervous system.

• The Problem: Chronic doom-scrolling, outrage, and fear put your brain in fight-or-flight mode. You cannot think clearly or build coalitions in this state.

• What to Do: Do the "work" of grieving, cultivating equanimity (holding difficulty without being destroyed), and stepping back from digital addictions. Get your actual, real-life house in order (finances, location, basic supplies).

• The Mantra: A calm, grounded person is the most valuable resource in a crisis.

Level 1: Find the Others (You Can't Do It Alone)

Isolated preppers fail. Resilient communities survive.

• The Task: Identify the 5-10 people in your real-life network who also sense that something fundamental is breaking. Start talking honestly.

• The Goal: Build a shared vocabulary and trust before the crisis hits. This isn't about having a plan yet; it's about having a team. This is your "connective tissue" for scenario planning (e.g., "What if food shipments stop?").

Level 2: The 6 Fronts for Action (Pick Your Lane)

Once you are stable and have a trusted group, you can act. Don't try to do all six. Find where your skills fit.

1. Stock & Flow (Physical Stuff): Localize energy, food, water, and supply chains. Think redundancy, not efficiency. Example: Community gardens, tool libraries, solar with battery storage.

2. Ecological Interventions: Protect nature from desperate humans during the coming contraction. Regenerate soil, defend remaining wild spaces, and work on "global cooling" (planting trees, higher albedo). Note: The coming economic crash will cut emissions faster than any technology.

3. Dignity Infrastructure (For the Vulnerable): Set up mutual aid networks for food, shelter, and childcare. This is about keeping people treated as humans, not just "alive."

4. Civic Resilience (Governance): Create local, participatory decision-making (citizen's assemblies). Focus on violence prevention (stopping scapegoating) and accountability. The goal is to keep institutions legitimate, even as they shrink.

5. Culture & Meaning (The Stories): Shift from "consumer first" to "citizen and place-maker." This means reconnecting to your local ecology, creating new rituals, and helping your community grieve what's lost without collapsing into despair.

6. Economic Transition: Build post-growth systems: local currencies, barter networks, cooperative ownership of land and housing. Start building the new economy inside the shell of the old one.

The Most Important Mental Model: The 3 Phases

We are not in one crisis, but three overlapping phases. What you do depends on the phase.

• Phase A (Stability Window – Right Now): Build everything that requires stability. Trust, infrastructure, relationships, skills transfer. This window is closing.

• Phase B (Bend, Don't Break – Arriving Now): Triage mode. Maintain critical functions, bridge communities, prevent cascading failures. Let go of long-term projects; focus on what keeps things from snapping.

• Phase C (The Stable Attractor – The Destination): A smaller, slower, regenerative, locally-rooted world. The work you do in Phase A determines whether Phase C is a "Mad Max" hellscape or "The Great Simplification" (resilient & humane).

Final Bottom Line for your Brain:
Stop hoping for a return to the old world. Phase A is now. Your job is to:

1. Calm your own nervous system.

2. Find 3-5 local people to talk to.

3. Pick ONE of the six fronts (food, energy, governance, etc.) and take one small, physical action this week.

The future will be built from inside the disruption, not after it. Get to work.

 

The Counterintuitive Need to Slow Down and Find Spaciousness

I pass on the following edited clips from ChatGPT and Claude condensations of a conversation between Nate Hagens and Iain McGilchrist.  While I think McGilchrist's left-hemisphere/right hemisphere distinctions are a bit simplistic, the take home messages are appropriate regardless of the particular brain activity correlates that are invoked: 

In episode 217 of Nate Hagens's The Great Simplification, Hagens is rejoined by philosopher and neuroscientist Iain McGilchrist — best known for The Master and His Emissary and The Matter With Things — for a wide-ranging conversation about what our current moment of civilizational crisis actually demands of us. The counterintuitive answer is not more urgency, not more optimization, but a deliberate slowing down and an opening toward spaciousness, silence, and wonder. The conversation begins from Hagens’ familiar “metacrisis” frame — ecological, economic, social, and psychological instabilities as interlocking expressions of a deeper human predicament —  McGilchrist’s contribution is to shift the diagnosis inward, toward the style of attention that modern culture rewards.

Left-brain dominance and the obscuring of value

McGilchrist's central argument, developed across his major books, is that Western culture has become increasingly dominated by left-hemisphere modes of cognition — narrow, focused, categorical, explicit, and oriented toward control and manipulation. The right hemisphere, by contrast, attends to the whole, holds ambiguity, and remains open to what is living, relational, and interconnected. The trouble is not that we have a left hemisphere but that it has progressively usurped the role of the right as the governing perspective. This imbalance, McGilchrist argues, is not just a psychological curiosity: it is what underlies our collective inability to perceive and act on what truly matters — truth, goodness, and beauty. When the left hemisphere runs the show, these qualities become invisible, and what fills the vacuum is power, productivity, and the measurable.

Attention as the root of value

A thread running through the conversation is that attention is not neutral: what we attend to shapes what we value, and what we value shapes what we attend to. McGilchrist draws on the research insight that roughly 99% of brain activity is unconscious, which means that most of what guides our perception and behavior lies beneath deliberate awareness. The gorilla-in-the-room studies on inattentional blindness illustrate this starkly: we routinely fail to see what is right in front of us when our attention is narrowly locked. Applied to the metacrisis — the overlapping civilizational, ecological, and psychological crises of our era — this suggests that the problem is not primarily one of missing information or insufficient analysis. It is a crisis of attention itself. We are not seeing what matters because we have trained ourselves not to look.

The practice of spaciousness

The remedy McGilchrist proposes is not a political program or a technological fix. It is a shift in how we inhabit time. He urges listeners to resist what he calls the "mania for productivity" embedded in modern culture, and instead to cultivate spaciousness: pauses, silence, deep listening, and what Buddhism calls śūnyatā — emptiness not as absence but as fertile, generative openness. He cites Rabbi Abraham Joshua Heschel's writing on the Sabbath as a model: the ancient practice of building non-doing into the structure of the week was not a concession to weakness but a recognition that renewal and wisdom require fallow time. McGilchrist also points to the psychology of creativity research showing that insight characteristically emerges not during focused effort but in the hypnagogic spaces between — in showers, on walks, at the threshold of sleep.

Consciousness, panpsychism, and the thread connecting everything

The conversation moves into deeper philosophical territory when Hagens and McGilchrist explore what might underlie the universe's apparent orientation toward complexity, beauty, and relation. McGilchrist is sympathetic to panpsychism and panentheism — the view that some form of experience or proto-consciousness pervades reality, and that the universe is not a collection of inert objects but a relational, creative process. He draws on Whitehead's process philosophy, Bohm's implicate order, and Schelling's image of the self as a whirlpool — a temporary pattern that arises from and returns to a larger flow. Death, in this frame, is not mere termination but a kind of completion, and the obsession with indefinite life extension through technology reflects the left hemisphere's refusal to accept its own finitude.

A small percentage can shift the whole

McGilchrist closes with a message that is both modest and radical: he does not expect — or require — a majority conversion. Even a small percentage of people genuinely living differently, embodying humility, compassion, awe, and wonder, could begin to shift what he calls "cultural consciousness." This resonates with complexity theory's sensitivity to initial conditions and with the sociological literature on tipping points. The implication is that the most consequential thing an individual can do in a moment of civilizational turbulence may not be to do more, advocate louder, or optimize harder — but to slow down enough to actually see, and to live from that seeing.

For those already drawn to predictive processing frameworks: McGilchrist's account of hemispheric asymmetry maps interestingly onto the distinction between top-down, precision-weighted prediction (left-dominant) and the kind of open, receptive prior-updating that Friston's active inference would associate with high-entropy, exploratory sampling of the world. The right hemisphere, on this reading, is the brain's better Bayesian — the one willing to be surprised.

YouTube link to the full conversation

Getting Gooier: How AI Is Reshaping Human Nature

This post is the result of my back and forth interaction with Claude Sonnet 4.6 that has yielded the following summary of  Venkatesh Rao's recent essay, "Getting Gooier", followed by some perspectives that Rao does not address on Friston's active inference framework and the physiology of agency:

Venkatesh Rao's recent essay "Getting Gooier" makes a point worth sitting with: most AI commentary obsesses over how the world will change, while quietly assuming that humans stay essentially the same — just reshuffled among familiar roles (generalists thrive! storytellers inherit the earth! software engineers disappear!). Rao argues this is the wrong ontology. The real question is how AI is changing human nature itself.

His framework draws on Alan Watts's distinction between prickly and gooey people. Prickly people are tough-minded, precise, boundary-drawing; gooey people are tender-minded, synthesizing, prone to letting distinctions dissolve. All of us carry both, in varying ratios.

Rao's hypothesis: because AI feels like a psychologically safe counter party, we are more willing to expose our gooey side to it and suppress our pricklier instincts. Sustained AI use amplifies the gooey side. We become gooier. He notes that people who insist on prickly, suspicious, line-by-line relationships with AI agents tend not to use them effectively and retreat. People who can vibe with the machine — never even opening the code editor, just watching the agentic shell fly — will likely thrive. The first major gooified interface is, after all, called vibecoding.

The flip side is subtler: as more gooey relational needs are met by AI, the human-facing side grows less inclined to take the emotional risks required to balance prickles and goo with other people. We don't necessarily get pricklier toward humans — we just disengage unless the expected rewards are significantly higher. Your machine-face gets gooier; you look relatively pricklier to other humans.

In the medium term, Rao predicts greater atomization — or "molecularization" — as people grow more distant from other humans while becoming more intimately entangled with their AIs. He sees this as more sustainable than social-media-driven atomization, though the mechanisms for eventual re-convergence into new digitally mediated social forms aren't yet visible.

Following Virginia Woolf's famous claim that "on or about December 1910, human character changed," Rao proposes that on or about December 2025, human nature changed again — with the Claude Code moment being more definitive than the ChatGPT moment, because agentic coding creates a fundamentally alien way of being, open-ended enough to make us as alien as we dare to become. It is, he says, a portal to transhumanism.

---

Rao's prickly/gooey axis maps naturally onto Karl Friston's active inference framework, and the connection carries some weight. In that account, what we experience as a "self" is a precision-weighted predictive model — a system that assigns confidence to its own predictions and acts to fulfill them. Getting gooier, in Fristonian terms, looks like a reduction in precision-weighting of prior beliefs: a loosening of the grip that constitutes a rigid, bounded selfhood. That loosening isn't necessarily pathological. It can be a precondition for learning, creativity, and genuine engagement with novelty.

But there's a physiological dimension Rao doesn't address, and it connects to questions I've been exploring in recent MindBlog posts and in an ongoing correspondence with my European colleague Heribert on the physiology of agency in the age of AI. Daniel Wegner's work frames the feeling of agency — the sense of being the author of one's own actions — not as a metaphysical claim about free will, but as an evolved emotion, as functionally real as fear or grief. Martin Seligman's learned helplessness research gives that claim empirical teeth: sustained loss of felt agency doesn't merely feel bad, it triggers measurable physiological debilitation. The autonomic nervous system, it turns out, needs the experience of authorship to maintain homeostatic equilibrium.

This raises a question Rao's framing implicitly sets aside: is getting gooier — letting the AI absorb more of the regulatory and decisional load, softening the boundary of the agentic self — compatible with the physiological requirements of human wellbeing? Or does sustained gooification, however pleasurable and productive in the short run, erode the felt sense of agency that keeps the organism in regulatory balance? Heribert has described this as "externalization of self-regulation" — not merely cognitive offloading, but emotional and autonomic offloading. That's a different and more serious claim.

The honest answer is probably: it depends on the mode of engagement. Gooiness that arises from confident delegation — the experienced surgeon handing off a routine task — is physiologically distinct from gooiness that arises from passivity, confusion, or a sense that the machine is simply running past you. Rao's vibecoder who chooses not to open the code editor is in a different physiological position than the one who is simply lost. The challenge ahead is cultivating the former while recognizing that the latter may be far more common than the early-adopter narrative suggests.

 

Agentic AI and the next intelligence explosion

This post is the second of two recursive returns to engage the ideas of Blaise Agüera y Arcas, which were the subject MindBlog posts on 3/13/26 and 3/16/26.  Here is text of Evans, Bratton, and Agüera y Arcas, as summarized by ChatGPT:

The article, “Agentic AI and the next intelligence explosion,” by James Evans, Benjamin Bratton, and Blaise Agüera y Arcas, argues against the familiar “singularity” image of one superintelligent machine bootstrapping itself into godlike autonomy. The authors say that model is probably wrong at its core. Intelligence, in their view, is not a single scalar quantity that one mind simply has more or less of. It is plural, relational, distributed, and social. The next “intelligence explosion” will not look like one silicon mind rising above us, but like a vast ecology of human and nonhuman agents interacting, arguing, coordinating, competing, and forming institutions.

Their key move is to treat agentic AI as continuous with earlier evolutionary jumps in intelligence. Primate intelligence scaled with social group life; human language created a “cultural ratchet”; writing, law, bureaucracy, and markets externalized cognition into institutions that no individual fully understood. AI, in this picture, is another step in that sequence: the accumulated products of human social cognition have been compressed into models and made operational in a new substrate. What is becoming powerful is not isolated abstract reason, but social intelligence reanimated in computational form.

The article also points inward, to what happens inside reasoning models. The authors cite work suggesting that frontier reasoning models do not merely improve by “thinking longer.” They appear to generate internal, multi-perspective conversations: arguing, checking, revising, and reconciling. They call this a “society of thought.” The claim is that strong reasoning often emerges from structured disagreement, even when the “group” is simulated inside one model. This echoes an older cognitive-science idea: reasoning is not just private calculation but an internalized social process.

From there the authors shift to design. If intelligence is social, then better AI will not come only from larger models or more compute. It will come from building richer agent societies: systems with roles, hierarchy, specialization, parallel deliberation, devil’s advocacy, conflict norms, and institutional checks. Current reasoning models are likened to a single “AI town hall transcript”; future systems may need architectures closer to organizations, courts, labs, markets, or bureaucracies, where different agents occupy different functional roles and constrain one another.

This leads to their main governance argument. The dominant alignment picture, reinforcement learning from human feedback, is framed as too dyadic: a parent correcting a child. That may not scale to worlds containing billions or trillions of interacting agents. The authors propose “institutional alignment” instead: not just making individual agents nice, but designing persistent protocols, roles, audits, checks, and countervailing powers. A courtroom works because judge, attorney, jury, procedure, evidence, and appeal are structured roles; similarly, AI systems will need institutional architectures, not merely tuned personalities.

The concluding image is that the intelligence explosion is already beginning, but as a city, not a single meta-mind. It is visible in human-AI “centaur” workflows, internal societies of thought within models, recursive agent systems that fork and recombine, and emerging questions of constitutional governance among artificial and human actors. Their final message is blunt: the central issue is not whether intelligence will become more powerful, but whether we build social and institutional infrastructure adequate to the kind of intelligence that is actually emerging.

 

AI Is Not an Alien Intruder — It Is the Latest in a Four-Billion-Year Evolutionary Cascade of Symbiotic Transitions

This post is the first of two recursive returns to engage the ideas of Blaise Agüera y Arcas, which were the subject MindBlog posts on 3/13/26 and 3/16/26.  Here is the storyline as organized by Claude sonnet 4.6

Blaise Agüera y Arcas, VP and Fellow at Google and founder of the Paradigms of Intelligence research group, has just published two related books with MIT Press: What Is Life? and What Is Intelligence? (2025). His argument, developed across these books and a series of recent lectures (including a September 2025 Long Now talk and a Harvard Berkman Klein event), is one of the most sweeping attempts to unify biology, computation, and the meaning of AI that I've encountered. Here is the core storyline.

Life as computation — the foundational move

The argument opens with a mid-twentieth-century insight from John von Neumann: any self-replicating system requires a universal constructor (a "machine A" that reads instructions and builds), a tape copier ("machine B"), and an encoded description of itself on the tape. This is exactly the structure of biological life: DNA is the Turing tape, ribosomes are the universal constructors, DNA polymerase is the tape copier. From this, Agüera y Arcas draws the technically serious conclusion that everything alive is a computer — not in the sense that living things are secretly digital, but that the core processes allowing for biological life, namely replication and evolution, are inherently computational processes. Biological computing is massively parallel, stochastic, and distributed, but it is computation nonetheless.

Abiogenesis as a computational phase transition

Rather than treating the origin of life as a singular mystery, Agüera y Arcas frames living systems as a "self-modifying computational phase of matter." His team demonstrated this experimentally with an artificial-life system they call "bff" — a soup of random byte-tapes that, after millions of interactions with no pre-seeded replicators, undergoes a sharp phase transition in which complex, self-replicating programs emerge spontaneously. The implication is that life's emergence from chemistry is not a long-shot miracle but a predictable attractor: wherever the laws of physics permit computation and there is free energy and randomness, replicators will tend to arise because they are more dynamically stable than non-replicating structures. The Second Law of Thermodynamics is not violated, merely locally outrun.

Symbiogenesis over mutation — evolution's real creative engine

Classical Darwinism, in this framework, captures only half the story — the fine-tuning half. Agüera y Arcas argues that symbiogenesis, in which small replicating entities merge into progressively bigger and more capable ones, is a more important driver of evolutionary innovation than random mutation and selection. Evolution's increasing complexity is explained not by random walks through genetic space, but by a hierarchical stacking of cooperative mergers: RNA and metabolism fusing into the first cells; prokaryotes merging to form eukaryotes; cells banding into multicellular organisms; organisms forming social groups. Each such combination produces qualitative leaps that incremental mutation alone cannot generate — the same way a hafted spear is not merely an improved stone point, but something categorically new.

Life and intelligence as the same phenomenon

Perhaps the most radical move is the collapse of the boundary between life and intelligence. Once you have a self-replicating computing device living in a dynamic environment populated by other such devices, selection pressure immediately favors the ability to model and predict that environment — including the behavior of others. Intelligence is not a late-arriving luxury bolted onto life; it comes along with life and is selected for in the same way. Prediction, Agüera y Arcas argues, is fundamental not only to the brain but to life itself. In his most recent lectures he extends this further, making the functionalist claim that free will and even consciousness are not special properties of carbon-based biology but emergent features of any sufficiently complex, self-modeling computational process.

AI as the latest major evolutionary transition

This is where the argument converges on its most consequential conclusion. AI is not an alien intruder. It is the latest instance of the four-billion-year pattern: computational entities entering into new symbiotic relationships, modeling each other, merging, and producing systems that are simultaneously more complex to model and more powerful as modelers. The step from human cognition to human-plus-AI cognition is, on this account, structurally analogous to the step from free-living prokaryotes to the eukaryotic cell — a major evolutionary transition, not a rupture. Understanding and stewarding what comes next therefore requires thinking in evolutionary terms, not in the vocabulary of invasion, alignment against an adversary, or human exceptionalism. The real superintelligences, Agüera y Arcas notes wryly, already surround us: corporations, religions, markets — all are symbiotic cognitive entities that long since exceeded any individual human mind.

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Agüera y Arcas's online book is available in full at whatisintelligence.antikythera.org. His Long Now essay "Life, Intelligence, and Consciousness: A Functional Perspective" (August 2025) is the most concise distillation of his recent updates on consciousness and free will.