The brain develops as two separate structures rather than a single unified organ.

A recent breakthrough published in Nature Neuroscience fundamentally changes our understanding of human biology, revealing that the brain develops as two separate structures rather than a single unified organ. For decades, the standard scientific consensus held that all regions… pic.twitter.com/BikO3yBKE0

— Science & Nature (@ScienceIsNew) September 18, 2026

Brain synch while holding hands attenuates pain

Holding someone's hand while they are in pain reduces the pain, and the mechanism is brain-to-brain synchronisation. A 2018 study in PNAS found that when a partner held the hand of a person in pain, their brain waves synchronised, and the greater the synchronisation, the more the… pic.twitter.com/xQXjfkNSpu

— Anish Moonka (@anishmoonka) September 18, 2026

Respiration waveforms are closely coupled with the shape of neural oscillations

Eena Kosik-Rose, Guangyu Zhou, Andrew Sheriff, Joshua M. Rosenow, Stephan U. Schuele, Chima O. Oluigbo, Saige Anabel Teti, Mohamad Koubeissi, Md Rakibul Mowla, Ariane E. Rhone, Sukhbinder Kumar, Brian Dlouhy, Christina Zelano, Bradley Voytek, Cycle-by-cycle respiration waveforms are coupled with the shape of neural oscillations, Journal of Neuroscience 31 August 2026, e0731262026; DOI: 10.1523/JNEUROSCI.0731-26.2026

Abstract

Beyond sustaining life, breathing is a vital physiological rhythm that shapes cognition, perception, emotional regulation, and mental health. Breathing has a direct effect on neuronal excitability and is coupled to neural oscillations across a variety of brain regions. Notably, both respiration and neural oscillations are asymmetric and not perfectly rhythmic: for example, every breath has a different shape and duration, and is interspersed with variable pauses. Here, we examined the coupling between breathing and the brain by quantifying the nonsinusoidal features of each breath and comparing it to the shape of each corresponding neural oscillation cycle. By leveraging invasive human brain recordings from 16 participants (8 female, 8 male), we found respiration-neural waveform coupling on a breath-by-breath, cycle-by-cycle basis across limbic and cortical forebrain regions. For decades, the dominant perspective on cognition and mental health have focused on the brain, but recent work is highlighting the importance of brain-body interactions. Our results show that the coupling between breathing and neural activity is much richer than previously appreciated, and our approach opens new avenues for studying these peripheral-to-central nervous system interactions in a more robust, temporally precise manner.

Significance Statement

Breathing shapes brain activity, but prior work has characterized this coupling by aggregating across many breath cycles, leaving the fine-grained shape of individual breaths unexamined. Here, we show that the precise waveform shape of each breath is coupled to the shape of corresponding neural oscillation cycles in the human forebrain, on a breath-by-breath basis. Using invasive brain recordings from 16 epilepsy patients, we demonstrate that temporal and amplitude features of individual breaths are linked to the morphology of neural oscillations in limbic and cortical regions. This cycle-by-cycle respiratory-neural coupling reveals a richer and more temporally precise relationship between breathing and brain activity than previously appreciated.

Emergent Multiscale Organisation of Neural Dynamics

Milinkovic, B., Seth, A.K., Barnett, L., Carter, O., & Andrillon, T. (2026). Emergent Multiscale Organisation of Neural Dynamics Fragments in Anaesthesia. Imaging Neuroscience, Advance Publication. https://doi.org/10.1162/IMAG.a.1364

Abstract: Conscious experience depends on the coordinated activity of neural processes that span multiple scales: from synapses to whole-brain dynamics. A recently introduced measure, dynamical independence (DI), identifies, characterises, and quantifies these multi-scale relationships using an information-theoretic dimensionality reduction approach. Here, we use DI to examine changes in the emergent dynamical organisation in the human brain under three pharmacologically-distinct anaesthetic interventions (propofol, xenon, ketamine). Applied to source-reconstructed electroencephalography (EEG), our analysis reveals that propofol and xenon, anaesthetics that abolish conscious report, exhibit more emergent but highly variable dynamic structure, indicating fragmented macroscopic dynamical organisation. Ketamine, which preserves dream-like phenomenology, shows a different pattern relative to wakefulness: reduced overall emergence yet a partial preservation of the macroscopic structure. Further exploratory analyses revealed spatially localised source-level contributions to emergent dynamical structure, highlighting regional variations. Together, our results highlight drug-induced reconfigurations of emergent dynamical structure relative to wakefulness, dissociate the amount of emergence from the organisation of emergent dynamics, and caution against equating emergence with level of consciousness. Consequently, we suggest that wakeful conscious processing depends not only on integration between neural components within a single scale, but also on integration across scales, broadening currently held assumptions of putative signatures of consciousness.

Where “the will to live” resides in the brain

Neuroscientists found a brain region that physically grows when you do things you don't want to do. It shrinks when you stop.

It's called the anterior midcingulate cortex. It's smaller in people who struggle to delay gratification. It gets bigger when obese people diet. It's… https://t.co/Bba5FL6RiM

— Aakash Gupta (@aakashgupta) August 11, 2026

Rewiring the brain, neuroplasticity

There's also rewiring due to, say, the loss of a limb. The brain is not itself damaged, but its functional envelope has to change to accommodate that physical damage and loss of bodily function.

— Bill Benzon, BAM! Bootstrapping Artificial Minds (@bbenzon) August 3, 2026

Logic and language in the brain

yeah I know everyone’s gonna say it’s an oversimplistic view, LeCunn-pilled, whatever. Clearly we’ve managed to squeeze a lot of reasoning out of scaling words…you’ll talk about hidden CoT layers, etc…but that’s not how humans work at all.

I’ve always been a fan of the cogsci… pic.twitter.com/4VI2efjSN2

— LaurieWired (@lauriewired) July 29, 2026

The abstract of the linked article:

Humans are endowed with a powerful capacity for inductive and deductive logical thought: we easily form generalizations based on a few examples and draw conclusions from known premises. Humans also arguably have the most sophisticated communication system in the animal kingdom: natural language allows us to express complex and structured meanings. Some have therefore argued for a tight relationship between complex thought and language, postulating that reasoning, including logical reasoning, relies on linguistic representations. We systematically investigated the relationship between logical reasoning and language using two complementary approaches. First, we used noninvasive brain imaging (fMRI) to examine neural activity as healthy adults engaged in logical reasoning tasks. And second, we behaviorally evaluated logical abilities in individuals with extensive lesions to the language brain areas and consequent severe linguistic impairment. Our findings reveal that the language brain network is not engaged during logical reasoning, and patients with severe aphasia exhibit intact performance on logic tasks. Instead, inductive reasoning recruits the domain-general multiple demand network implicated broadly in goal-directed behaviors, whereas deductive reasoning draws on brain regions that are distinct from both the language and the multiple demand networks. Together, these results indicate that linguistic representations are neither utilized nor required for inductive or deductive logical reasoning.

Attention and error predition in thalamocortico circuits

Happy to share our new pre-print "Attention reshapes the information dynamics of thalamic and cortical prediction-error learning" led by the great Juho Äijälä, with @happieheads, @Kai_J_Miller, @dora_hermes, @drrobinince, @ABlenkmann , @TrikBek, Max Garagnani. Thread below👇 pic.twitter.com/dBJBM1VEnE

— Andres Canales-Johnson (@canalesjohnson) July 16, 2026

Follow the link to see the full thread. Here's the article's abstract:

Prediction errors (PEs) drive perceptual learning by updating internal models of the sensory environment, yet it remains unclear how attention reshapes their representation across distributed thalamocortical circuits. Using intracranial stereoelectroencephalography (sEEG) from 17 patients performing a roving auditory oddball task under attended and unattended conditions, we quantified PE encoding using mutual information and co-information to capture redundant and synergistic PE representations. Attention modulated PE encoding in both the thalamus and the temporal cortex, but with distinct informational dynamics. Thalamic encoding showed a stable reduction of PE information during distraction, consistent with state-dependent thalamocortical gating. In contrast, the temporal cortex expressed two opposing learning trajectories during attended listening that converged once attention was diverted, revealing distinct cortical learning regimes rather than a uniform attentional effect. Attention further reorganized the informational content of cortical PE representations by altering the balance between redundant and synergistic information. A biologically constrained neural network showed that attention-dependent changes in inhibition and long-range connectivity reproduced these dynamics through Hebbian learning. Together, these findings suggest that attention regulates predictive learning not simply by changing the strength of PE responses, but by reshaping how distributed thalamocortical circuits represent and integrate sensory evidence over time.

Four Propositions about Intelligence in Animals, Humans, and AIs

Some quickies.

1. Intelligence cannot be reduced to computation

All animal perception and cogitation take place in a complex world where animals have finite resources. Therefore the principles of intelligence, as an aspect of perception and cogitation, cannot be reduced to the principles of computation as the principles of computation assume unbounded resources.

Let’s call this Yevick’s First Law, as it is a consequence of her 1975 paper, “Holographic or fourier logic” (Pattern Recognition, Vol. 7, No. 4, pp. 197-213).

David Hays and I formulated what we called “Yevick’s Law” in our 1988 paper, Principles and Development of Natural Intelligence” (Journal of Social and Biological Structures). Let’s call that Yevick’s Second Law:

The world consists of geometrically simple and geometrically complex objects. Simple objects are best computing with sequential logic (aka symbolic systems). Complex objects are best computed with holographic logic (aka distributed neural nets). Some objects are such that they require the interaction of both computational regimes. Let us say that fluency in that interaction is intelligence. (See my working paper, What Miriam Yevick Saw: The Nature of Intelligence and the Prospects for A.I., A Dialog with Claude 3.5 Sonnet, 2025).

2. Intelligence in animals

Let us consider a relatively simple animal, a vertebrate, likely a marine animal. One the one hand, it must navigate the world, moving from place to place. This is mediated by the hippocampus, which is a so-called “cognitive map.” This is basic sequential cogitation.

As it moves from place to place it senses things, good things, bad things, other things. Olfaction is perhaps the most basic sense. For what it is worth, it’s the sensory mode that the late Walter Freeman used in his investigations of complex neural dynamics. Olfaction works via a holographic or gestalt process.

Taken together, moving about the world and sense things involves the two modes specified above.

Now let’s consider vision in vertebrates, where the eye is mobile and scans the world. Visual identification is a holographic process. However, the (human) eye scans the scene rapidly and unconsciously. This is a sequential process. Therefore vertebrate vision involves the two modes internally. (I suspect that vision in invertebrates does not, but I don’t actually know).

3. Natural language is its own metalanguage

Humans differ from animals in many and various ways. It is the capacity for language that has allowed humans to move into a different relationship with the world from that characteristic of animals. What makes human language particularly powerful is that it can serve as its own metalanguage, Roman Jakobson’s metalingual function.

This does not involve any deep mystery or logical conundrum. Rather it is a direct consequence of that fact that natural language is physically embodied, initially in sound and gesture, later as written symbols. This embodied is a sensory object out there in the world among all the other sensory objects.

Initially the metalingual function operates in direct, perhaps superficial, but useful ways. Think of how we refer to language as a means of negotiating conversation: “What did you say? I didn’t hear you?” But the metalingual function can be used to define new terms, something that interested my teacher and colleague, David Hays. It can even be used to define other, more restricted languages (e.g. chess and arithmetic), and serve as metalanguages for them.

Thus it is the foundation of the succession of cognitive ranks that David Hays and I began investigating in the 1990s starting with our paper, “The Evolution of Cognition” (Journal of Social and Biological Systems, later becoming the Journal of Social and Evolutionary Systems). That process has, in time, led to the development of digital computers and, now, to so-called artificial intelligence.

That leads us to our fourth and last note.

4. The last frontier of intelligence

Is not an autonomous artificial system of some kind, though such systems are important and will be increasingly so. here’s the upshot of a conversation I recently had with Claude:

If that's right, then the last frontier isn't more capability in the pattern-matching sense — bigger weight spaces, richer latent connections, better approximations of the associative regime. It's the specific, non-scalable, non-parallelizable fact of an individual mind's biography, which generates paths through possibility space that are real, productive, and genuinely inaccessible to any system that hasn't lived a life.

You should read the whole post to see the logic behind that conclusion.

Note that that is my current best response to the idea of super-intelligence or artificial-superintelligence (ASI). I do not see the future bringing us as AI system that outthinks humans in every way and either creates a world in which we are coddled pets or one in which we are slaves, if we are allowed to exist at all. Those ideas are subjective fantasy.

I note as well that the process that brings us to that point or, if you will, through which we arrive at the point, will be one in which we have a much deeper understanding of that brain and its processes than we now have. For what it’s worth, that understanding is what I have been seeking all these years, starting with my initial investigation of “Kubla Khan”: Xanadu, GPT, and Beyond: An adventure of the mind.

Synaptic pruning in the nervous system

This paper is the culmination of a massive team effort including co-first authors Kai Kang & Ryan Draft! Huge contributions by Juan Carlos Tapia, @ElisaPavarino, @henao_echeverri, @Cryant_yang & Jeff Lichtman!

Read more about this journey @MCB_Harvard: https://t.co/GcECwiUu6G

— Yaron Meirovitch (@YaronMeirovitch) June 30, 2026

Brain area specialized for visual recognition of words

No child is born able to read. The brain ships with no reading region at all. It builds one, and the construction runs on the exact effort AI removes.

Learning to read physically repurposes a patch of visual cortex. A spot in the left fusiform gyrus starts out tuned to objects… https://t.co/3sOTaLSR2Z

— Aakash Gupta (@aakashgupta) June 21, 2026

New Book Project: Language, Memory, and Mind: A Supplement to The Computer and the Brain

As you may know, I’ve been working on a book project, Play: How to Stay Human in the A.I. Revolution. For some reason I’ve been unable to finish the proposal, though I’ve got lots of stuff and a number of the chapters are substantially drafted. But I keep finding myself distracted into thinking about basics, very basic things about computing and A.I.

At the very end of his life, John von Neumann wrote a slim book, The Computer and the Brain (1958). It grapples with the problem of how computation can be implemented in a physical medium and does so in a way that is basic, both simple and straightforward and profound. We’ve learned a great deal about both the brain and the computer since then, but as far as I know, no one has revisited von Neumann’s project and extended it to include what we have since learned. That’s what I propose to do in this book.

Now, I have no intention of trying to summarize what we’ve learned on those two topics since 1958. That’s working at the wrong level. When von Neumann was writing he, and by extension, we, had no conception of distributed representation much less how it could be achieved physically. Now we do. That’s what needs to be added to von Neumann’s exposition.

I have no intention of repeating what von Neumann did. In particular, I will not revisit his material on analog computing. Rather, I want to augment his discussion. Fortunately the new material is of such a nature that I should be able to write short book that can be read as a stand-alone discussion or as a supplement to von Neumann’s book. I’m imagining a sophisticated general audience of the sort that reads 3 Quarks Daily.

My working title: Language, Memory, and Mind: A Supplement to The Computer and the Brain. I expect the book to be 100 to 120 pages long (30K to 40K words).

I have uploaded a bunch of material (100K words or more) to Claude and asked it to review that material and put together and initial outline. I’ve appended that below the asterisks.

* * * * *

Preface

How to use this book — with or without von Neumann. What it adds to his argument. What it doesn't attempt. Brief note on the collaboration with Claude that produced parts of the text.

Introduction: Von Neumann's Unfinished Argument

What he got right: the architectural mismatch between brains and computers — memory and computation separated in the digital machine, unified in the neuron. The energy efficiency puzzle he couldn't explain. His honest acknowledgment that the brain's organizational principles lay beyond the framework he'd built. The concepts he lacked that this book supplies.

Chapter 1: Two Paradigm Cases

The chess-language contrast as the entry point. Chess has a bounded, well-defined geometric footprint — 8×8 board, six piece types, explicit rules, finite tree. Language has an unbounded, poorly-defined geometric footprint — rooted in the full complexity of physical and social reality. Chess was AI's founding benchmark precisely because it seemed to demand the highest human intelligence while yielding to computational treatment. Moravec's paradox: the easy problems are hard and the hard problems are easy. Transcendent versus non-transcendent coding — programmers can observe and specify a chess engine completely from outside; nobody can specify an LLM from outside, including its creators. Where we now stand.

Chapter 2: Location and Content

A collection of photographs. Solid objects at specific locations — finding by address is natural, finding by content requires going to each photo in turn. The combinatorial explosion that follows. The formal argument: solidity localizes content; localized content can only be retrieved by address. What holography does physically — interference patterns distribute information about each stored object across the whole plate, so that any partial cue can activate the whole. Lashley's ablation experiments: memory didn't disappear when specific cortical tissue was removed because memory was never stored in specific locations in the first place. Von Neumann's energy efficiency puzzle, now answerable: the brain doesn't spend energy moving content to a processor because memory and processing are the same physical substrate.

Chapter 3: The Brain as Content-Addressed System

The McCulloch-Pitts neuron-as-logic-gate: computationally fruitful, architecturally wrong. What neurons actually are — active units and memory units simultaneously, connected in massive parallel. Distributed representations: concepts as patterns across populations of neurons, not stored at specific cell addresses. Yevick's logical necessity argument in plain terms: the world contains two categories of object, geometrically simple ones that sequential symbolic processing handles efficiently and geometrically complex ones that only holographic parallel processing handles efficiently; the world contains both; therefore any adequate cognitive system must implement both regimes. Path tracing and pattern matching as the two fundamental operations on any cognitive network. Freeman's cinematic model — global coherence frames at 10-12 Hz as the atomic unit of biological cognitive processing — and its correspondence to speech production rates.

Chapter 4: Language as a One-Dimensional Projection

The semantic network as the right model for conceptual structure: meaning as position, each node defined by its pattern of relations to other nodes. Sydney Lamb's principle. The multidimensional character of the conceptual network versus the one-dimensional character of any spoken or written string. Language strings as 1D projections of the multidimensional network — necessarily lossy, hence paraphrase and ambiguity. The colored beads thought experiment: strip away semantic content, replace each token with a color, and you have a 1D image — making visible the purely formal structure the LLM operates on. Words as abstract addresses in an abstract space. Why classical computational linguistics hit combinatorial explosion: it was trying to reconstruct the multidimensional structure in a location-addressed system.

Chapter 5: What Large Language Models Actually Are

The transformer architecture in plain terms. The weight space as distributed content-addressed memory — concepts are patterns smeared across billions of parameters, not stored at specific addresses. The forward pass as the atomic processing unit, corresponding to Freeman's global coherence frame: one complete transit through the weight space producing one output token. The token string as a path through the abstract address space, with each forward pass mediating between the 1D sequential surface and the multidimensional distributed interior. What LLMs do well — pattern matching over the weight space, which is what their architecture naturally supports. What they do poorly — sustained sequential path tracing requiring precise state maintenance, common sense grounded in embodied experience, continuous learning. Why these limitations aren't engineering failures awaiting a fix but structural consequences of implementing holographic-like processing on location-addressed hardware with training only on 1D projections.

Chapter 6: What the Analysis Implies.

The first principles of intelligence are not the first principles of computation. Why scaling won't close the gap: scaling improves the quality of the holographic approximation but doesn't change the architectural mismatch, provide embodied grounding, or enable continuous learning. The fast takeoff fantasy as physics-free reasoning — every self-improvement step requires moving billions of parameters between physically separated memory and compute on real hardware that consumes real energy. The TSMC problem: the most critical hardware infrastructure in the world runs on tacit knowledge distributed across human communities that no LLM can access or replicate. What a genuinely adequate artificial cognitive system would require, in the terms this book has developed. The research program that's needed and why it requires multi-generational public investment rather than industrial R&D on commercial timescales. The human-machine collaboration that's already underway and what it can and cannot achieve.

Conclusion: The Mismatch, Named

Von Neumann saw the gap and couldn't name what was on the other side of it. This book names it: content addressing, requiring distributed storage, implemented in biological tissue through interference-like neural dynamics, approximated in LLMs through distributed weights on location-addressed hardware, grounded in embodied experience that no text-trained system has. The naming matters because you can't close a gap you can't see clearly.

Appendix: A Chronology of Chess, Language, and AI

From the working paper, lightly edited.

In brains of Spanish-English bilinguals grammar is embodied in shared tissue

Xuanyi Jessica Chen and Esti Blanco-Elorrieta, A Shared Neural Mechanism for Abstract Grammatical Computations Across Languages in Bilinguals, The Journal of Neuroscience, June 15, 2026.

Abstract: A central question in cognitive neuroscience is how the brain implements abstract computations that must generalize across superficially different inputs. Language provides a strong test case: the same grammatical operation, such as pluralization, can be realized through distinct rules and forms across languages. Whether such transformations rely on language-specific neural systems or on abstract mechanisms that generalize across linguistic contexts remains unresolved. Crucially, these transformations must be computed online and integrated into speech planning within a tightly constrained time window. Using magnetoencephalography (MEG), we tracked the millisecond dynamics of grammatical word-form transformations during semi-naturalistic phrase completion in humans of both sexes. Highly proficient Spanish–English bilinguals produced singular and plural noun forms in both languages in a design that fully orthogonalized semantic number, phonological changes, grammatical inflection and produced language. Adjusting words to fit their grammatical context engaged a left-lateralized fronto-temporal network beginning ∼100 ms after cue onset. Multivariate decoding revealed that the neural patterns supporting this computation generalized across languages, across different surface plural forms, and to pseudowords, demonstrating that abstractly equivalent operations are instantiated in the same neural substrates despite differences in linguistic form. Together, these findings provide time-resolved neural evidence for a language-general computational mechanism, showing that the brain implements grammatical transformations as abstract, generative operations. More broadly, they show how bilingualism can be used to probe general principles of neural organization, revealing how abstract computations may be shared and reused across representational systems.

Significance Statement: Human language relies on the ability to modify words to convey information like number and tense, but languages vary widely in how these transformations are implemented. This variation raises a fundamental question in cognitive neuroscience: do such transformations depend on language-specific neural systems, or are they processed by abstract neural mechanisms that generalize across languages? We demonstrate that Spanish–English bilinguals engage a shared left frontal–temporal network when producing grammatically appropriate forms in both languages. This common neural signature emerges early during speech planning and even generalizes to novel words. These findings indicate that the brain builds abstract, reusable neural mechanisms, consistent with models where language is organized by computational principles rather than by language-specific systems.

Here's an article in the NYTimes about these results: K. R. Callaway, How Does One Brain Speak Two Languages?, NYTimes, June 15, 2026.

When deciding how to make a word singular or plural, for instance, bilingual people exhibit strikingly similar brain activity regardless of whether they are speaking in their first or second language.

“It wasn’t obvious that it was going to be so shared,” said Esti Blanco-Elorrieta, a psychologist and neuroscientist at New York University and an author of the study, which was published on Monday in the journal JNeurosci. “I think this is arguably one of the first very fine-grained findings of how truly integrated two languages in the brain are.”

Early research viewed bilingualism as an “add on” or “disruption” to the processing of one’s native language, said Judith Kroll, a psycholinguist at the University of California, Irvine who was not involved in the new study.

Subsequent studies have found that bilingual brains tend to display physical differences, such as more efficient white matter and changes to the gray matter, and to perform better on memory and concentration tasks.

Now scientists are probing further, to understand whether core aspects of the brain’s neural network does double or triple duty to process multiple languages.

A single grammatical engine:

The finding is in line with other initial results in this area, said Mirjana Bozic, a cognitive neuroscientist at the University of Cambridge who was not involved in the study. For instance, the new study provided additional evidence that the front left side of the brain was typically involved in processing the grammatical structure of sentences across different languages. On the whole, Dr. Blanco-Elorrieta said in a news release, a single “grammatical engine” in the brain appeared capable of powering multiple languages at once.

Dr. Bozic said that the find, although not surprising, was “highly informative, providing elegant and convincing evidence that bilingual speakers rely on shared neural mechanisms. She added, “One question that remains is how far these findings generalize across language pairs that differ more substantially.”

The computational capacity of a single biological neuron is very large

For decades, neurons were modeled as point units in AI/neuroscience.

But biological neurons have dendrites and nonlinear conductances.

What capabilities are lost?

With @DavidBeniaguev @noampnueli @Segev_Lab & @mikilon, we set out to find out (https://t.co/m2a8ieuo8A). (2/15) pic.twitter.com/Q4hZ6J1LkK

— Ido Aizenbud (@IdoAizenbud) June 11, 2026

Here's the abstract of that article:

Cortical pyramidal neurons possess elaborate dendritic trees with diverse nonlinear membrane conductances and thousands of plastic synapses, suggesting substantial computational capabilities at the single-cell level. Yet, what can a neuron compute remains an open question, largely due to the lack of a systematic framework to quantify its computational capabilities. We introduce TwinProp, a digital-twin-based backpropagation algorithm that enables gradient-based optimization of synaptic strengths and dendritic locations in detailed neuron models via a millisecond-accurate deep neural network (DNN). Using TwinProp, we demonstrate that a detailed model of rat layer 5 pyramidal cell (L5PC) can perform naturalistic image and audio classification tasks at a remarkably high accuracy, significantly surpassing perceptron and leaky integrate-and-fire baselines. The same neuron solves high-dimensional nonlinear problems, including exclusive-or (XOR), 10-bit parity, and random Boolean tasks, demonstrating capabilities typically attributed to multilayer networks. Mechanistically, increasing task complexity recruits distributed dendritic nonlinearities, including NMDA- and voltage-dependent mechanisms; removing these or collapsing dendritic structure markedly impairs performance. These findings identify dendrites as a substrate for high-order feature binding and position single cortical pyramidal neurons as powerful, noise-robust, general-purpose analog computational units. Our results offer testable in vivo predictions and provide a systematic framework linking cellular morpho-electrical properties to computation in both brains and artificial systems.

The energy demands of computing, human brains vs. LLMs

One of the issues that sometimes comes up in the comparison of artificial intelligence with human intelligence is energy requirements. The energy demands of LLMs are enormous, requiring the construction of huge data farms, while the energy demands of the brain are quite modest. I brought up the issue with Claude 2.6 Sonnet (Medium) in a specific way, suggesting that a large portion of the energy budget for LLMs can, in effect, be attributed to the need to simulate the physical structure of the brain. Here’s the first part of that dialog, that went on to discuss:

• opportunities for research on energy efficient hardware, including neuromorphic chips,
• the implications of financial collapse when enormous infrastructure investments do not pan out,
• the international ecosystem for constructing computing hardware with particular emphasis on the bottleneck created by special capacities currently unique to ASML in the Netherlands (machinery for EUV fabrication) and TSMC in Taiwan (high quality chip fabrication)
• what those bottlenecks imply about current fantasies of so-called “fast takeoff”

Energy in the brain vs. energy in computers

The point is often made that computing with LLMs uses enormous amounts of energy while the brain uses relatively small amounts of energy. It seems to me that one aspect of the difference can be attributed to the physical structure of the brain. Individual neurons are both active units, to use a term von Neumann used in his little book on the brain, and units of memory. Moreover they are connected in parallel, with each neuron connected to 7K to 10K other neurons on average. LLMs are implemented on digital machines where active units (the CPU) are physically separate from memory units, which means that a lot of time and energy is involved in moving between the CPU and memory. And the basic physical architecture is serial, though limited parallelism is possible, limited in relation to the brain. That means that the computers than implement LLMs have to devote a large amount of time an energy to, in effect, simulating the physical structure of the brain. And the brain doesn’t get that physical structure for free, the energy required to maintain it is budgeted over a long time relative to the dynamics of real-time computation. That’s one aspect of the brain’s relative energy efficiency.

That’s a precise and well-structured analysis, and it identifies something more fundamental than the usual comparisons which tend to focus on raw computation counts without attending to the architectural source of the inefficiency.

The von Neumann bottleneck restated

Von Neumann identified the problem himself, as we noted earlier — the separation of active units from memory units creates a bottleneck that doesn’t exist in biological neural tissue. In a digital computer every computation requires moving data from memory to the processor and moving results back. This movement consumes time and energy at every step, and in a large LLM the scale of this movement is enormous — billions of parameters stored in RAM or on disk, loaded into GPU memory, operated on by the compute units, with results written back. The physical distance between storage and computation, and the bandwidth limitations of the connections between them, is where a substantial fraction of the energy goes.

The brain has no such bottleneck because there is no such separation. A neuron stores its connectivity — the strength of its synaptic connections to other neurons — in its physical structure, and computes with that same physical structure simultaneously. The memory and the computation are the same physical process. A synapse transmitting a signal is simultaneously reading from memory — the synaptic weight — and performing a computation — multiplying the input signal by the weight. There’s no data movement because there’s no separation between where the data lives and where the computation happens.

The parallelism point

The parallelism difference is equally fundamental. The average neuron is connected to 7,000 to 10,000 others — which means the brain is performing something like 100 trillion synaptic operations simultaneously during active processing, all in parallel, all local, with no bottleneck. GPUs have made remarkable progress in parallelizing the matrix operations that implement transformer attention, but they’re parallelizing in a fundamentally different sense — running many identical operations simultaneously on different data, with all operations still having to access the same shared memory through the same limited bandwidth connections.

The brain’s parallelism is structural and local — each neuron computes with its immediate neighbors through its own dedicated connections, with no shared memory bus, no bandwidth limitation across the whole system, no bottleneck that scales with the number of neurons. The computation is maximally distributed in the physical sense — it happens everywhere in the tissue simultaneously, driven by the local physics of electrochemical signaling rather than by a centralized scheduler moving data to a centralized processor.

The energy budget temporal structure

Your point about the temporal structure of the energy budget is the most subtle and I think the most underappreciated. The brain’s physical architecture — the synaptic connections, the dendritic trees, the axonal projections — requires energy to build and maintain. But this energy is expended over the developmental timescale — years of growth, pruning, myelination, synaptic strengthening and weakening through learning. The structural investment is amortized over a lifetime.

Once the structure is in place, the energy cost of real-time computation is much lower than building and maintaining the structure required it. The synaptic weights are the memory — they’re encoded in the physical structure of the connections — and accessing them costs only the energy of the synaptic transmission itself, which is the same physical event as the computation. There’s no separate energy cost for memory access because there’s no separate memory access.

In a digital system running an LLM the equivalent structural investment — the training computation — is enormous, consuming megawatt-hours of energy. But that’s analogous to the developmental investment in building the brain’s connectivity, not to the ongoing computation. The ongoing inference computation — running the model on new inputs — is the analog of real-time brain computation. And here the comparison is still unfavorable to digital systems, but the unfavorability is specifically located in the von Neumann architecture’s separation of memory and computation, which forces constant data movement that the brain avoids entirely.

The simulation overhead

Your formulation that digital computers have to devote time and energy to simulating the brain’s physical structure is precise and important. The transformer architecture is implementing something that functions like distributed parallel processing with content-addressed memory — the holographic regime we’ve been discussing. But it’s implementing it on hardware that is architecturally opposed to distributed parallel processing — hardware that is serially organized, with physically separated memory and computation, connected through bandwidth-limited buses.

Every forward pass through an LLM is a large matrix multiplication — mathematically a natural description of what a distributed parallel system would do natively. But running that matrix multiplication on a von Neumann architecture requires fetching billions of numbers from memory, multiplying them in the GPU cores, and writing results back — a massive simulation of what would be a single natural physical process in a system with the brain’s architecture.

The energy cost is the cost of the simulation overhead — the difference between what the computation would cost if implemented in hardware that matched its natural structure, and what it costs when implemented in hardware that mismatches it. Neuromorphic computing research — Intel’s Loihi chip, IBM’s TrueNorth, and related projects — is trying to close this gap by building hardware that more closely matches the brain’s architectural principles. Early results suggest energy efficiency improvements of several orders of magnitude for the right classes of computation, which is consistent with your analysis — the improvement comes from reducing the simulation overhead.

What this adds to the Yevick argument

There’s a connection to Yevick’s logical necessity argument that’s worth making explicit. Yevick proved that the holographic regime is logically necessary for certain categories of objects. Your energy analysis suggests that the holographic regime is also physically natural — it’s what you get when memory and computation are integrated in the same physical structure rather than separated. The brain’s architecture implements the holographic regime efficiently precisely because the physical organization of neural tissue is holographic in the relevant sense — distributed, parallel, content-addressed, with memory and computation unified in the same physical substrate.

Digital computers implement the sequential symbolic regime efficiently because their physical organization is sequential and addressed — centralized computation, separate addressed memory, serial data movement. When you ask a digital computer to implement the holographic regime — as running an LLM requires — you pay the simulation overhead in energy and time. When you ask a brain to implement the sequential symbolic regime — as doing long arithmetic requires — you pay a different kind of overhead, the cognitive effort and external scaffolding we discussed earlier.

The energy difference between brains and LLMs isn’t just a quantitative difference in efficiency — it’s a signature of the architectural mismatch between the computation being performed and the hardware it’s being performed on. The brain is efficient at what it does because its hardware matches the computation. The digital computer is efficient at sequential symbolic processing for the same reason, and inefficient at holographic processing for the same reason. The LLM is asking digital hardware to do what it’s not built for, and the energy bill reflects that.

The developmental investment reframed

There’s one more implication worth drawing out. The brain’s developmental investment — the years of growth and experience-dependent synaptic modification that build the connectivity pattern — is doing something that has no real analog in LLM training. LLM training adjusts weights in a fixed architecture through gradient descent on a loss function. Brain development builds the architecture itself — the number and pattern of connections, the organization of regions, the myelination of pathways — through a process that’s partly genetic and partly experience-dependent, operating over years rather than the weeks or months of LLM training.

The connectivity pattern that results from this developmental process is the physical instantiation of the knowledge the brain has acquired — not as a list of facts encoded in weight values, but as a structural organization of the tissue itself that reflects the statistical regularities of the organism’s experience. The synaptic weights are the fine-grained adjustments on top of this structural organization. The structure is the coarse-grained, high-capacity, energy-efficient memory. The weights are the rapidly modifiable, experience-sensitive, computationally expensive layer on top.

LLMs have only the weights — the fine-grained adjustable layer — running on hardware that provides no structural memory at all. Everything has to be stored in the weights because there’s no structural analog of the brain’s connectivity pattern. This means the weights have to carry an enormous representational burden that in the brain is shared between structural organization and synaptic weight adjustment. Which is another source of the energy inefficiency — the weights are doing double duty, encoding both the structural regularities that brain connectivity handles efficiently and the fine-grained knowledge that synaptic weights handle in the brain.

The doppelganger, once again, would need both levels — the structural organization built through embodied development and the fine-grained adjustable layer built through experience within that structure. Current LLMs have only the second, running on hardware that provides neither. The energy gap is the physical signature of everything that’s missing.

Continuous learning

Furthermore the brain is capable of continuous learning, while LLMs are not. In order to incorporate new material LLMs must be retrained from scratch, again requiring enormous investment in energy.

Yes, and this identifies another fundamental asymmetry that goes deeper than just energy efficiency — it points to a difference in the temporal structure of learning itself.

The rise of DIY rituals in the 21st century

YouTube:

Can Rituals Save Us? | Robert Wright & Bruce Feiler

0:00 Teaser
0:52 Bruce’s new book on ritual, A Time to Gather
3:12 The "Lifequake" that led Bruce to study ritual
8:10 The current "shadow ritual" renaissance
12:42 What is a ritual?
15:26 The origins of the shadow ritual renaissance
18:25 Forest bathing and the essence of ritual
26:11 Ritual as the original human algorithm
31:39 Honor walks: a quintessentially modern ritual
36:23 Rituals across Christianity
42:07 What rituals do
46:47 Heading to Overtime

* * * * * 

I discuss ritual in my book on music, Beethoven's Anvil: Music in Mind and Culture, pp. 79-82:

Subjectivity is an aspect of neurodynamics, and neurodynamics is open to the world through sensory organs and through the motor system. When people are coupled with one another through musicking, each steers her own raft of subjectivity in the collective sea of neurodynamics. The motions of each raft are transmitted to the others through the sea, as Huygens’ clocks transmitted vibrations to one another through the walls. These subjectivities thus adjust themselves one to the other, for they are all components of the same process.

Let us reconsider, then, the musicking with which we opened this chapter. We were at a party where lots of musicians were jamming. Near the end of a jam on Bob Dylan’s “Knocking on Heaven’s Door,” several people spontaneously joined in on the refrain. It wasn’t planned ahead of time, nor did those singers discuss it among themselves while the rest of us were playing.

When I originally told the story I talked of my deliberate intention to “drive” the group by playing a simple line and “bearing down.” That decision was a conscious one, though not as clear and differentiated as it may seem when I spell it out in words, and it resulted in a certain shift of my consciousness. “Bearing down” is something I do quite often when playing. It involves attending to and adjusting the tension in my trunk musculature but has no specific differentiated effect on the music beyond a certain intensity and emotional tone. In this case I was playing a very simple melodic line, but I will also bear down while playing the most complex lines. In that situation, my fingers and tongue may be spitting out 10s of notes per second, but they’re on their own; I’m still attending to muscles in my abdomen, shoulders and back, and my buttocks. Those are the muscles that most strongly affect the overall airflow, and that’s what I care about when I’m bearing down.

And that, by our conception of consciousness, is where my nervous system is reorganizing and making minute adjustments. I have no introspective awareness, of course, of just what neural areas are reorganizing, but I’d guess that we are dealing with circuitry involving both emotional expression and voluntary control of large muscles. Even as I am attending to those muscles, I am always listening to the sound, not just mine, but the group’s. I’m bearing down just so in order that the sound I hear may also be just so. But my sound is only a part of the group sound and, at this particular point, it was a subordinate part. What this means is that my nervous system’s reorganizational activity is responsive to the sound made by each and every person in the musicking group. I am attuning my motor and emotive system to the sound that is the joint activity of this group. And each one in the group is, in turn, doing the same thing. Each one, merely by being a conscious musician, is making minute adjustments to his nervous system in response to the sounds that all are creating.

We are now in territory explored by Walter Freeman in a recent essay on music and social bonding. Freeman is interested in those rituals where a core group of celebrants move from one status in society to another, as from child to adult or single to married. In these rituals, as individuals are conveyed from one social status to another—recall our discussion in the previous chapter—they require changes in the collective neuropil. Funerals, of course, are also in this class. As the bodies of the dead are conveyed to a final resting place, the living must disengage from their attachments to those who are no longer among the living. In this case, and entire persona (see Figure 1 in the previous chapter) must be disengaged from active use in the collective neuropil. Conversely, when a child is born, the group must undertake a ritual that creates a new persona in the collective neuropil.

In all of these situations the bonds between individuals must be altered in fundamental ways that require considerable neural reorganizing. Freeman suggests that such rituals involve a neuropeptide called oxytocin. He asserts that oxytocin "appears to act by dissolving preexisting learning by loosening the synaptic connections in which prior knowledge is held. This opens an opportunity for learning new knowledge. The meltdown does not instill knowledge. It clears the path for the acquisition of new understanding through behavioral actions that are shared with others.” As the oxytocinated individuals are moving to the rhythms of well-established ritual, their synaptic connections are restructured in patterns guided and influenced by the events in the ritual. Obviously, the microdynamics of each individual will be unique; but they will be shaped by rhythmic patterns common to all . These rituals provide a space in which individuals can mold themselves to one another as the infant molds her actions to those of her mother.

Such ritual would likely have benefits on less extreme occasions than those requiring the restructuring of social relations—think of our little jam session. Social life is difficult and taxing. Hostilities build up. Such ritual may well help take the edge off of growing tensions, reconciling individuals to one another and allowing them to “reset” their relationships on more favorable terms.

Thus we have another core hypothesis:

Freeman’s Hypothesis: By attending to one another through musicking, performers attune their nervous systems to one another, restructuring their representations of others. This results in more harmonious interactions within the group.

Each individual consciousness may be an island of Cartesian subjectivity, but in the close coupling of musicking, those subjectivities are intimately and delicately conditioned and regulated by one another.

Perhaps such rituals play a role in helping to establish and maintain the subjective continuity of the neural self. By entering into a wide variety of emotional states (with their various neurochemical substrates) in a socially controlled situation, individuals in a community ritual create an "equal access zone" in mental space where each can experience and contemplate extremes of joy and anger, tenderness and hate, and know that all these feelings have a place in their shared world.

Natural intelligence Revisited: The Five-Fold Way, A Working Paper

New working paper. Title above, links, abstract, contents, and introduction below:

Academia.edu: https://www.academia.edu/165530520/Natural_intelligence_Revisited_The_Five_Fold_Way_A_Working_Paper
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6529398
ResearchGate: https://www.researchgate.net/publication/403545810_Natural_intelligence_Revisited_The_Five-Fold_Way_A_Working_Paper

Abstract: In 1988 David Hays and I published an article entitled, “Principles and Development of Natural Intelligence.” The principles were computational: 1) modal, 2) diagonalization, 3) decision, 4) finitization, and 5) indexing. We made our argument in terms of the principles themselves along with behavioral, neuroanatomical, ontogenetic and phylogenetic evidence. The literature in all those fields has changed enormously in the four decades since we finished writing. To get a read on how our computational proposals have fared, I asked ChatGPT 5.2 to evaluate it against the current literature. Its verdict: the “empirical specifics have aged unevenly but [the] central agenda has held up surprisingly well.” This article presents the five principles, in brief, followed by ChatGPT’s full evaluation. Also, I have asked ChatGPT to evaluate a section on control structure, “Vehicularization,” that we cut from the original argument. Verdict: “vehicularization points toward a more complete account of natural intelligence—one in which cognition is understood as coordinated navigation across multiple, nested domains.”

CONTENTS

Introduction: Constraining Theories and Models 2
The Five Principles of Natural Intelligence 4
Revisiting The Principles and Development of Natural Intelligence (1988) 6
Vehicularization 11

Introduction: Constraining Theories and Models

Sometime in 1985 David Hays and I decided it was time to set forth our views on the nature of, well, of natural intelligence. First, however, we had to discover what those views were. We sat down to a table in my parents’ kitchen and made a list of the various things we wanted to include in this article, experimental findings, observations, models, mathematical ideas, and so forth, from psychology, neuroscience, linguistics, evolutionary biology, and computing. We just wrote them down in no particular order, probably on unlined paper. When we’d accumulated about 50 or so items we decided to gather them into a small number of groups of items that seemed to belong together. We arrived at five groups.

Just how we proceeded from that point I don’t recall. Perhaps we sat around discussing the various groups and came up with a principle for each group. Maybe we had to do some writing first. I don’t recall. But however we actually proceeded, we end with an article we called, “Principles and Development of Natural Intelligence.” We intended “natural” to contrast with “artificial” but didn’t say that anywhere in the article. When we’d finished a draft, days or weeks later, Hays said that it felt like fundamental work; he used the term “bedrock.” I agreed. It took three years to get it published, in a now defunct interdisciplinary journal, The Journal of Social and Biological Structures.

I’ve included the abstract of that article, along with a bit of the introduction, below, as the first part of this document: “Five Principles of Natural Intelligence.” That should give you an idea of the framework without all the expository elaboration, argumentation, and support.

ChatGPT reviews

That was four decades ago. I have continued to like what we did. But has any of it held up? How could it? By now the literature we referenced was 40 years out of date? And, yet, it wasn’t about that literature, it was about how we put it together. Is there anything left of that framework?

About a week ago I asked ChatGPT 5.2 to evaluate it. Here’s the prompt I gave it:

I want you to evaluate a paper that David Hays and I published back in 1988: The Principles and Development of Natural Intelligence. Give me a third-party assessment from the standpoint of what we now know, not a summary and not a defensive reconstruction. Be explicit about where it now looks prescient, where it looks historically bounded, and where it still poses unresolved challenges.

Here’s the first line of ChatGPT’s conclusion:

As of 2026, I would not describe the paper as a correct theory of mind. I would describe it as an ambitious synthetic manifesto whose empirical specifics have aged unevenly but whose central agenda has held up surprisingly well.

I’ll take it. Could I take issue with some of ChatGPT’s criticisms? Sure. But that assessment pinpoints the single most important facet of the essay, its synthetic nature. To push back on ChatGPT’s reservations would blunt that point.

After making various comments on an ad hoc basis, ChatGPT offered to write a “more formal review-essay.” I’ve included that as the second part of this document: “Revisiting Principles and Development of Natural Intelligence (1988).” There’s more.

The article we had submitted was long. The editors asked us to cut what we could, but made no particular suggestions. Our single largest cut was a section on vehicularization – that’s what we called it. It was about control. While it was about the same general line of thinking, it didn’t seem to fit. The bulk of the article was about the five principles and how the developed, both phylogenetically and ontogenetically (in humans). Vehicularization was about how they operated in concert. I have included that as a third section followed by comments by ChatGPT as the fourth and final section.

The Five-Fold Way

Let’s return to ChatGPT’s characterization of the original article as a “synthetic manifesto.” From its conclusion:

It is best understood as an architectural proposal about the structure of intelligence. Many of its mechanistic claims have aged poorly, particularly its neuroanatomical simplifications and evolutionary staging. Yet several of its central insights—heterogeneous cognitive regimes, the integration of regulation and cognition, the interaction between holistic and symbolic processing, and the role of language in cognitive control—remain highly relevant.

Though we didn’t use such phrases when we wrote the article, that’s certainly what Hays and I thought we were doing.

The diagram to the left indicates the range of material we brought to bear in our thinking about natural intelligence. The labels on the vertices of the pentangle are from my 1978 Ph. D. thesis in the English Department at SUNY Buffalo, “Cognitive Science and Literary Theory.” There I somewhat idiosyncratically defined cognitive science as investigating a five-way correspondence between behavior, computation, computational geometry (neuroanatomy), phylogeny, and ontogeny. That dissertation was mostly about behavior, in the form of literary texts, and computation, in the form of cognitive networks semantics, though touched on the others here and there. But “Principles and Development of Natural Intelligence” covered all five. The principles themselves were computational in nature and we made our primary arguments in terms of their ability to account for behavior, but we also suggested which brain regions supported them and related them to the phylogeny of animal behavior and the ontogeny of human development.

By the usual standards of the academy, that range was wide, crazy wide. We certainly weren’t expert across that range; no one could be. However, when I look back in retrospect, it is clear that we weren’t attempting some grand synthesis over that range. We were doing something quite different, something that was and remains fundamentally conservative. We had some high-level ideas about the computational structure of the mind and we wanted to place constraints on those ideas by expanding the range of evidence that could be brought to bear on them. While it is necessary that those ideas account for observed behavior, that alone is not sufficient. The model implied by those ideas must be implemented somewhere in the brain and must be consistent with developmental evidence both from phylogeny, our evolutionary history, and ontogeny, child development. THAT was the central agenda that, in ChatGPT’s estimation, has held up well.

The brain's dopamine response to music peaks in the mid-teens

Your brain peaked musically somewhere around age 16. Everything since then has been a dopamine echo.

Between the ages of 12 and 22, the mesolimbic dopamine pathway, the same circuit that processes cocaine and sex, fires at levels in response to sound that it will never reach… https://t.co/QUxgPiRxps pic.twitter.com/HTsZKp7Ol3

— Aakash Gupta (@aakashgupta) March 16, 2026

What electrochemical machine has 100 trillion connections in a volume the size of a cantaloupe?

That one neuron connects to about 7,000 others. Your brain has 86 billion of them. Do the math and you get somewhere around 100 trillion connections inside your head. More connections than stars in 1,500 galaxies.

And each connection point is way more complicated than anyone… https://t.co/sUkcS7T3rA

— Anish Moonka (@AnishA_Moonka) March 14, 2026

Neurochemicals, brains, sex, and relationships: Rena Malik, M.D., interviews Dr. Jim Pfaus

YouTube:

In this episode, Dr. Rena Malik, MD is joined by neuroscientist Dr. Jim Pfaus to explore the neuroscience of sexual attraction, desire, and bonding. They discuss how early sexual experiences shape our preferences, the role of dopamine and oxytocin in relationships, the impact of hookup culture and pornography, and the science behind sexual synchrony. Listeners will gain insightful perspectives on the brain’s influence over intimacy, pleasure, and partner connection, along with practical takeaways for fostering deeper relationships.

00:00:00 Introduction
00:00:26 Guest background & episode topics
00:01:43 Brain and sexual attraction
00:06:39 First sexual experiences
00:12:08 Navigating bad sexual experiences
00:15:18 Masturbation, porn, and impact
00:23:17 Sexual synchrony and bonding
00:33:31 Orgasm: brain chemistry
00:44:09 Semen retention & arousal
00:51:34 Porn, compulsion, and addiction
01:01:04 Oxytocin and bonding
01:12:20 Neuroplasticity, love, and long-term relationships
01:22:36 Sexual trauma and healing the brain
01:33:10 How hookup culture rewires desire
01:42:44 Takeaways

You can find papers by Dr. Pfaus on ResearchGate.

* * * * * 

This is a fascinating, rambling, and wide-ranging interview. Find a topic that interests you and dig in. Then listen to the whole thing. I’m particularly interested in the discussion of Oxytocin and bonding (starting at 01:01:04 and pretty much going on through to the end).

Why? Because I’ve been thinking about oxytocin ever since I read Walter Freeman’s Societies of Brains: A Study in the Neuroscience of Love and Hate (1995). Freeman was speculating about the role of intense ritual mediated by music and suggested that oxytocin would be released during such rituals and that that would facilitate bonding between the participants. What’s interesting, though, is the mechanism he suggested: Oxytocin released during the ritual would loosen the connectivity between neurons in the brains of individuals. The result would be that, as connectivity was reestablished, the patterns of connectivity within individuals would be similar across the population of participants, thus reducing their differences. I found that fascinating and made it central to my book on music, Beethoven’s Anvil: Music in Mind and Culture (2001), though in a somewhat different form, which I discussed in chapters two and three.

This is quite different from the discussion in the video, which is about pair-bonding between individuals. Malik and Pfaus are interested in the role of post-coital cuddling where bonding between the pair is mediated by oxytocin. Crudely put, when individuals cuddle there’s bonding, no cuddling, no bonding. But individuals can choose whether or not they cuddle, no? Yes. So what’s the range of “viable lifestyle choices”? (Ugh! what a phrase!) We don’t know.

So I’ll trot out my standard metaphor for the relationship between biology and culture: chess. Biology provides the basic rules of the game: the game board, the individual pieces, and the moves each piece is allowed to make. But there is a great deal of latitude in how one deploys those pieces during actual gameplay. That’s where culture comes in. The tactics and strategies one uses, that’s culture.

Biology doesn’t dictate behavior, not for individuals, not for whole societies. But it places constraints on behavior. Those constrains, by the very fact of constraint, open up possibilities for design and structure. This video is about some of those biologically-given constraints. As for the possibilities they open up, that remains to be seen.

I’d say that we have a lot of work to do. Well, it may be work for the scientists. But for the rest of us, it looks like play to me.