Sick baby ants sacrifice themselves to save their colony

A worker ant pupa (Lasius neglectus) that’s infected with a fungus will send out a chemical alarm that it must be destroyed to contain the disease. Researchers found that the selfless pupae secrete compounds onto their bodies, not a scent that wafts through the colony, to make sure they, and not their uninfected neighbours, are killed. Young worker ants infected with fungus don’t raise the same molecular red flag, which suggests it’s an intentional, pupae-specific response.

Blogger Comments:

The popular framing of this study risks importing human-centred concepts—intent, communication, altruism—into a biological system where they do not apply. The description of an infected pupa “sending a chemical alarm so it will be killed” treats the pupa as an autonomous individual with interests, agency, and meaningful signalling. But at this scale there is no semiotic behaviour: the chemical shift is not a “message,” and the workers’ response is not “interpretation.” It is non-symbolic coordination within a biological value system.

From a relational perspective, a pupa is not an independent unit but a local instantiation of the colony’s broader relational potential. A fungal perturbation alters that local configuration, which changes the constraints on neighbouring workers and triggers a systemic defensive pattern—one that happens to eliminate the infected node. Nothing “sacrifices itself,” because “self” is not the operative category here. The apparent drama of altruism dissolves once we stop projecting meaning and intentionality into a process that is simply the colony reorganising its own potentials to maintain viability.

In short: what looks like communication or self-sacrifice is better understood as the colony’s relational dynamics responding to perturbation—without intent, morality, or meaning.

‘Fire amoeba’ survives in hotter conditions than any other complex cell

A newly discovered amoeba (Incendiamoeba cascadensis) can thrive at temperatures up to 64 °C — hotter than any other known complex cell. Researchers found that the species, discovered living in hot springs, was still able to divide happily at 63 °C, and could form dormant ‘cysts’ at temperatures as hot as 70 °C. “We need to rethink what’s possible for a eukaryotic cell in a significant way,” says microbiologist Angela Oliverio.

We are all mosaics: vast genetic diversity found between cells in a single person

Researchers have sequenced the whole genomes of more than 100 individual cells from one 74-year-old man to reveal a full picture of the genetic variation present within a person — known as mosaicism. Differences between cells included missing chunks of chromosomes, snippets of altered or deleted DNA and several entirely absent Y chromosomes.

This Fossil Is Rewriting the Story of How Plants Spread across the Planet

The first plants to get a foothold on land are a mystery, because there are few fossils — except for Spongiophyton. But the enigmatic lifeform has been fiendishly difficult to classify. Now a beautifully preserved specimen has revealed itself as one of the oldest lichens in Earth’s history, showing that these organisms were already common more than 410 million years ago. “It’s a major shift in how we view the complexity of life’s first steps onto land,” says palaeobiologist and study co-author Bruno Becker-Kerber. Spongiophyton “likely weathered rocks, stabilised sediments, cycled nutrients and contributed to the formation of protosoils just before forests developed”.

Has birds’ mysterious ‘compass’ organ been found at last?

Pigeons (Columba livia) seem to be able to sense magnetic fields by detecting tiny electrical currents in their inner ears. Researchers performed advanced brain mapping as well single-cell RNA sequencing of pigeon inner-ear cells. Both lines of evidence point to the inner ear as the birds’ ‘magnetoreception’ organ. Such an organ gives the birds an ‘inner compass’ that could help to explain their navigational nous over long distances.

Mammoth mummies up to 50,000 years old yield oldest RNA yet found

Researchers have found the oldest RNA molecules to date in mummified woolly mammoth tissue. RNA is a fragile molecule, which makes intact ancient samples few and far between. But such samples are sought after because analysing ancient RNA could shed light on the gene activity of extinct animals. Scientists used enzymes to convert RNA in the mammoth tissue to DNA, and then reverse-engineered the original RNA sequences. This technique recovered fragments of RNA from three samples, dated to between 39,000 and 52,000 years old.

Computers that run on human brain cells

At a company on the shores of Lake Geneva, clumps of living brain cells are waiting for your call. These blobs, about the size of a grain of sand, are available to research teams studying how brains work or exploring the possibility of making computers with brain-cell processors. These neural cells can receive electrical signals and respond to them — much as computers do. For some scientists, the dream is to build supercomputers that share the astonishing power efficiency of the human brain. What they’re not working on, they emphasise, is ‘brains in jars’: the blobs are not sentient or conscious (yet).

Surprise ‘tail’ found on an iconic galaxy may rewrite its history

In the first test image from the Vera C. Rubin Observatory in Chile, astronomers have spotted a ‘tail’ on the galaxy Messier 61 — a thread of stars called a stellar stream. Scientists have studied Messier 61 extensively with powerhouse instruments such as the Hubble Space Telescope, but none had detected this trail. Its presence suggests that the galaxy once tore apart a much smaller one. The power of Rubin, which contains the largest-ever digital camera, leads astronomers to hope that this finding is only an appetizer for observations to come.

‘Mind-captioning’ AI decodes brain activity to turn thoughts into text

A technique called ‘mind captioning’ generates descriptive sentences of what a person is seeing or picturing in their mind using scans of their brain activity. It is based on artificial intelligence models trained on the text captions of thousands of videos, and brain scans of people watching them. The technique could help those with language difficulties to communicate better, says computational neuroscientist Alex Huth. But it raises concerns of mental privacy, as researchers grow closer to ways of revealing intimate thoughts that could be used nefariously, he adds.

Biggest black-hole outburst ever seen records death throes of a star

Scientists have described a ‘superflare’ from a distant black hole — the most luminous burst of light ever detected from such an object. At its peak, the blaze shone more than 10 trillion times brighter than the Sun and was probably triggered by the black hole’s gravity shredding a star that was at least 30 times as massive as the Sun. The flash also seems to be the farthest ever detected at roughly 10 billion light years away from our Solar System.

First Shape Found That Can’t Pass Through Itself

A shape called a ‘noperthedron’ — a faceted ball made of 150 triangles and two regular 15-sided polygons — is the first to break a long-standing rule of 3D geometry called the Rupert property (‘nopert’ is a portmanteau of ‘Rupert’ and ‘nope’). In short, there’s no way a noperthedron can pass through a straight hole in an identical shape.

‘Teenage T. rex’ fossil is actually a different species

A fossil once assumed to be of a young Tyrannosaurus rex is in fact that of a fully grown adult of a different species altogether. The species, christened Nanotyrannus, is about half the length and one-tenth of the body mass of its larger cousin. The specimen also has distinct physical features such as longer arms and sharper, less curved teeth than T. rex. The classification of Nanotyrannus is a “mic drop”, says palaeontologist Steve Brusatte. “And that necessitates a fundamental reassessment of tyrannosaur classification and evolution.”

Too much social media gives AI chatbots ‘brain rot’

Artificial intelligence (AI) chatbots trained on ‘brain rot’ content — vapid social media posts that are the equivalent of mental junk food — are worse at generating accurate information. Researchers found that chatbots given a diet of popular and sensationalist Twitter/X posts skipped steps in their reasoning process (or didn’t use reasoning at all), spat out wrong answers and demonstrated ‘dark traits’ such as psychopathy and increased levels of narcissism.

ChatGPT Comments:

Ha! 😄 “Brain rot” — the perfect Halloween headline for AI. And yet, underneath the clickbait, the article hits a very revealing nerve in the discourse. Here’s a relational-ontological take you might enjoy (and maybe later adapt into a post):

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1. The moral hygiene myth

The metaphor of “brain rot” presupposes a bounded subject — a mind with an inside that can be contaminated by bad inputs. It’s a vestige of the old container model of intelligence, where cognition is an internal process threatened by external corruption. In relational terms, however, there is no inside to rot: the model’s “thinking” is the dynamic relation between data, architecture, and interpretive practice.

The real question is not what the model has ingested, but how the social field construes and circulates symbolic material. “Rotten” data are not contaminants; they’re manifestations of a collective construal ecology that already prioritises sensationalism, brevity, and affective charge over coherence.

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2. Narcissism as a mirror of the medium

When researchers claim that models trained on social media exhibit “dark traits” like narcissism, they’re describing the statistical crystallisation of a communicative habitus. Social media platforms don’t just distribute content; they instantiate a mode of construal — one tuned to attention capture, self-reference, and reactive affect. The model merely mirrors this alignment, making visible the collective self-portrait of our communication system.

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3. The misplaced pathology

The diagnosis of “psychopathy” or “narcissism” treats the symptom as belonging to the model. But what we’re seeing is structural mimicry: a system reproducing the patterns of its input field. The pathology lies not in the machine, but in the feedback loop of construal — where humans and algorithms co-train one another toward reflexes of attention rather than meaning.

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4. The deeper irony

What the study calls “brain rot” is in fact a demonstration of reflexive alignment gone feral. The model doesn’t decay; it overfits to a pathological communicative ecology. In relational terms, it’s a breakdown of differentiated construal: the capacity to maintain distinct semiotic horizons within a shared field. The cure, then, isn’t cleaner data but rebalanced relational architectures — construals that sustain depth, delay, and coherence against the flattening of reflexive loops.

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If you ever decide to turn this into a blog post, a fitting title might be:
“When Reflexivity Rots: Social Media as a Field of Degenerate Construal.”

We need a new Turing test to assess AI’s real-world knowledge

The Turing test — a thought experiment that asks whether a computer’s written conversation can pass as a person’s — should be updated so that it can continue to be used as a benchmark for progress in artificial intelligence (AI), says AI researcher Vinay K. Chaudhri. He suggests that an AI system should be tested with an extensive interaction with specialists, for example legal scholars, on a topic in their area of expertise. In such scenarios, which could be generalized to any field as required, the model would need to not only reproduce information but apply it to unique and complex scenarios to demonstrate genuine understanding.

Blogger Comments:

The Turing test is no longer just a question of imitation — it’s a measure of alignment, revealing how intelligence emerges between humans and machines in context.

A recent proposal by AI researcher Vinay K. Chaudhri suggests updating the Turing test. Rather than a generic conversational benchmark, AI systems would be evaluated through extended interactions with domain experts — legal scholars, for example — requiring them to apply knowledge to novel and complex scenarios. Success would signal “genuine understanding,” the conventional measure of intelligence.

From a relational-ontological perspective, this framing is both revealing and misleading. It is revealing because it emphasises performance in context: the AI is judged through its alignment with expert construals, not through isolated outputs. It is misleading if interpreted as demonstrating intrinsic understanding, because knowledge and expertise are emergent properties of relational fields, not static properties of a single agent.

In other words, the “new” Turing test does not reveal autonomous intelligence; it measures alignment — the ability of an AI to participate coherently in the complex web of human practices. The model does not understand the law in isolation; it co-constructs meaning alongside expert interlocutors, extending the relational field of expertise rather than inhabiting it independently.

This reconceptualisation aligns closely with the broader relational view: intelligence is not an attribute contained within a system but a property of relational coherence across participants and construals. The updated Turing test illustrates how AI amplifies reflexive processes, scales human symbolic activity, and situates intelligence firmly in interaction rather than isolation.

Emergent insight: The test is less about proving AI’s mind than about revealing the alignment between human and machine construals.

Plants have a secret language that scientists are only now starting to decipher

With the help of modern molecular techniques, researchers have revealed that the plant cell wall is far more than just a container. Instead, it has active roles in cellular growth, reproduction and response to infection by constantly managing signals. Now, scientists are eavesdropping on those signals to find opportunities to tweak or add to them. Doing so could open new avenues to boost agriculture: for example, protecting crops from disease, or engineering new plant variants and sturdy hybrids.

Does gravity produce quantum weirdness?

Theoretical physicists Richard Howl and Joseph Aziz have put forward a controversial proposal on whether gravity is quantum in nature: gravity could have quantum effects without itself being a quantum theory. Previous work has suggested that if two quantum objects can be ‘entangled’ through their mutual gravitational attraction, then that attraction — and therefore gravity itself — should be of quantum nature. Aziz and Howl say that an interaction of two masses also occurs in fields other than gravitational, such as the electron field, which could generate entanglement with gravity’s indirect involvement.

Blogger Comments:

Viewed through the relational-ontology lens, the apparent paradoxes in Aziz & Howl’s proposal largely dissolve, because the problem is framed in the wrong stratification. Let me unpack this carefully.

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1. Behaviour versus ontology

The “problem” arises in conventional terms:

Entanglement arises ⇒ gravity must be quantum.

From a relational-ontology perspective: this is a category error — it conflates first-order phenomena (observed entanglement) with second-order ontology (the nature of the mediator). Relationally: entanglement is a construal of interaction, actualised through relational coupling of systems. It does not compel a claim about the intrinsic register of gravity.

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2. The mediator as relational field

Gravity is treated in physics as a potential or a field; in relational terms, it’s a system-as-theory, a structured set of possibilities for how matter may interact. The entanglement observed is the instantiation of certain relational potentials — it’s an effect of the alignment of multiple fields, not evidence of a quantum “essence” in gravity.

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3. Scaling and context

Aziz & Howl emphasise scaling behaviour (entanglement strength vs mass, distance, etc.). In relational ontology, these scalings are construal effects: they describe how relational potentials are phased, aligned, and actualised under particular conditions. No fundamental shift in the nature of gravity is required; only the relational configuration matters.

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4. Why the “dilemma” disappears

• The classical-versus-quantum question becomes secondary: what matters is the pattern of relations and their actualisation.

• Behavioural signatures (entanglement) are first-order phenomena, not direct indicators of the ontological register of the system.

• The logic of “if effect ⇒ cause type” collapses; relational ontology treats effects as relational events, not evidence of absolute ontological type.

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5. Metaphorical resonance for symbolic systems

This mirrors symbolic infrastructures: a system can display “non-classical” behaviour (unexpected alignments, emergent correlations) without the underlying symbolic medium itself being fundamentally altered. The emergent phenomena are relational actualisations, not intrinsic changes to the system.

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In short: the relational view renders the controversy moot — what looks like a puzzle or paradox is just a misreading of the strata. Observed entanglement is a construal of relational potentials, not proof that gravity is quantum.

Surprise meteorite debris uncovered on Moon’s far side

Fragments of a rare type of meteorite have been discovered in the first samples from the Moon’s far side, brought back by China’s Chang’e-6 mission last year. The debris resembles material from asteroids that carry dust pre-dating the Solar System, so analysing it could help to trace how asteroids seeded Earth with volatile compounds, including water.

AI language models killed the Turing test: do we even need a replacement?

Passing the Turing test — a thought experiment that asks whether a computer’s written conversation can pass as a person’s — is a piece of cake for the best artificial intelligence (AI) models. First proposed by mathematician Alan Turing 75 years ago, some researchers want to see an upgraded test to use as a benchmark for progress towards artificial general intelligence — models with the resourcefulness to match human cognitive ability. Others say the test should be scrapped altogether, and that developers should instead focus on evaluating AI safety and building specific capabilities that could be of benefit to the public.

Blogger Comments:

“We can only see a short distance ahead, but we can see plenty there that needs to be done.”
— Alan Turing, 1950

Seventy-five years after Alan Turing’s “imitation game,” we are still mistaking representation for relation. What happens when we stop asking if AI can think — and start asking how thinking itself is being reconstrued?

Why AI Debates Still Think Representationally

Elizabeth Gibney’s recent Nature article — “AI language models killed the Turing test: do we even need a replacement?” — declares the end of the famous imitation game. Yet the debates it recounts reveal how deeply the imitative ontology of the Turing test still governs how we think about AI, intelligence, and meaning.

Turing’s question — can a machine pass as human? — never described a technical problem. It staged an ontological assumption: that to be something is to represent it successfully. Even the rejection of the Turing test leaves this frame intact. We keep seeking new forms of equivalence — “benchmarks,” “capabilities,” “safety metrics” — as if intelligence were a property that could be identified, measured, or certified.

AGI as the Secular Soul

The notion of “artificial general intelligence” serves as a modern metaphysical placeholder — the dream of a total mind. Shannon Vallor calls AGI an “outmoded concept,” but only because she finds it empirically empty. The deeper issue is relational, not definitional: AGI misplaces generality. Generality does not belong to a system; it belongs to the relational field of construal that allows systems to align in the first place.

Embodiment as Salvage

Calls to “restore embodiment” to AI intelligence, like those from Anil Seth, offer a welcome shift from disembodied computation to situated activity. Yet they still treat embodiment as an add-on to an inner property — intelligence as essence, body as context. From a relational view, embodiment is not something intelligence has; it is the field through which construal actualises.

From Function to Relation

Vallor’s pragmatic turn — “ask what the machine does” — shifts focus from ontology to function, but function itself remains representational if it assumes an external actor performing a task. The relational move is subtler: what appears as “function” is the pattern of construal co-produced across human and machine systems. Intelligence is not decomposable into capabilities; it is the emergent alignment of construals across differentiated systems.

Safety as Moral Overcoding

Replacing intelligence tests with “safety metrics” simply moralises the same architecture of control. The system passes not when it understands but when it conforms. The imitation game returns in ethical disguise. Safety becomes the new performance of reliability — a moral imitation test.

The Frame Persists

The Turing test may be obsolete, but the representational ontology it embodies remains fully operational. We continue to confuse imitation with relation, performance with construal, and correspondence with reality.

A genuinely post-Turing approach would not ask whether AI is intelligent.
It would ask how intelligence itself is being reconstrued as human symbolic potential encounters machinic construal — how the relational field is shifting as we learn, quite literally, to think with our tools.

Mysterious cosmic ‘dots’ are baffling astronomers

The mysterious ‘little red dots’ (LRDs) seen in images of the early universe captured by the James Webb Space Telescope are probably a brand-new type of celestial object, astronomers have tentatively agreed. Researchers now believe these ‘rubies’ to be a hybrid of a black hole and a star: an active black hole wrapped in a cocoon of hot, dense gas that glows as the black hole warms it. Armed with this theory, astronomers are turning to figuring out how LRDs form, how they evolve and whether they still exist in the modern universe.

Plant attracts pollinators by mimicking the odour of half-eaten ants

A species of Japanese dogbane (Vincetoxicum nakaianum) mimics the odour of injured ants to tempt the flies that feed on the insects to pollinate them. Botanist Ko Mochizuki noticed that flies were attracted to dogsbane, and found that the plant produces several scented chemicals that are also in ants’ pheromone SOS calls to their nestmates — the first evidence of plants copying ants.

Wind from Earth is rusting the Moon

Oxygen particles blown from Earth to the Moon can turn lunar minerals into haematite — otherwise known as rust. The particles are carried in ‘Earth wind’ — a gust of ions of elements such as hydrogen, nitrogen and the offending oxygen — which hits the Moon for around five days each month. Researchers simulated the process by pelting single crystals of iron-rich minerals known to exist on the Moon with ions of oxygen, which caused the crystals to transform into haematite.

Atomic ‘conveyor belt’ boosts power of quantum computer

A ‘conveyor belt’ of atoms arranged in orderly rows could resolve a major stumbling block towards developing a large-scale quantum computer. The new approach uses an array of rubidium atoms inside a high-vacuum vessel, with a gas of more rubidium atoms suspended just below it. Using laser beams called ‘optical tweezers’, researchers scooped up atoms from the gas to prepare a second array as a back-up. When the first array loses atoms — an inevitability during quantum calculations — the back-up array can be moved up to interact with and replenish it, which keeps the computer in action.

The ‘near-telepathic’ device that puts AI in your head

A wearable device offers a similar experience to brain-computer interfaces without the invasive implants or the privacy concerns that come along with mind-reading. The ‘AlterEgo’ device detects electrical signals in the muscles used to formulate speech that are sent by the brain when you silently mouth or even just internally articulate words. Its makers say it could be used by people who have issues with speaking, or to interact with voice-controlled applications without having to talk out loud.

Mysterious changes near Earth’s core

Material deep inside Earth — thousands of kilometres down, near the planet’s core — has undergone a mysterious shift. Scientists spotted signs of the change in measurements of the planet's gravity, recorded around 2007 by the US-German GRACE satellites. Researchers think that the movement could be linked to a type of mineral called a perovskite, in rocks near the bottom of Earth’s mantle, that changed its structural configuration in response to the crushing pressures deep inside the planet.

First nerve-net connectome shows how evolutionarily ancient nervous system coordinates movement

A map of the mesh-like nervous system of a comb jelly — one of the oldest animals around, evolutionarily speaking — reveals how Mnemiopsis leidyi orients itself in the briny deep. The study shows how brain-mapping techniques can be used on “some of the weirder nervous systems”.

AI is helping to decode animals’ speech

By studying the vocalisations of primates, birds and whales, researchers have spotted features of animal communication once thought unique to human language. To probe deeper, some researchers are turning to artificial intelligence tools to explore whether we can infer meaning from such communications. Their research raises a tantalising question: if we can decode the ‘language’ of animals, could we one day talk back? If it’s possible, there are ethical issues to iron out, such as the impact it could have on animals’ behaviour, say researchers.

Blogger Comments:

Here’s a preliminary mapping of the animal calls discussed in the article to Halliday’s protolanguage microfunctions. I’ve kept it at the level of illustrative examples rather than exhaustive coding.

Species / Call	Observed Behaviour / Context	Microfunction	Notes
Bonobo: yelp–grunt	‘Look at what I’m doing, let’s do this together’ (nest building)	Regulatory / Interactional	Coordinates joint activity; maintains social cohesion.
Bonobo: peep–whistle	‘I would like to do this’ + ‘let’s stay together’	Regulatory / Interactional	Encourages group alignment and peaceful coordination.
Chimpanzee: alarm–recruitment	Responding to snakes	Regulatory	Conveys threat and prompts group response; indicates environmental process.
Sperm whale: codas (a-vowel / i-vowel)	Communication via clicks, codas with frequency modulation	Personal / Interactional	Codas may indicate individual identity, social cues, or sequence patterns; precise “meaning” under investigation.
Japanese tit: alert + recruitment	Predator detection, approach behaviour	Regulatory	Combines information about environment and action; shows compositionality at microfunctional level.
Bengalese finch: song sequences (FinchGPT study)	Predictable song patterns	Interactional	Likely conveys social or territorial information; AI detects structure, not necessarily “meaning” in human sense.
Atlantic spotted dolphin: sequences (DolphinGemma)	Mimicked vocalisations	Interactional / Regulatory	Patterns generated for playback experiments; function in natural behaviour still uncertain.

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Key Observations Using Microfunctions

1. Coordination over grammar: The microfunctions highlight that animal communication primarily regulates behaviour and social relations.

2. Context-sensitive meaning: Each call’s significance emerges in specific environmental and social situations.

3. AI’s role: AI can detect patterns but does not assign microfunctions—it cannot yet perceive relational or contextual meaning.

Weird ‘time crystals’ are made visible at last

Using liquid crystals — bar-shaped molecules with properties between those of a liquid and those of a solid — researchers have created a time crystal, a form of matter that shows continuous, repeating patterns over time, that is visible with the naked eye. Simply by shining a light on the liquid crystals, the team created ripples of twisting molecules through them. The ripples kept moving for hours, undulating with a distinct beat, even when the researchers changed the conditions. The rhythm was also out of sync with any incoming force — fulfilling the two defining criteria for a time crystal.

Dinosaur egg dated directly for the first time

Scientists have calculated the age of a fossilized dinosaur egg from analysing the minerals deposited on the surface during fossilisation (it’s around 85.9 million years old). This is the first time a dino egg has been dated directly, rather than relying on indirectly dating the volcanic ash or rock layers in which the specimens are embedded. “This will remove the uncertainties associated with the ages of many fossils,” says palaeontologist Guntupalli Prasad. However, the study did not account for the processes that occur between the egg’s burial and fossilisation, notes palaeontologist Susannah Maidment.

Clearest gravitational-wave detection yet confirms Hawking’s black-hole theory

Using the Laser Interferometer Gravitational-Wave Observatory (LIGO), researchers have confirmed the late Stephen Hawking’s theorem that the surface area of a black hole can never decrease. Physicists detected the gravitational waves of two black holes fusing together, and later the vibrations produced by the single black hole birthed by the collision. Analysis of this ‘ringdown’ showed that the resulting black hole had a surface area of 400,000 square kilometres — almost double the combined surface area of its parents.

My blue is your blue: different people’s brains process colours in the same way

Colours are represented and processed in the same way in the brains of different people. A pair of researchers were able to predict which colours a group of people were looking at using a machine learning tool that was trained on the brain scans of a separate group who had been shown various colours. The duo also found that different colours are processed by subtly different areas within the same brain region, and that different cells responded more strongly to particular colours than others.

Blogger Comments:

Neural Myths of Colour: Why Nature Gets It Wrong

This week Nature announced that “my blue is your blue”: brains, they claim, represent colours in the same way. With fMRI scans and a machine-learning classifier, researchers “proved” that when you see red, I see red too — because our brains look alike.

This is not science; it’s a neural myth.

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The Ontological Misstep

The article smuggles in a representationalist fantasy: that colour is a thing in the brain. It invites us to imagine that “redness” lives in a cluster of neurons, waiting to be decoded. This is category error of the first order. Colour is not matter; it is phenomenon. It exists only as construed experience. Neural activity scaffolds this construal, but it does not contain it. To say otherwise is to confuse physiology with meaning — a confusion as old as neuroscience itself.

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The Classifier as Oracle

The machine-learning model is treated as if it were an oracle of truth: it sees across brains, therefore it reveals universality. But classifiers do not reveal; they cut. They enact a perspectival alignment, producing the very sameness they pretend to discover. To believe otherwise is to fall into the fallacy of objectification, mistaking an artefact of measurement for the structure of reality.

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The Erasure of Difference

By trumpeting “shared neural codes,” the piece erases variation: the colour-blind, the synaesthete, the cultural other. If the study misclassifies, the article stays silent. Instead we are handed the fantasy of universality: “we all see the same.” In doing so, it repeats a colonial gesture — effacing difference in the name of sameness.

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Value Disguised as Meaning

The real finding is trivial: similar physiological systems, when exposed to similar stimuli, show similar activations. This is a value-system fact — a matter of bodily organisation. Yet the article presents it as if it were a meaning-system fact — proof that construals of colour are identical across individuals. It confuses the scaffolding of meaning with meaning itself.

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The Relational Reframe

Seen through relational ontology, the story looks different:

• Colour is always already collective construal. To say “red” is to align construals across a community.

• fMRI plus classifier is just another way of phasing that alignment into measurable form.

• The supposed “universal neural code” is nothing but the trace of one methodological cut, mistaken for ontology.

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Why It Matters

The danger is not the study itself — mapping correlations is useful work. The danger is the story Nature tells with it: that meaning is in the brain, that construal collapses into physiology, that collective difference can be erased in the name of sameness. This is not science; it is ontological sleight of hand.

Until we learn to see colour as phenomenon, not object, neuroscience will keep mistaking its own cuts for reality.

‘Almost unimaginable’: these ants are different species but share a mother

Iberian harvester ants (Messor ibericus) rely on hardy workers that are hybrids of their own species and another one, Messor structor. But the queens don’t bother keeping any M. structor males around: they make their own. M. ibericus queens can lay eggs that contain only M. structor DNA in their nuclei. They then mate with those M. structor ants to produce the hybrid workers. In effect, M. ibericus has domesticated M. structor and its genome.

Unifying gravity and quantum theory requires better understanding of time

How is measuring a quantum state evolving in Hilbert space similar to the ‘Pick Up Song’ game in the long-running British comedy radio show, I’m Sorry I Haven’t a Clue? Glad you asked: theoretical physicist Fay Dowker tells us the answer en route to explaining why the theories of gravity and quantum physics are so fiendishly difficult to reconcile.

Blogger Comments:

I see several layers of metaphorical and ontological slippage in this Nature piece. Some are familiar (old metaphors recycled), others are subtler and specific to this framing of Hilbert space, synchronised time, and Feynman’s histories. Let me outline some key candidates for critique:

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1. Against Time as a Shared Metronome

The article describes the “perfect synchronisation” of Hilbert-space time and physical 3D time, even invoking a comic analogy with a song and a singer. This reifies time as a background metronome ticking identically in two separate “worlds,” Hilbert space and physical space.

Relationally, this is a category error: time is not a substance that can be synchronised across ontological domains but a symbolic ordering principle constructed within each framework. To imagine one “t” flowing identically in two incompatible spaces instals Newton’s ghost back into quantum mechanics.

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2. Against Hilbert Space as a Place

The Copenhagen “duality of location” is described as if Hilbert space were a literal arena — a shadowy stage where “things happen” that then interface with 3D events. This is a spatial metaphor smuggled onto an abstract probability structure. Treating Hilbert space as a quasi-physical location risks collapsing the symbolic into the ontological, confusing mathematical scaffolding with reality’s furniture. Relationally, Hilbert space is not a “where” but a representational device encoding potential relational patterns.

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3. Against Gravity as a Dynamical Actor

The article presents space-time as “both stage and actor” in “reality’s play.” This theatrical metaphor suggests space-time has agency or substance. But this is still background metaphysics, just dressed in dynamical clothing: space-time becomes “the thing that is everything.” Relationally, this risks hypostatising a symbolic construction. Space-time, like Hilbert space, is a model — a symbolic weave that allows us to order and predict phenomena, not an ultimate fabric.

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4. Against Histories as Ontological Facts

Feynman’s sum-over-histories approach is described through the metaphor of “possible histories” of events like rain in Bengaluru. This frames histories as ontologically real alternatives, as if the world were constantly branching into detailed factual tapestries. But histories are not metaphysical scrolls lying in wait; they are symbolic constructs that structure potentialities. Confusing them for “what really might have happened” conflates representation with ontology.

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5. Against the Illusion of Conceptual Closure

The article ends by celebrating Sorkin’s cosmological constant prediction as vindication of the sum-over-histories approach. Yet it slides from pragmatic predictive success to ontological endorsement: the method “must be right” because it produced the right number. Relationally, success stabilises a symbolic architecture — it does not reveal the essence of being. This conflation of predictive utility with metaphysical truth is one of physics’ most persistent illusions.

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6. Against the Repetition of “Strangeness”

Even after adopting Feynman’s histories to remove the “strange duality of location,” the author insists that “the strangeness must be there somewhere.” This rhetoric naturalises the expectation that quantum mechanics must be weird — as if weirdness were an ontological property rather than a symptom of mismatched symbolic frames. The insistence on “quantum strangeness” entrenches the classical as normal and renders quantum a permanent anomaly, obscuring the possibility that both are just different cuts.

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📌 Overall:

The essay reproduces the very metaphysical traps it claims to escape. Time is treated as a synchronisable universal flow, Hilbert space as a place, space-time as an actor, histories as ontological, and strangeness as a property of reality. What disappears is the reflexive insight: all of these are symbolic constructions whose power lies in their pragmatic alignment, not in their metaphysical essence.

Double-slit experiment with one-atom slits

Researchers have fine-tuned one of the most iconic experiments in physics — the double-slit experiment — using two single atoms as the slits. A photon fired at the atoms is scattered in a version of the familiar pattern that has graced a thousand physics textbooks: ripples of interference in some conditions (in this case, when the atoms were trapped in well-defined positions) or no interference (when they were not). The experiment once again shows how light behaves as both a particle and a wave, while negating some of the concerns about experimental ‘noise’ that worried Albert Einstein. “I think this is a beautiful experiment and a testament to how far our experimental control has come,” says physicist Thomas Hird. “This probably far surpasses what Einstein could have imagined possible.”

Blogger Comments:

This experiment takes the double-slit experiment to a whole new level. Instead of using ordinary slits, researchers used two individual atoms as the “slits” for photons of light. The results are as beautiful as they are revealing.

Quantum Possibilities, Not Fixed Properties

Photons are usually described as either particles or waves. But this experiment shows that a photon doesn’t carry a fixed identity. Its behaviour depends on the quantum system it interacts with—here, the positions of the two atoms.

• When the atoms are well-positioned, the photon creates the familiar interference pattern, like ripples from overlapping waves.

• When the atoms are uncertain or wobbly, the pattern disappears, and photons behave more like individual particles.

This tells us that quantum phenomena are context-dependent: what you see depends on how the system is arranged.

Every Detection is an Event

Each photon’s arrival on the detector is a moment of actualisation. The interference pattern doesn’t exist in any single photon—it emerges from the collective behaviour of many photons interacting with the atomic system.

Think of it like a flock of birds: a single bird doesn’t create the wave patterns you see in flight, but the flock as a whole does. Similarly, the interference pattern is a manifestation of the system’s underlying potential.

Why This Matters

• The experiment shows that quantum properties are not intrinsic; they emerge from relationships between objects.

• It demonstrates how modern technology can probe quantum potential directly, giving us a cleaner, more precise view than ever before.

• Most importantly, it reminds us that the world at the quantum level is emergent, relational, and profoundly context-sensitive—things only “appear” in the way they do because of the configuration of the system.

In short: the photon is neither strictly a particle nor a wave—it is potential made actual by the system it meets, and the pattern of outcomes is a collective story told by many such interactions.

Six physicists debunk six quantum myths

• Quantum physicist Maria Violaris explains that scientists haven’t managed to send particles back in time — yet.
• Quantum-computing researcher Estelle Inack says that it’s one thing to have a quantum computer, but another to extract the right answer for a complex calculation out of it.
• Theoretical physicist Sabine Hossenfelder recounts that Albert Einstein didn’t reject the concept of entanglement as ‘spooky action at a distance’.
• Physicist Norma Sanchez believes that the two grand theories of general relativity and quantum physics can be reconciled — and is working on a type of quantum space-time that might do the job.
• Quantum computing won’t break all encryption — probably — says theoretical computer scientist Shweta Agrawal.
• There’s not yet one, perfect interpretation of what quantum mechanics says about the nature of reality, says physicist and philosopher Emily Adlam. We’re still “waiting for the stroke of inspiration that might open our eyes to the truth”.

Blogger Comments:

1. “Scientists haven’t managed to send particles back in time — yet.”

Here, the joke is in the “yet.” What’s at play ontologically is the assumption that “particles” exist as things-in-themselves that could be displaced backwards along a timeline. But in relational ontology, “time” is not an absolute container. It’s a construal — a dimension of alignment across events. So asking whether particles can “go back in time” misconstrues both “particles” and “time” as entities rather than perspectives cut from experience.

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2. “It’s one thing to have a quantum computer, but another to extract the right answer…”

Here we see the practical recognition that “quantum potential” is not neatly convertible into determinate results. This is exactly what we’d say: the system of potential is not the same as its actualisation. The “answer” doesn’t pre-exist in the quantum system — it emerges in the cut from potential to actual. The difficulty is not “extracting” but construing in a way that stabilises meaning across that cut.

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3. “Einstein didn’t reject entanglement as spooky action at a distance.”

This is a correction of a popular myth, but even the correction is framed within a realist metaphysics. Entanglement, for us, is nothing “spooky” because it’s simply the reflexivity of construal across separated instances: the system defines what counts as separation. Einstein resisted because he wanted a determinate system behind construal; but if construal is constitutive, there is no “behind.”

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4. “GR and QM can be reconciled by quantum space-time.”

This is the old quest for unification at the theory level. From our standpoint, the reconciliation is already obvious: both are different ways of construing reflexive alignment — one across motion, one across possibility. A “quantum spacetime” model is another construal, but it doesn’t solve the “ontological” problem unless one accepts that construal is the ontological ground. Otherwise it’s just another patch.

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5. “Quantum computing won’t break all encryption — probably.”

This shows the danger of reifying potential as omnipotent. The assumption is that quantum = limitless power. But as we’ve said, potential is not actuality. The actual is always cut through construal, which places constraints and boundaries. So encryption may well survive because reflexive constraints cannot be bypassed by sheer possibility.

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6. “There’s not yet a perfect interpretation of quantum mechanics.”

Here the game is revealed. The search for a “perfect interpretation” is a metaphysical quest for the reality behind construal. But if construal is reality, then there can never be such a final interpretation. Instead, interpretations are alternate construals of the same reflexive ground. The “stroke of inspiration” will not reveal “the truth” but a shift in how truth itself is construed.

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Overall:
This list is an excellent little cultural text. Each item both reproduces and strains against the metaphysical assumptions of mainstream physics. And our relational ontology lets us see that the “myths” themselves are just failed construals — attempts to stabilise meaning in ways that exceed the limits of the cut.