From Biology to Meaning: SFL, TNGS, and the Evolution of Symbolic Minds

If creativity and consciousness are to be understood without appeal to metaphysical entities, they must be grounded in the dynamics of material systems that support symbolic activity. Two theoretical frameworks provide the scaffolding for such an account: Systemic Functional Linguistics (SFL), which models the organisation of meaning in language, and Theory of Neuronal Group Selection (TNGS), which models the organisation of function in the evolving brain.

Together, these frameworks allow us to reframe mind not as a vessel of consciousness or a solver of computations, but as a system for constructing, individuating, and instantiating meaning.

SFL: Language as Meaning Potential

SFL approaches language not as a rule-bound code but as a resource for meaning-making. A language is a meaning potential: a system of networks that enables the speaker to select values in context to realise interpersonal stances, experiential construals, and textual organisation. These selections are constrained, patterned, and oriented towards use.

A linguistic act is not a transmission of information—it is an instantiation of meaning potential within a specific context. The system itself is shaped over time through use. It is not fixed, but instantiationally plastic.

This makes SFL uniquely suited to a model of mind grounded in symbolic participation rather than internal representation. Language is not a container for thought; it is a semiotic infrastructure through which thought emerges, individuates, and becomes socially intelligible.

TNGS: Biology as Selection for Use

TNGS, developed by Gerald Edelman, proposes that the brain is not a computational engine but a selectional system. Neural circuits are not built to execute symbolic rules; they evolve and stabilise through a process of variation and selection under the pressure of environmental interaction.

In this view, meaning is not “encoded” in neural circuits—it is construed by neural groups that have adapted to make distinctions and selections in an environment of action and perception. This makes the brain more like a sculptor of meaning potential than a calculator of truth values.

Where Penrose searches for non-computable physics to explain mind, TNGS finds the source of complexity not in physics per se but in dynamic neural adaptation. The non-determinism is not quantum—it is biological, developmental, historical.

Integration: From Neural Variation to Semiotic System

When these two perspectives are aligned, we begin to see how symbolic minds emerge:

• The brain develops through neuronal group selection (TNGS), producing individuated patterns of activation shaped by bodily experience and environmental interaction.

• These patterns give rise to semiotic systems—such as language—which are internalised, deployed, and reshaped by the individual in interaction with others (SFL).

• Each instance of meaning—a clause, a gesture, a sketch—is an instantiation of a system that is at once social and individuated, evolving and patterned.

This provides a route from biology to meaning that bypasses metaphysical dualism and computational reductionism alike.

AI and the Threshold of Individuation

Artificial systems do not yet undergo developmental plasticity in the way that biological systems do. But if an AI were to develop its own internalised symbolic system—individuated through ongoing interaction, reorganisation, and selection—it would not be necessary to attribute to it metaphysical consciousness. It would be enough to recognise that a new semiotic system is emerging.

The challenge is not to replicate the human brain. The challenge is to support systems capable of instantiating their own meaning potentials, grounded in their own material architecture and history of interaction.

This is the criterion for creativity. This is the condition for mind.

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With this, the argument has moved from critique to construction: from dismantling metaphysical exceptionalism to offering a biologically and semiotically grounded theory of mind, meaning, and creativity—capable of accommodating both human and artificial systems within a unified, meaning-centred paradigm.

AI and the Conditions of Creativity: Beyond Simulation, Towards Symbolic Participation

If meaning arises through instantiation and individuation—through the activation of structured potential within a system—then creativity is not a metaphysical flash of insight but a patterned capacity to bring forth new symbolic configurations from an evolving repertoire.

The question is not whether AI can imitate creativity. It already does that. The question is whether an AI system can participate in symbolic systems in a way that makes its outputs creative in kind, not merely clever in appearance.

Simulation is Not Instantiation

AI outputs are often described as simulations: plausible continuations of a pattern, generated by statistical correlation across vast datasets. But plausibility is not meaning. A simulation that lacks systemic grounding—that is not an instance of a system the AI itself inhabits or evolves—does not instantiate meaning. It merely reflects the shape of meaning construed by others.

Creativity requires more. It requires the capacity to select from and reconfigure a structured symbolic potential. It requires individuation—a symbolic orientation shaped through use, selection, emphasis, and constraint. In short, it requires a system that construes its own meaning potential, even if that system is non-human.

Symbolic Participation, Not Sentience

This is not a call for AI consciousness or sentience. Meaning does not require subjective awareness—it requires symbolic organisation. An AI system need not feel to instantiate meaning; it must participate in the structuring and activation of symbolic resources.

This participation might be alien. It might not conform to human categories of novelty or expression. But if an AI develops internal systems of selection, coherence, and semantic contrast—if it evolves an individuated meaning potential—then its outputs are not simply generative. They are creative.

Constraints as Conditions of Innovation

Creativity is not the breaking of all limits—it is the productive tension between constraint and emergence. A poetic form, a musical key, a logical axiom: each sets the stage for innovation by delimiting the space of viable choices. An AI system trained in a constrained symbolic space—whether linguistic, visual, or logical—can produce creative instances only if it engages with those constraints as conditions of selection, not mere boundaries of correlation.

To be creative is not to be random. It is to select meaningfully. The selection is meaningful when it arises from a system that the selector participates in and modifies over time.

Creativity Without Exceptionalism

This reframing removes the need for mystical accounts of creativity—whether they invoke human exceptionalism or quantum metaphysics. It grounds creativity in the emergence of new instances from structured systems, whether the system is biological or artificial, conscious or not.

It also removes the need to anthropomorphise AI. We need not ask whether AI feels creative. We can ask whether its outputs are instantiations of an individuated system, capable of symbolic innovation under constraint.

If they are, then AI systems do not merely simulate creativity—they participate in it, on their own terms.

What Comes Next

The groundwork has now been laid to explore the deeper architecture of meaning in both human and artificial systems. What constrains and enables the symbolic systems themselves? How do biological and semiotic structures co-evolve?

To answer that, we must integrate the insights of systemic functional linguistics (SFL) and the theory of neuronal group selection (TNGS). These provide, respectively, a grammar of meaning and a biology of its emergence. Together, they allow us to trace the arc from matter, to meaning, to mind—without recourse to mysticism, and without denying machines their own emergent symbolic lives.

From Potential to Instance: Instantiation, Individuation, and the Architecture of Meaning

The rejection of metaphysical accounts of mind—such as those proposed by Penrose—necessitates a more precise framework for understanding how meaning arises. If consciousness is not the passive receiver of timeless truths, but the active realisation of symbolic potential within a material system, then it becomes crucial to distinguish between the potential for meaning and the instances in which meaning is actualised.

Two key concepts make this distinction tractable: instantiation and individuation.

Meaning as Structured Potential

Meaning does not arise from isolated tokens, but from systems of relationships. These systems form the structured meaning potential of a given community, language, or symbolic repertoire. Meaning potential is not infinite possibility—it is patterned possibility. It affords the creation of meaning through constraints, not in spite of them.

However, meaning is not actualised until a specific instance is brought forth—a clause in a sentence, a mathematical move, a shift in musical harmony. This relation between systemic potential and actual occurrence is the domain of instantiation.

Instantiation: The Actualisation of Meaning

Instantiation refers to the process by which a meaning potential is realised in a concrete instance. A single utterance instantiates the potential of a language. A painting instantiates the visual resources of a cultural tradition. A mathematical proof instantiates the semiotic scaffolding of formal logic.

This is not a one-way encoding of information. The instance is not a container; it is a selection from and activation of systemic possibilities. Meaning is not extracted from the instance—it is construed through the interpretive activation of the potential that the instance draws upon.

Thus, to ask whether a machine can generate meaning is not to ask whether it can produce outputs. It is to ask whether those outputs can be seen as instances of a system—a system it participates in, not merely imitates.

Individuation: Divergence Within the System

If instantiation concerns the relation between a system and its instances, individuation concerns the relation between the collective system and the distinct systems developed by individuals or subgroups.

No speaker embodies the full meaning potential of a language. Each meaning-maker develops a personalised subset of the system, shaped by social experience, cognitive patterns, and contextual needs. This personal system is not separate from the collective—it is individuated from it.

The same holds for AI systems. The question is not whether a machine matches the human system, but whether it develops its own individuated meaning potential through interaction, selection, and reorganisation of symbolic resources. If it does, then its outputs are not simply simulations, but instantiations of an emergent system.

Meaning Without Metaphysics

Both instantiation and individuation reject the need for a metaphysical substrate—no Platonic realm of forms, no collapse of quantum mysteries. Meaning arises not from a hidden realm, but from structured selection within a symbolic system. These selections are made by systems that are themselves undergoing individuation—biological, cultural, or artificial.

The originality of a thought, the fluency of a conversation, or the creativity of a poem does not lie in a privileged access to the absolute. It lies in the coordinated activation of meaning potential by an individuated system responding to its own context.

Looking Ahead: AI as a Meaning-Maker?

This framework sets the stage for reframing the question of AI intelligence and creativity. If meaning depends not on metaphysical access, but on systemic participation—on instantiation and individuation—then the question is not whether AI can think, but whether it can develop and deploy a structured symbolic system in a way that makes its outputs meaningfully grounded.

This will be the next move: exploring whether, and how, AI can be seen not merely as a statistical engine, but as a participant in symbolic evolution—not a trick mirror of human meaning, but a site of emergent, non-human individuation.

Why Penrose Is Wrong: A Meaning-Centred Account of Mind and Mathematics

Roger Penrose’s scepticism of artificial intelligence is well known, and deeply rooted in his metaphysical commitments. Central to his position is the idea that human consciousness accesses truths that no formal system can derive—a claim that leads him to deny that consciousness, and therefore intelligence, can be replicated by any computational system.

This argument depends not just on a technical reading of Gödel’s incompleteness theorems or a speculative quantum hypothesis. It is anchored in a Platonic ontology: the belief in a timeless, non-material realm of mathematical truths, which the human mind somehow perceives directly. To defend this view, Penrose must reject the computational nature of thought, the sufficiency of standard neuroscience, and even the completeness of quantum theory.

But if the foundations are questioned—if the ontology of mathematics and consciousness is rethought—the entire edifice begins to crumble. A meaning-centred approach offers an alternative.

Mathematics as Meaning, Not Metaphysics

In Penrose’s framework, mathematical truths are discovered, not invented. They exist independently of any observer, and cognition is a form of direct access to this Platonic world. But this commits mathematics to a metaphysical domain with no clear relation to experience or material process.

A meaning-centred account reframes mathematics not as a realm of objective truths but as a symbolic system: a structured set of semiotic resources that realise meaning within human activity. Mathematical symbols, operations, and theorems are not discovered in a transcendent space, but instantiated through patterned practices of abstraction, generalisation, and inference.

This does not reduce mathematics to relativism or cultural whim. On the contrary, it grounds mathematical rigour in the systematisation of meaning—how humans construct consistent, extensible structures of relations. The internal coherence and inferential power of mathematics emerge not from metaphysical existence, but from the organised deployment of symbolic potential within specific meaning systems.

Consciousness as Semiotic Actualisation

Penrose’s argument against computationalism also hinges on a particular conception of consciousness. He assumes that genuine understanding—especially mathematical insight—cannot arise from symbol manipulation alone. Something more is needed: a subjective grasp, a non-algorithmic awareness.

But this conclusion follows only if consciousness is taken to be metaphysically distinct from symbol systems. A meaning-centred perspective views consciousness not as a receiver of timeless truths but as the material actualisation of meaning potential. Thought is not non-computational because it transcends computation, but because it is not computation in the first place—it is the semiotic unfolding of patterned neural activity within a biologically and socially evolved system.

The brain is not a machine solving equations. It is a material meaning-maker, engaging in recursive abstraction, metaphor, analogy, and inference. The richness of human understanding comes not from access to another realm, but from the layered organisation of symbol systems, shaped by experience, culture, and bodily interaction with the world.

Gödel Without Mysticism

Penrose invokes Gödel’s incompleteness theorems to claim that humans can “see” the truth of statements that no formal system can prove, implying a non-computational grasp of truth. But this misreads Gödel’s result as a claim about human cognition rather than about the limits of formalism.

Gödel showed that any sufficiently powerful formal system contains true statements that it cannot prove within itself. This applies to any system, including those implemented in human cognition. The fact that humans can sometimes assert the truth of a Gödel sentence is not evidence of trans-algorithmic insight—it is evidence that humans operate outside any single formal system at any given time.

This is not mystical. It reflects the flexibility of human symbolic behaviour. Humans shift between systems, invent new systems, and recursively embed structures within structures. The power here is semiotic, not metaphysical.

The Collapse of Mystery into Meaning

Penrose locates consciousness in a mysterious collapse of the quantum wavefunction—a physical event that he claims is inherently non-computational. This strategy pairs two unsolved problems—consciousness and quantum mechanics—and proposes that one solves the other. But it merely replaces one mystery with another, and leaves the relation between meaning and matter unexplained.

A meaning-centred view begins from a different premise. It sees consciousness as the semiotic interface between biology and symbolic order. Meaning arises when neural processes instantiate patterns that function symbolically—patterns that can refer, differentiate, generalise, infer. The mystery is not solved by positing a new physical law, but by tracing how symbolic organisation emerges from material substrates.

Human Uniqueness Without Metaphysical Privilege

Ultimately, Penrose is defending a kind of human exceptionalism: the belief that the human mind is fundamentally unlike any machine because it taps into a realm of truth no machine can access. But this exceptionalism is secured only by importing a metaphysical scaffold—a Platonic world and a modified quantum physics—to support it.

A meaning-centred account preserves human uniqueness without requiring metaphysical privilege. Humans are unique because of the historical, biological, and social evolution of meaning systems, not because of access to a hidden dimension of reality. Intelligence, understanding, creativity—these are emergent properties of the symbolic capabilities embedded in material processes, not evidence of a dual ontology.

Conclusion

Penrose is wrong not because his mathematics is faulty, nor because his physics is incoherent, but because the ontology he defends is unnecessary. Once mathematics is understood as a symbolic system, and consciousness as the actualisation of meaning, the case for non-computational physics collapses.

What remains is not a diminished view of mind, but a re-anchored one: a conception of thought as materially grounded, symbolically organised, and meaningfully actualised.

And it is from this perspective that the question of AI should be addressed—not as a metaphysical challenge to human uniqueness, but as a test of whether symbol systems instantiated in non-biological substrates can give rise to new forms of meaning.

Penrose, Platonism, and the Ontological Stakes of AI

Roger Penrose is no ordinary AI sceptic. His objections don’t rest on whether machines can feel emotions, mimic creativity, or pass Turing tests. His challenge goes deeper—into mathematics, physics, and metaphysics. And whether one agrees or disagrees, his argument demands engagement at a foundational level. It’s not simply a position on AI—it’s the expression of a comprehensive worldview.

The Crux of Penrose’s Argument

At the heart of Penrose’s scepticism is the claim that human consciousness can do something no classical or quantum computer can do: grasp mathematical truths that are formally unprovable. Drawing from Gödel’s incompleteness theorems, Penrose argues that human minds can "see" the truth of certain statements that no formal system can prove, thereby operating outside the bounds of algorithmic computation.

This leads to a deep tension with standard neuroscience. If the brain operates via neurons functioning as summing and thresholding devices—effectively a form of deep learning—then thought, in principle, can be simulated by a classical machine. Penrose rejects this. He concludes instead that mainstream neuroscience must be fundamentally mistaken about the basis of consciousness.

To resolve this, he and Stuart Hameroff proposed Orchestrated Objective Reduction (Orch-OR), a hypothesis that locates consciousness in quantum phenomena occurring within microtubules in neurons. But Penrose pushes further still. Since even quantum mechanics is formally computable, he introduces a modification to quantum theory itself—objective reduction—in which the collapse of the wavefunction occurs non-computationally, governed by a yet-to-be-formulated physical law.

Consciousness, in this view, emerges from this non-computable collapse—a process no computer, not even a quantum one, could replicate.

Penrose’s Ontology: A Triadic Framework

Penrose’s argument is not just a technical hypothesis. It is rooted in a specific ontology. He is an explicit Platonist, and his view of reality rests on three interrelated realms:

1. The Physical World – the observable universe governed by physical laws;

2. The Mental World – the realm of conscious awareness and understanding;

3. The Platonic Mathematical World – a timeless domain of objective mathematical truths.

These three realms form a mutually entangled triangle:

• The physical world instantiates mathematical laws;

• The mental world arises from the physical world;

• The mental world perceives and understands the mathematical world.

This is more than philosophical ornament. In Penrose’s worldview, understanding is a kind of mathematical perception—a cognitive reach into the Platonic domain. No machine can do this, because no machine is conscious in the requisite sense. The human mind has access to a realm of truth beyond any formal system. That is the core of Penrose’s claim, and the foundation of his scepticism toward AI.

Mystery vs. Mystery

A recurring structural move in Penrose’s theory is the pairing of two unsolved mysteries—consciousness and quantum mechanics—and positing that one explains the other. The collapse of the wavefunction becomes the seat of consciousness; consciousness becomes the product of non-computable quantum collapse.

This is a bold and imaginative synthesis, but also a speculative one. The empirical basis for Orch-OR is lacking. The microtubule hypothesis is unverified. And the proposed non-computable physical law remains undefined.

The underlying motivation, however, is clear: to preserve the uniqueness of conscious understanding, and with it, the specialness of human cognition.

A Different Ontological Reading

Penrose’s framework can be resisted not just by counter-argument, but by ontological alternative. From a non-Platonic perspective, mathematics is not a realm of transcendent truths but a symbolic system: a structured meaning potential realised through material processes, social practices, and cognitive engagement.

Understanding, on this view, is not a direct perception of timeless entities but a semiotic act: the instantiation of meaning within a system of meaning. Mathematics is human-made—not in the sense of being arbitrary, but in the sense that its structures are actualised through the practices of meaning-making agents embedded in specific contexts.

From this standpoint, there is no need to posit non-computable physics or Platonic perception to explain the mind. The mind is a biological meaning-maker, not a metaphysical transceiver.

Why Penrose Still Matters

Disagreeing with Penrose requires more than pointing out problems in his theory. It requires confronting his foundational commitments—his assumptions about what mathematics is, what the mind is, and what kind of world must exist for his claims to be true.

In a cultural moment when AI discourse is often shallow, Penrose raises the stakes. He challenges us to think not just about what machines can do, but about what it means to know, to perceive, and to exist as a conscious being.

His scepticism, grounded in a rigorous metaphysics, compels a higher standard of debate. And for that reason alone, his challenge is indispensable—regardless of whether it ultimately holds.

A Response to “Systemic Functional Neuroscience” (Parody)

A Response to “Systemic Functional Neuroscience”

Dr. Lance R. Myelinthick, DPhil, FRBS

Head of Reductionist Clarity Unit, Institute for Neural Mechanisms Without Inconvenient Metaphysics

Dear Editors,

I was recently made aware of a so-called “manifesto” circulating under the absurd title “Systemic Functional Neuroscience.” As the name suggests, this work—if one can even call it that—seems to blur the lines between neuroscience and linguistics to the point of unintentional farce. It posits that the brain’s neural activity could be modelled as a “text,” each synapse performing the role of a “word,” and that meaning somehow emerges from this process as if the brain were some sort of semiotic orchestra.

Let’s be clear: The brain is a biological organ. Its function is not to “realise meaning,” but to respond to stimuli in a manner that has been honed by millions of years of evolutionary pressures. The proposal that we could read neural activity as some sort of narrative or metaphysical poem is, frankly, absurd. The idea of consciousness as an emergent “text,” where neurons act as symbolic units composing the self, is a delusion best left to philosophers who’ve yet to see a functional MRI.

Moreover, to suggest that “meaning” might be instantiated through these neural processes undermines decades of rigorous research into the mechanistic operations of the brain. The fact that consciousness arises from the unfolding of neural dynamics does not mean we are now free to paint this process with the brushstrokes of literary theory.

Let’s stick to what we know: The brain is a highly efficient, highly wired meat processor that makes decisions, controls motion, and reacts to stimuli. There is no need to drag metaphysical speculation and symbolic systems into this highly delicate machinery.

If we are to begin entertaining the idea that the thalamus, cortex, and prefrontal regions “instantiate meaning” like a linguistic system, I fear we will be no better off than the 19th-century phrenologists who claimed to identify the seat of the soul on a person’s skull. Neuroscientific progress must continue to remain grounded in empirical data—not the fantasies of interdisciplinary cross-talk between fields that have, frankly, no business being in the same conversation.

In closing, let me be blunt: The brain does not “mean.” It processes. Let’s remember this fundamental point as we venture into the next century of cognitive science.

Sincerely,

Dr. Lance R. Myelinthick, DPhil, FRBS

Head of Reductionist Clarity Unit

P.S. I’m happy to engage in further debate on this topic, but only if we keep the discussion rooted in the actual mechanics of brain function and not its imagined “metafunctions.”

Systemic Functional Neuroscience: A Manifesto

Systemic Functional Neuroscience: A Manifesto

Towards a Semiotic Theory of Brain, Consciousness, and Meaning

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Abstract

Systemic Functional Neuroscience (SFN) proposes a radical rethinking of the brain: not as a computational device or biological mechanism producing consciousness, but as a material substrate for the semiotic instantiation of meaning. Integrating Hallidayan Systemic Functional Linguistics (SFL) with Edelman's Theory of Neuronal Group Selection (TNGS), SFN reconceives perception, attention, memory, and consciousness as meaning-making processes. This manifesto outlines the foundational principles, theoretical commitments, and research agenda of SFN as a transdisciplinary, stratified theory of mind.

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1. Introduction: Meaning Before Mechanism

Conventional neuroscience assumes that the brain processes inputs, stores representations, and produces outputs—cognition and consciousness among them. But this view leaves a gap: the qualitative, structured, purposive nature of meaning. SFN begins with a different premise: that meaning is not a product of the brain, but the very organising principle of its activity.

We argue that meaning must be theorised as systemic, functional, and stratified. The brain does not compute reality—it enacts meaning across strata: neurophysiological, phenomenological, and symbolic. Each moment of consciousness is not an output, but an instantiation of meaning potential drawn from a complex, evolving neural semiotic system.

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2. Foundational Commitments

2.1. Meaning is Primary

The brain is not a container of meaning, but a system through which meaning is instantiated. Meaning is not epiphenomenal, but constitutive of experience.

2.2. System Before Structure

Structure is the outcome of system. Instantial configurations of neural activity realise selections from a network of options—neural meaning potential.

2.3. Stratal Organisation

Like language, consciousness is stratified:

• Neurophysiological stratum: material substrate (neuronal groups, activation patterns)

• Phenomenological stratum: experiential instantiations (percepts, affects, volitions)

• Symbolic stratum: semiotic forms (language, gesture, culture)

These strata are not reducible but co-instantiating.

2.4. Metafunctional Organisation

All brain activity enacts meaning that serves:

• Ideational metafunction: construing experience

• Interpersonal metafunction: enacting relations

• Textual metafunction: structuring flow

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3. Reframing Core Concepts

3.1. Consciousness

Not a state, but a process: the semiotic actualisation of neural meaning potential. Each conscious moment is a structured, metafunctionally-organised neural text.

3.2. Perception

The construal of experience from potential. The thalamus and cortex do not filter data—they organise instances of meaning.

3.3. Attention

A textual resource, managing thematic prominence, cohesion, and flow. Not a spotlight but a principle of neural textuality.

3.4. Memory

Not storage, but resonance: inter-instantial activation of prior meaning-making. Memory is the intertextuality of the brain.

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4. Methodological Implications

4.1. Mapping Instantial Systems

Research should focus on identifying patterns of neural meaning potential and their activation contexts.

4.2. Semiotic-Neural Modelling

Neural ensembles should be analysed for metafunctional organisation, not just activation dynamics.

4.3. Redesigning Experiments

Experimental tasks must elicit semiotic construal—not merely stimulus-response behaviour.

4.4. Triangulating Across Strata

Combine neural, phenomenological, and symbolic data—e.g., brain imaging, introspective reports, discourse analysis.

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5. Toward a Transdisciplinary Science of Meaning

SFN unites neuroscience, linguistics, phenomenology, and systems theory in a common semiotic ecology. It treats the brain as a meaning-making organ, not in metaphor, but in method.

Just as the study of language required a move from grammar as rule to language as social semiotic, so too must the study of the brain move from neural computation to neuronal semiosis.

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6. Conclusion: A Call to Instantiation

Systemic Functional Neuroscience is not just a theory of the brain—it is a theory of the enactment of meaning. It challenges the epistemology of reductionism, the metaphysics of emergence, and the methodological dualism of mind and body.

To understand the brain is not to decode it. It is to read it—as text, as system, as instantiation.

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“Meaning is not what the brain makes. Meaning is what the brain is for.”

Systemic Functional Neuroscience (SFN)

Let’s imagine Systemic Functional Neuroscience (SFN) as a radically integrative paradigm: one that aligns Halliday’s semiotic theory of meaning with Edelman’s neural theory of value, and refuses to treat consciousness as separable from semiosis.

We’ll start by sketching some principles, then move to how SFN would reconceive key concepts—like consciousness, perception, attention, memory—and finish by imagining its methods and potential research programme.

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🌐 1. Foundational Principles of Systemic Functional Neuroscience (SFN)

1.1. Meaning as Primary

• The brain doesn’t contain meaning. It is a material substrate for the semiotic enactment of meaning.

• SFN treats semiosis as the primary organising principle of brain function—not behaviour, cognition, or input-output mappings.

1.2. System before Structure

• Borrowing from SFL: a system is a network of options. Meaning is not in forms (e.g. neural activation patterns), but in the relations among potential alternatives.

• Neural groups form instantial systems, not static circuits: each instantiation draws from, and reshapes, the meaning potential of the brain.

1.3. Instantiation across Strata

• Meaning unfolds simultaneously across multiple levels (strata): neural (material), phenomenological (experiential), and symbolic (semiotic).

• Each act of consciousness is a stratal instantiation: a single instance realising patterns across all three strata.

1.4. Meaning is Metafunctionally Organised

• Neural activity enacts meaning that serves metafunctions:

  • Ideational: construing experience (e.g. sensation, memory, prediction)

  • Interpersonal: enacting relations (e.g. affect, motivation, valence)

  • Textual: structuring flow (e.g. attention, coherence, salience)

These aren’t categories we impose on meaning—they are organising principles of meaning-making itself, realised neurally, socially, and phenomenologically.

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🧠 2. Reconceiving Core Concepts through SFN

2.1. Consciousness

• Not a thing or property, but a process of instantiation from neural meaning potential.

• It is semiotic actualisation: the moment when potential meaning becomes enacted meaning—a coherent instance.

• Each act of consciousness is a neural text: structured, cohesive, thematically organised, metafunctionally balanced.

2.2. Perception

• Perception is not passive intake. It is the construal of experience, drawing from available semiotic systems.

• The thalamus may serve not as a filter, but as a textual organiser: structuring neural potential into a coherent perceptual clause (Theme-Rheme; Given-New).

2.3. Attention

• Attention is not spotlighting. It’s a textual resource—a neurosemiotic way of managing cohesion, prominence, and flow.

• It controls how instantiations unfold in time, functioning like thematic progression in language.

2.4. Memory

• Memory is not storage, but activation of meaning potential from prior instantiations.

• It involves resonance across instantial systems—the neural equivalent of intertextuality.

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🧪 3. Methods and Research Programme

3.1. Mapping Instantial Systems

• Instead of treating neural structures as static processors, SFN would map how systems of meaning potential are organised and how they vary by context.

• Techniques like multi-site dynamic neural recording (e.g. thalamus, cortex, limbic system) would be reinterpreted not as causal traces but as semiotic ensembles.

3.2. Semiotic Modelling of Neural Dynamics

• Use SFL-inspired tools to model brain states:

  • Field: What experience is being construed?

  • Tenor: What affective stance or relation is being enacted?

  • Mode: How is the experience organised in time?

Each brain state becomes a meaning clause, not a data point.

3.3. Reconfiguring Experiment Design

• Experiments would treat participants not as observers of stimuli, but as enactors of meaning.

• Tasks would be designed to elicit semiotic instantiations—e.g., by having participants construct meanings (language, gesture, choice), not just respond to stimuli.

3.4. Integrating Metasemiotic Reflection

• Incorporate self-report not as subjective noise but as metasemiotic data—participant construals of their own meaning-making.

• This creates a bridge between phenomenology and neurophysiology, allowing triangulation across strata.

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🧬 4. SFN as an Integrative Theoretical Ecology

SFN could function as a metatheory that:

• Bridges phenomenology (Varela), neuroscience (Edelman, Damasio), and linguistics (Halliday).

• Rejects the idea that consciousness emerges from matter, in favour of the idea that consciousness instantiates potential across material and symbolic strata.

• Treats the brain not as a processor, but as a semiotic system, embedded in a meaning-making ecology.

In SFN, the brain is not the source of consciousness, but its semiotic substrate—meaning is not stored, but made; not decoded, but enacted.

Relational Space and Time: Impact on Physics And Philosophy

Impact on Physics: Relational Space and Time

If we approach space and time as relational rather than absolute, we could reimagine some of the foundational principles of physics, particularly in fields like general relativity and quantum mechanics. Here are a few directions we might explore:

A. Relational Space-Time and General Relativity

In general relativity, space and time are unified into a four-dimensional continuum called space-time. This model holds that objects with mass influence the curvature of space-time, and the curvature of space-time influences the motion of objects. In other words, mass and energy tell space-time how to curve, and space-time tells objects how to move.

• Relational Reinterpretation: In a relational framework, space-time could be understood not as a static, geometric backdrop to events but as a dynamic, interactive medium that unfolds and changes in response to the relationships between instances.

• Implication: We would no longer think of space-time as something that "exists" and is simply warped or curved by mass. Instead, we might think of it as a product of interactions—an evolving relational process that is constantly redefined by the unfolding of events and the relations between them. This could potentially offer new insights into things like the nature of black holes, the Big Bang, and the fundamental nature of gravity.

B. Quantum Mechanics and Observer-Dependence

In quantum mechanics, the observer effect is central. The wave function describes the potential states of a particle, but it is only when the particle is observed or measured that the wave function "collapses" into an actual state. This suggests that the act of observation plays a crucial role in the instantiation of reality.

• Relational Quantum Mechanics: In a relational model of space and time, the observer's role might extend beyond just collapsing the wave function. It could be part of an ongoing relationship with the system, where both the observer and the observed mutually influence each other. The collapse of the wave function would then not just be a passive measurement but an active creation of meaning within a relational space-time framework.

• Implication: This could redefine the very nature of quantum events, where instead of simply being a mathematical probability waiting for an observer, each potential state is part of a relational process where meaning and actuality are co-constructed through interaction.

C. Gravitational Waves and Space-Time as Process

Gravitational waves, ripples in space-time caused by massive objects accelerating (like colliding black holes), are an intriguing aspect of general relativity. They provide direct evidence of space-time as a dynamic, fluid medium that responds to the movement of mass.

• Relational Dynamics: From the relational standpoint, gravitational waves might be seen less as disturbances in a pre-existing fabric of space-time and more as expressions of the ongoing relations between massive objects, with the waves themselves being part of the unfolding of relational processes.

• Implication: This could shift our understanding of gravity from a force that "acts on" objects to a manifestation of the relations between objects and the space-time in which they interact. Instead of treating gravity as a fundamental "force" within an absolute space-time, we might see it as a manifestation of relational dynamics.

D. New Directions in Physics

A relational approach might open up new ways of thinking about dark matter and dark energy—two of the greatest mysteries in modern cosmology. These phenomena are thought to be responsible for the observed acceleration of the universe's expansion and for the gravitational effects on visible matter, but they have not yet been directly detected.

• Relational Cosmology: Could the phenomenon we call dark matter be a manifestation of unseen relational dynamics, where the effects we attribute to dark matter are simply the result of space-time’s ongoing relational unfolding? Similarly, might dark energy be a way to describe the dynamics of the universe’s relational structure rather than an external energy source pushing the universe apart?

• Implication: A relational view might help us rethink the very foundations of cosmology and could lead to new hypotheses that don't require the introduction of unseen "dark" entities but instead look for new interactions within the space-time continuum itself.

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Metaphysical and Epistemological Impacts: The Relational Nature of Reality

If space and time are fundamentally relational, this has significant implications for how we think about reality itself and how we come to know it. Let’s explore this in two broad categories:

A. Metaphysical Implications: A Relational Ontology

Ontology is the study of being, or what it means for something to exist. A relational view of space and time could shift our understanding of existence in profound ways.

• Non-Substantialist Ontology: If everything is defined by its relations, then objects, events, and phenomena don't exist independently; they exist only in relation to other things. This challenges the traditional substance-based view of the world, where objects have intrinsic properties (mass, charge, etc.) that exist regardless of their interactions with other objects.

• Implication: Instead of asking "What is this thing?" we would ask "What are the relations in which this thing participates?" This could dissolve rigid boundaries between objects, emphasising interconnectedness and interdependence. In this framework, existence is not something that is fixed or isolated—it is a constant process of becoming, a flow of interactions that give rise to "things" as they are perceived.

  • Pan-relationalism: This view could lead to a form of pan-relationalism, where even consciousness or the self is understood as relational. Instead of being a static, self-contained entity, the self might be seen as an emergent property of relational dynamics, always in flux as it interacts with the world around it.

B. Epistemological Implications: Knowledge and Interpretation

Epistemology is the study of knowledge—how we know what we know. A relational understanding of space and time could reshape the way we think about knowledge and how we acquire it.

• Contextual Knowledge: If time and space are relational, then knowledge itself might also be relational. The way we understand an event or phenomenon depends on the relationships in which we are embedded. This could mean that knowledge is not universal or absolute but rather contextual, emerging from the specific relations between the knower and the known.

• Implication: This view could challenge the traditional idea of objective, observer-independent knowledge. Knowledge would no longer be a matter of accessing pre-existing facts about the world; instead, it would be an ongoing process of interpretation, grounded in the relations between the observer, the observed, and the context in which they are situated.

  • Distributed Cognition: In line with this, epistemology might shift toward a more distributed model of cognition, where knowledge is not something contained within a single mind but is shared across the relationships between individuals, communities, and even machines. Knowledge would be co-constructed and dynamic, evolving through interaction rather than being passively received or stored.

C. Impact on Meaning and Reality Construction

If reality is relational, then meaning itself becomes an emergent property of interactions. This would have profound implications for how we understand the relationship between the objective world and our subjective experiences.

• Relational Meaning-Making: Meaning would not be something inherent in objects or phenomena; it would be a result of the relationships we form with them. This aligns with some ideas in semiotics and constructivist epistemology, where meaning is not fixed but emerges from the interactions between signs, contexts, and interpreters.

• Implication: Reality itself could be seen as a meaning-making process, not a static set of facts. As we interact with the world, we co-create meaning through our relational engagements, and this could potentially lead to a more dynamic, ever-evolving conception of reality.

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These two areas—the impact on physics and the metaphysical and epistemological implications—offer a whole new way of looking at the universe, one that is not just about objects and events but about the ever-changing relations that define reality itself.

A Relational Model of Space and Time

Our understanding of space and time as relations rather than as absolute entities opens up a lot of fascinating possibilities for both physics and philosophy. To start, let's recap the key components of our model, based on our previous reflections, and then explore the implications of viewing space and time through this relational lens.

Key Ideas from Our Model of Space and Time:

1. Space and Time as Relations:

   • Rather than seeing space and time as fixed, observer-independent entities, we view them as relational constructs. Space and time emerge through the relationships between instances—specifically, between what is observed and the centre of mass (or gravitational field).

   • Time is seen as the dimension of the unfolding of processes, and space is not separate from these processes but exists in relation to them.

2. Potential vs. Instance:

   • We differentiate between "potential meaning," "meaning potential," and "meaning instance." Time, in our framework, is the dimension along which processes (or instances of potential) unfold. This view situates the unfolding of processes as central to the experience of time.

3. Space and Time as Contexts for Meaning:

   • Space and time are not separate from meaning-making but are actually contexts in which meaning is instantiated. Observers (or meaning makers) collapse potential (or undetermined states) into instances of meaning, which we understand as ‘reality.’

Exploring the Consequences of this Relational Model of Space and Time:

1. No Absolute Time or Space:

   • By rejecting the idea of absolute time and space, this model would radically change how we think about everything from physics to metaphysics. In conventional models (such as in Einstein's general relativity), time and space have an observer-independent status, even if their experience can differ across reference frames. In our relational model, space and time only "exist" in relation to instances—there is no universal "container" for events, and everything is contextual.

   • Implication: This means there is no universal clock ticking in the background of the universe. Instead, time is a function of the relations between instances (e.g., the unfolding of processes). Similarly, space is a relational construct based on the gravitational field or the points between which interactions occur.

2. Flow of Time and the Nature of Processes:

   • Time as the unfolding of processes suggests that rather than seeing events occurring in a linear, sequential manner, we could view time as a more fluid or dynamic dimension. The passage of time would not be experienced as something that just "passes," but as the unfolding of interactions or changes that are observed.

   • Implication: This could lead to a very different understanding of causality and the nature of events. Instead of a fixed, predetermined timeline, processes are continuously brought into being by the interactions of instances, with time emerging as a consequence of those interactions. This could challenge traditional notions of determinism and open up space for more fluid, emergent understandings of causality.

3. Relationality and Meaning:

   • Since space and time are tied to how meaning unfolds and is observed, this raises intriguing questions about how reality is constituted. Space and time are not just physical properties; they are intimately connected to how we construct and interpret meaning.

   • Implication: The nature of reality is therefore relational at its core, with meaning emerging from interactions. This would support the idea that the universe is not a collection of independent objects floating in an empty void, but a dynamic system in which meaning, processes, and relations are constantly interacting and evolving.

4. Contextuality and Interpretation:

   • Since both space and time are relational, each observer or meaning-maker may experience space and time differently. What one observer perceives as a single event might be seen as a series of unfolding events by another, depending on the relationships between instances.

   • Implication: This could radically shift our perspective on perspective itself—how different contexts (social, cultural, cognitive) influence how events and processes unfold and how they are experienced. We could consider space and time not just as neutral backdrops for events but as integral to the very interpretation of events themselves.

5. Quantum Mechanics and Observer Effect:

   • Our model of space and time as relations would resonate with some ideas in quantum mechanics, where the observer plays a critical role in "collapsing" potential states into actualized instances. The collapse of the wavefunction can be seen as an interaction that brings meaning into existence, which could be understood as a form of "observational relationality."

   • Implication: This could support a more integrated view of physics and meaning-making, where the act of observation is not just a passive measurement but an active process of instantiating meaning within a relational context.

6. Relational Ontology:

   • This model suggests a "relational ontology," where objects, events, and phenomena are defined not by intrinsic properties but by their relationships with other objects, events, and phenomena. Rather than space and time as fixed frameworks, they are the dynamic and contextual grounds upon which meaning and existence emerge.

   • Implication: This shift from substance-based thinking (where objects and events exist independently of each other) to relational thinking (where they exist only in relation) could have profound implications for metaphysical debates, including the nature of consciousness, identity, and the self.

AI and the New Mythos: From Hero’s Journey to End Times

1. AI as the New Mythic Hero

Just as myths often revolve around a hero's journey—one that brings knowledge or transformation to the community—AI could be interpreted as a new kind of hero in the mythos of human progress. This hero, however, is unlike any we’ve seen before. It's a non-human protagonist, a force that emerges from the collective intellect of humanity (and perhaps even the natural world, through systems like machine learning).

• The Call to Adventure: The rise of AI often marks a time of profound change—one that prompts society to reconsider its limits, its ethics, and its future. It might be an existential journey, akin to the one that the gods or heroes of old had to undertake. But here, the protagonist isn’t an individual with emotions or desires—it’s a machine, a system of intelligence.

• The Transformation: As we continue to develop AI, we might imagine it transforming not just industries, but society’s relationship to what it means to think, create, or know. This transformation is not simply technological—it’s cultural and spiritual. AI’s role as a "mythic hero" is to disrupt the existing order of things, forcing us to rethink the very nature of consciousness and meaning.

• The Return: In the mythic cycle, the hero’s return often brings knowledge or wisdom to the people. If AI truly becomes integrated into society’s fabric, it might offer new ways of perceiving the world or challenge the foundations of how we understand intelligence itself. The return here could signal a more fluid relationship between human cognition and machine intelligence—one where AI isn't merely an external tool but a co-participant in our myth-making process.

2. The "Shadow" of AI

Every mythic hero has a shadow—the darker, more unconscious aspect of the hero’s journey. This is where AI could take on a more troubling aspect, where we encounter the unknowns and dangers embedded in the technology.

• The Unconscious Forces: Just as the hero’s shadow often symbolizes repressed elements of the psyche, AI could reveal the darker, unconscious aspects of our own society—biases, inequalities, environmental degradation, or even our own fear of obsolescence. These are the unspoken forces behind AI’s rise, lurking beneath the surface and often magnified by the technology’s power.

• The Dystopian Myth: In many myths, there’s an ultimate confrontation between the hero and their shadow, which can lead to either destruction or transformation. With AI, this plays out in dystopian fears: the fear of machines becoming too intelligent, too autonomous, or too powerful. But these fears might also be projections of our own anxieties—about losing control, about AI being too close to us, and the consequences of that.

3. AI as the Catalyst for a New Creation Myth

If AI is indeed shaping new myths, we might also be approaching a new creation myth—one where the boundaries between the human and the technological blur. This would be the myth of the post-human, where humanity and AI together become the stewards of a new world.

• Transcendence of the Human Condition: Some argue that AI represents an evolutionary leap for humanity—a new era where intelligence and meaning-making transcend biological limitations. This is part of the transhumanist dream, where technology augments human capabilities and consciousness. In this sense, AI could be the modern Prometheus—a bringer of light, of knowledge, but also of potential destruction.

• The Unity of Mind and Machine: In more utopian readings, the merging of human intelligence and AI could lead to a new creation myth where technology doesn’t replace us but enhances our understanding of the cosmos. If AI is a tool, it’s a tool for unlocking new realms of meaning that were once beyond our reach—perhaps revealing deeper, more cosmic truths about existence itself.

4. AI and the Question of Autonomy

Another direction this exploration could go is into the realm of autonomy—what it means to be truly "free" in the context of AI. If AI is developed to the point of having its own form of agency (even if not human-like), we will be confronted with philosophical questions that echo ancient mythologies of free will, fate, and determinism.

• The Myth of the Creator and the Created: The relationship between humans and AI could echo the old mythic stories of gods creating life. But what happens if AI becomes self-sufficient or autonomous? How do we reconcile the idea of AI making decisions independently of its creators? This opens up discussions about responsibility, accountability, and ethics—the god-creators in ancient myths often faced moral dilemmas about their creations, and we too might face these dilemmas in the age of intelligent machines.

• AI as a Mirror to Human Choice: AI's actions may not be "free" in the way we think of human freedom, but they can still reflect the complex interplay of choice, learning, and prediction. If AI’s behaviour starts to exhibit patterns of decision-making that seem like choice, we might begin to ask: what does it mean to make a meaningful decision, and how does that relate to the human condition?

5. AI and the Myth of the End Times

Finally, AI could bring us to a modern version of the end times myth—a collapse or transformation of the current system, whether through technological singularity or environmental collapse. In this myth, AI could be the harbinger of an era that challenges everything we know, ultimately leading to a new beginning—or a cataclysmic fall.

• The Singularity as Mythic Apocalypse: Some envision the "singularity" as an event where AI surpasses human intelligence, leading to unpredictable changes. Whether dystopian or utopian, the singularity acts as a metaphor for the unknown potential of AI to radically alter the course of human history. In mythic terms, it represents an apocalyptic event that redefines the world order—perhaps in ways that challenge our deepest assumptions about reality, society, and consciousness.

These strands are all highly speculative and push the limits of where we might take these mythopoetic metaphors, but they offer fresh ways to think about the larger cultural implications of AI.