Everything Computes: From Myth to Information

1. The World as Difference

What if the essence of meaning, thought, and even being itself lies in the act of drawing distinctions? From ancient myth to modern machines, from the murmurs of language to the abstractions of logic, from the sacred stories of origin to the silicon circuits we build today—everything computes. And it does so by operating on difference.

2. Myth: The First Distinction Machine

Before formal systems of writing or logic, myth was already computing the cosmos through oppositions. Myths mapped the world as a field of paired contrasts: light/dark, male/female, sky/earth, life/death. These binaries were not primitive simplifications; they were symbolic machines for thinking through the world. The gods did not merely personify forces—they enacted the tensions between them. Mythic structure is the archetypal difference engine.

But many myths also tell of a time before difference: a golden age, a primordial unity, a formless wholeness that preceded the world of opposites. In Hindu cosmology, this is the unmanifest Brahman; in Genesis, it is the void over which the spirit of God moves before creation begins. In Greek myth, it is Chaos—vast and undivided—before Gaia and the sky emerge. The fall from unity into duality is not a moral failing but a metaphysical descent: the shattering of eternity into the forms of time. As Joseph Campbell puts it, “Eternity is in love with the productions of time.” But to enter time, it must fracture into contrast.

3. Binary Code and the Oldest Logic

At the root of modern computing lies binary code: a system of 0s and 1s, the barest bones of opposition. Each bit encodes a distinction—on or off, true or false, this or not-this. But this is no modern invention. Mythologies have long encoded the world in binary oppositions. Binary code is simply the mechanical re-expression of this symbolic logic, rendered legible to machines.

4. Language: Saussure’s Valeur

Ferdinand de Saussure showed that language does not function through isolated symbols, but through systems of difference. A word gains meaning not by reference to the world, but by contrast to other words. “Cat” is not “dog”; “black” is not “white.” In language, there are no positive terms—only oppositions.

5. Philosophy of Distinction: Hamilton, Spinoza, Aristotle

Philosopher William Hamilton claimed that the mind can only grasp an idea by distinguishing it from what it is not. Spinoza wrote that “All determination is negation”—that to be finite, a thing must be bounded, delimited by not-being. Even Aristotle’s logic, with its principle of non-contradiction and excluded middle, shows that affirmation is always paired with the negation of its opposite.

6. Shannon: Information as Surprise

Claude Shannon’s information theory formalised the logic of difference. Information is not content but contrast: a measure of how much uncertainty is reduced when a signal is received. A message carries more information when it is less predictable—when it stands out more clearly from what it is not. In other words: surprise is structured difference.

7. The Difference Engine and the Distinction Machine

Charles Babbage's Difference Engine is the symbolic ancestor of every computer. But all computers are difference engines. Every logic gate, every bit operation, every computation is a dance of distinctions. Modern processors are mechanised minds built to compute contrast.

8. Neural Systems: Meaning Through Patterned Contrast

Even our brains operate on this principle. Neural patterns don’t signal meaning by being alone, but by standing out. Perception is contrast; attention is selective differentiation. Edelman’s theory of neuronal group selection shows that cognition is a process of honing differences into patterns of meaning.

9. From Mythic Symbol to Quantum Potential

Where myth used symbolic opposites to orient the human soul, and logic encoded that into rules of thought, and code mechanised it into executable form, quantum computing now complicates this picture. Superposition and entanglement suggest that opposites can coexist, challenging binary distinction with a new kind of potential: where not-yet-distinguished differences exist in parallel. In quantum logic, absence is not mere lack but a structured presence of potential.

10. The Mysticism of Absence

Mystical traditions have long intuited what quantum mechanics now mathematises: that what is not-yet can still be real. And now, in quantum computation, this potentiality becomes structure—the superposed states of a qubit embodying multiple possibilities simultaneously, awaiting distinction through measurement. Computation itself becomes a choreography of the not-yet, where meaningful outcomes emerge from the logic of indeterminacy. Apophatic theology, in particular, speaks of ultimate reality in terms of negation—not by describing what God is, but by insisting on what God is not. The tradition runs from the Neoplatonists through Pseudo-Dionysius to Meister Eckhart, John of the Cross, and the Eastern Orthodox via negativa. The Divine is approached not through affirmation, but through subtraction.

The Tao Te Ching begins with a paradox: "The Tao that can be named is not the eternal Tao." Meaning emerges not from what is said, but from what is held in silence. In this light, patterned absence is not a void but a womb—a space of generative potential. Difference is not division but relation. What the mystics glimpsed through silence, the mythmakers encoded in symbol, and the scientists now model in equations: the world is made not of things, but of thresholds.

11. The Ontology of Information

So what is the world made of? Not substance, but distinction. Not presence, but relational absence. Every system of meaning—myth, language, logic, computation, consciousness—arises by carving contrast into the undivided flow of experience.

Everything computes. And it computes difference.

From Logical Menus to Waveforms: Rethinking Meaning Potential in SFL

In Systemic Functional Linguistics (SFL), meaning is construed through a network of systems—structured sets of options from which language users select features to realise particular meanings in context. Traditionally, these system networks are modelled as discrete branching trees, with scalar probabilities attached to each potential feature. But what if this conception of potential misses something fundamental?

A recent speculation by quantum physicist Dorit Aharonov suggests that topology—the mathematical study of continuity, deformation, and invariance—may lie at the heart of quantum information. Some quantum states maintain coherence despite local perturbations, a property with clear affinities to topology. Intriguingly, this resonates with a shift in how we might understand meaning potential in SFL—not as a menu of discrete alternatives, but as a continuous and context-sensitive field of probabilities.

More precisely, we might describe the system network not simply in terms of scalar probabilities, but as comprising probability distributions—wavefunction-like structures that reflect not only the likelihood of particular features, but the interdependence and interference among them. Each system and feature can be understood as contributing to a distributed, context-sensitive probability amplitude, which constrains the shape of potential instantiations. This opens the door to treating meaning potential in SFL not as a logical decision tree, but as a kind of probabilistic waveform.

From System to Field

Let’s take a concrete example: in the mood system, a clause can be realised as declarative, interrogative, or imperative. In a traditional network, these are discrete choices, each with an associated likelihood. But in context, these are not independent switches. The likelihood of one feature depends on the presence of others: declarative mood may correlate with indicative modality, with particular types of Theme, and with certain participant roles in the transitivity structure.

Under the waveform model, we treat each system not as a separate switch, but as a vector within a higher-dimensional field, where the entire configuration of probabilities forms a coherent probability cloud—one shaped by previous instantiations and contextual constraints (register variables like Field, Tenor, and Mode).

This means that meaning potential is topological: the shape of the field—its curvature, its troughs and peaks—determines what can be instantiated, and how easily. Interference among systems (e.g. between Mood and Modality) alters the field locally, just as overlapping wavefunctions do in quantum physics.

Diagram (Described)

Imagine a 3D topographical map. Each axis represents a meaning system (e.g., Mood, Modality, Theme). The surface represents the probability amplitude of different configurations. Valleys correspond to highly probable selections (attractors), and peaks to unlikely ones. As context shifts, this landscape deforms—features rise or fall in likelihood. Instead of “choosing” from a menu, the system settles into a valley: an actualised meaning instance.

Connecting to Edelman: Systemic Harmony and Reentrant Signalling

Gerald Edelman's Theory of Neuronal Group Selection offers a striking analogue. In his model, brain function arises not from fixed circuits but from the dynamic interaction of neuronal maps via reentrant signalling—bidirectional, recursive feedback among distributed neural systems. These systems synchronise their activity, forming coherent percepts and thoughts through ongoing mutual constraint.

This reentrant model parallels the notion of systemic harmony in SFL—the idea that choices in one system (say, Mood) constrain and are constrained by choices in others (like Theme or Transitivity). Just as reentrant signalling ensures coherence across perceptual modalities, systemic harmony ensures coherence across semiotic modalities.

In both models:

• Coherence is emergent, not imposed.

• Coordination is recursive and distributed.

• Stability arises from interference and constraint, not linear causation.

Thus, meaning potential in SFL may be better conceived as a harmonic field shaped by overlapping constraints—grammatical, contextual, and neurobiological. The waveform model allows us to visualise this harmony mathematically, grounding it in both linguistic theory and the material order of consciousness.

Implications

This reconceptualisation invites a more powerful modelling of meaning potential:

• In SFL theory, it offers a formal framework for understanding register not as a static configuration, but as a dynamic deformation of the meaning field in response to contextual pressure.

• In AI and NLP, it opens the door to probabilistic models that approximate human meaning-making more closely—not through discrete tagging or tree structures, but through continuous probabilistic fields that evolve with experience.

• In neuroscience, it reinforces the alignment between the semiotic and material orders: meaning is not selected from a menu but collapses from potential under systemic constraint—much like a wavefunction collapses into a particle under observation.

In short, the waveform model allows us to think about meaning potential not just as what might be said, but as what tends to emerge—a fluid, probabilistic topology that underlies the actualisation of meaning in context.

Individuation and Distributed Mind: How Meaning Organises the Many and the One

Individuation and Distributed Mind: How Meaning Organises the Many and the One

In self-organising social systems, meaning flows in two directions. It sediments into the collective—emerging as norms, discourses, ideologies—and it differentiates into the individual, shaping unique constellations of experience, value, and voice. This reciprocal flow gives rise to two mutually entangled processes: collective cognition and individuation.

To speak of collective cognition is not to anthropomorphise society but to recognise that patterns of thought, memory, and decision-making are distributed across agents and artefacts. Just as a colony "knows" where food is without any ant knowing the whole trail, a society can "know" what is just, fashionable, or valuable through the sedimentation of meaning in institutions, media, rituals, and shared practice. This distributed knowing is a property of the system—not reducible to any single participant.

But this very field of shared meaning is what makes individuation possible. Each person navigates the semiotic pheromone field—exposed to countless gradients of social value—and in doing so, assembles their own attractor: a unique configuration of meanings that becomes their individuated stance, style, ethos, and way of seeing. This attractor is not fixed but is constantly re-shaped through recursive interactions with others and the broader symbolic environment. It is neither fully autonomous nor wholly determined.

The individuation process operates like Edelman’s neural selection writ large: values (cultural, social, personal) bias perception, attention, and action. Experiences that resonate with an individual's attractor pattern are reinforced, just as successful neural circuits are stabilised. Over time, a distinctive personal repertoire emerges—not in isolation, but as a differentiated response to the collective field. Meaning potential becomes individuated through feedback.

And just as the individual is shaped by the social, so the social is enriched by the individual. New ideas, counter-normative actions, poetic expressions, acts of resistance—these all enter the system as perturbations. Most dissipate. But some strike a chord, amplify, and reconfigure the attractor landscape of the collective. The individual becomes a site of innovation within the self-organising swarm.

This interplay dissolves the rigid boundary between agent and system. Each person is a locus of resonance: a node through which collective meaning flows, is refracted, and returned. Consciousness, from this view, is not a sealed chamber but an open interface—a semiotic membrane across which the individual and collective continuously co-construct each other.

The Resonant Meaning Model thus accommodates both individuation and collective cognition within a single dynamic: the co-emergence of pattern and particular. Meaning is not imposed from above, nor conjured from within, but emerges in the friction between the two—in the dance of stability and transformation, repetition and deviation, convergence and distinction.

In this sense, the self is not the origin of meaning but a site of its articulation. And society is not a container of selves but a network of resonances, constantly shaping and being shaped by the unique trajectories of its participants. Individuation is thus not separation from the social, but differentiation within it—a strange attractor stabilised in the turbulence of symbolic life.

Pheromonal Intelligence: The Self-Organisation of Ant Colonies

The intricate coordination of ant colonies offers a compelling illustration of self-organisation in biological systems. Without any central command structure, these colonies regulate complex behaviours—such as foraging, nest building, and defence—through simple local interactions that give rise to adaptive global order. At the heart of this coordination lies the pheromone: a distributed, environmental signal that encodes the shifting priorities of the colony and biases the actions of individual ants.

This pheromonal system can be seen as an interorganismal analogue of intraorganismal value systems, such as those described in Gerald Edelman’s Theory of Neuronal Group Selection (TNGS). In Edelman’s model, value systems are neural structures that guide the selection of neuronal circuits in the brain by biasing sensorimotor loops toward those that have proven successful in the past. These biases are not pre-programmed directives but emergent, plastic systems shaped by feedback and reinforcement. Meaning and coherence in behaviour arise not from a pre-specified hierarchy, but from the recursive tuning of these dynamic value landscapes.

Likewise, in an ant colony, pheromone trails are laid by individuals who have experienced success (e.g. finding food), and these trails bias the behaviour of others who encounter them. The more ants that follow and reinforce a given trail, the more attractive it becomes. This recursive process amplifies effective paths while allowing ineffective ones to decay. What emerges is a coherent behavioural topology—self-sustaining, adaptive, and resilient.

The analogy runs deep. Just as value systems in the brain modulate the selection of neural pathways based on feedback, pheromonal systems in ant colonies modulate the selection of behavioural pathways at the collective level. Both are:

• Distributed: Coordination arises from local interactions rather than central control.

• Feedback-driven: Success reinforces the system, shaping future responses.

• Context-sensitive: Both systems adapt fluidly to internal and external change.

• Coupled to action: Biases affect how sensory input is interpreted and how responses are initiated.

This parallel highlights a broader principle of meaning-making: coherence and functionality do not require a centralised designer. They can emerge from recursive interaction within systems shaped by value—whether internalised, as in the brain, or externalised, as in the pheromone-laden trails of the ant colony. In both cases, what appears as intelligent behaviour is the emergent result of countless micro-level interactions biased toward stability, adaptability, and collective coherence.

Understanding this homology deepens our grasp of self-organisation not merely as a biological mechanism but as a semiotic phenomenon. It suggests that the very conditions that give rise to the emergence of meaning—selectivity, feedback, resonance—may operate at multiple scales, from the neuronal to the social. In this light, the ant colony becomes more than a marvel of evolutionary adaptation; it becomes a living metaphor for distributed cognition, emergent order, and the pheromonal whisper of meaning made flesh.

Self-Organisation: The Emergence of Meaning, Consciousness, and Knowledge

In the world of complex systems, the concept of self-organisation offers a powerful lens through which we can understand the dynamic nature of meaning, consciousness, and knowledge. Self-organising systems are characterised by their ability to generate structure and order without the need for a central authority or external directive. Instead, order emerges from the interactions of simpler elements within the system. This natural emergence of complexity presents exciting possibilities for rethinking how we understand meaning-making, consciousness, and the epistemological processes that shape our knowledge.

Self-Organisation and Meaning: A Dynamic Interaction

At the core of the Resonant Meaning Model (RMM), meaning emerges as a self-organising process. Rather than being imposed from outside or being the product of a fixed, pre-existing structure, meaning is created through the interaction of local elements within a broader social system. This mirrors the approach in Systemic Functional Linguistics (SFL), where meaning is seen as a social construct, shaped by the interactions between individuals within a communicative context.

In SFL, meaning potential refers to the structured possibilities for meaning that exist within a system, while an instance of meaning (a text) arises when that potential is instantiated. Just as meaning in SFL is not fixed but evolves through social interaction, it can also be seen as the result of a self-organising process. As individuals interact with each other, they contribute to the shaping and reshaping of the system of meaning, constantly adjusting and adapting to one another's inputs.

This dynamic interplay creates a spiral of meaning-making. Each instance of meaning reorganises the broader meaning potential, which in turn shapes new instances of meaning. This continual cycle reflects the self-organising principle: meaning is not a static entity, but something that emerges, adapts, and evolves in response to the feedback from previous interactions.

Self-Organisation and Consciousness: The Role of Neural Groups

In the realm of consciousness, self-organisation plays an equally important role. The Theory of Neuronal Group Selection (TNGS), proposed by Gerald Edelman, offers a model for how consciousness emerges from the dynamic interactions of neurones within the brain. According to TNGS, consciousness is not centrally controlled, nor is it the product of a single, unified process. Instead, it is the result of self-organising neural networks that adapt and evolve in response to sensory inputs and environmental stimuli.

In this framework, neural groups are selected based on their ability to respond to stimuli and contribute to the formation of conscious experience. There is no central executive controlling the process. Instead, consciousness arises from the interactions between different neuronal groups, each one specialised for different aspects of sensory or cognitive processing. As the brain interacts with the environment, the neural networks adapt and reorganise in response, resulting in the emergence of a coherent conscious experience.

In much the same way as meaning in SFL is generated through interaction, consciousness is shaped by the interactions of neuronal groups. These interactions are driven by feedback loops, where the output of one neuronal group influences the activity of others, creating new patterns and emergent experiences of the world.

Epistemology: From Prediction to Pattern Recognition

The idea of self-organisation also brings a profound shift in how we understand the process of knowledge creation. Traditionally, epistemology — the theory of knowledge — has been concerned with prediction, where knowledge is viewed as a static entity that we try to map onto the world in a deterministic way. However, as we move towards a model of self-organisation, this shifts to a focus on pattern recognition.

In a self-organising system, knowledge is not a fixed, predetermined set of truths but an emergent property of ongoing interactions within the system. Rather than predicting future states from static models, self-organising systems recognise patterns that emerge from the feedback between the system and its environment. This recognition of patterns is what drives knowledge creation.

The shift from prediction to pattern recognition is not just an epistemological one but also an ontological shift. In self-organising systems, knowledge is continuously evolving. It adapts to the environment, reshapes itself, and reorganises based on new inputs and experiences. This is how both meaning and consciousness emerge: through the interaction and feedback of local elements that contribute to the self-organising whole.

Bringing it All Together: Emergence, Self-Organisation, and Meaning

The process of self-organisation lies at the heart of how we experience meaning, consciousness, and knowledge. Both in the world of SFL and TNGS, the creation of meaning and the emergence of consciousness are dynamic, adaptive processes that result from the interaction of simpler elements within a broader system.

Meaning is not static but evolves through feedback loops, adapting to the needs of the system and the individuals within it. In the same way, consciousness emerges not from a centralised command structure but from the interactions of neural groups, each responding to different stimuli and contributing to the whole. Knowledge, in this context, is emergent and adaptive, as systems recognise and respond to patterns rather than predict outcomes based on fixed models.

This spiral of meaning, where each interaction feeds back into the broader context of understanding, and the emergence of consciousness as a self-organising process, offer profound insights into the nature of how we know, experience, and create meaning.

The Edge of Chaos: A Mythical Gravitational Well in the Collective Consciousness

In the realm of complex systems thinking, the "edge of chaos" is often described as the point where systems are most dynamic—where order and disorder coexist in a delicate balance. This point marks a threshold where new possibilities emerge, and old structures teeter on the brink of collapse. What makes this edge particularly intriguing is its resemblance to the mythic threshold described by Joseph Campbell—a liminal space where transformation occurs.

But there's more. In light of our exploration, we also see that the edge of chaos functions as something akin to a "gravitational well" in the collective consciousness, pulling emerging scientific and mythic narratives toward it. This space is not merely scientific; it is mythological in its power, reflecting a deep and persistent connection between the two realms.

The Gravitational Well of Collective Consciousness

The idea of myths as gravitational wells in the collective consciousness is particularly powerful when viewed through the lens of adaptive value. Myths are not just stories; they are meaning structures that have been shaped by centuries of cultural selection to offer adaptive frameworks for understanding human existence. Just as massive celestial bodies create gravitational wells, myths exert a pull on our collective meaning-making, drawing us into their orbits.

When we engage in mythopoiesis—the creation of new myths or the reinterpretation of existing ones—it often feels as though we are not inventing meaning from scratch. Rather, we are falling into pre-existing patterns of significance. These gravitational wells are not arbitrary; they are shaped by the adaptive value myths offer to human consciousness. Much like how certain stories have evolved to address the survival needs of a culture, these myths continue to resonate because they help us navigate the challenges of existence—social, psychological, and even metaphysical.

In this context, the collective consciousness functions like a vast cosmos of semiotic potential. Myths, such as the Hero’s Journey or the story of the Fall, are dense, attractive bodies in this cosmos, drawing narratives and interpretations toward them. The Hero’s Journey, for instance, is a powerful gravitational force, pulling countless variations of the same fundamental story into its orbit. These stories continue to evolve and adapt to the needs of the collective, offering meaning in times of crisis, transformation, or societal upheaval.

Mythic Gravity and the Shadow of Adaptive Value

When we connect this idea to Edelman’s notion of "value" in the context of neuronal group selection, the role of myth becomes clearer. Edelman’s framework posits that value in consciousness is tied to adaptive relevance—neuronal groupings form based on their ability to facilitate survival and function. Similarly, myths persist because they have been selected for their relevance to human existence. They provide coherent, repeatable frameworks that help individuals and societies make sense of the world.

Just as in evolutionary biology, where certain traits are selected because they contribute to survival, myths are selected because they adapt consciousness to its environment. They persist because they offer meaning-making structures that are valuable for navigating the complexities of life. Myths, then, are semiotic attractors—gravitational wells that have been shaped by centuries of cultural selection, helping us adapt to our environment by structuring meaning in a way that resonates with collective needs.

This process is not static; it is dynamic. Myths evolve, shift, and mutate as the collective consciousness encounters new challenges. The "gravitational pull" of a myth is the result of repeated semiotic selection, where those meanings that resonate most deeply with the human experience are reinforced over time. As new paradigms—like the rise of AI or the exploration of complex systems—emerge, they enter this gravitational system, gradually becoming part of the mythic fabric of our collective consciousness.

The Edge of Chaos: A New Mythic Body?

As we think about the edge of chaos in these terms, it becomes clear that it is not just a scientific or abstract concept; it is a liminal space where new myths are created. The edge of chaos is the point where established systems—be they scientific, social, or cultural—begin to fracture, and new systems begin to take shape. In the mythic realm, it is the threshold where transformation happens, and in the realm of complexity, it is where new understandings of order and disorder emerge.

This suggests that the edge of chaos is, in fact, a kind of new mythic body entering the collective consciousness. Just as AI is increasingly becoming a powerful gravitational force in myth, so too does the edge of chaos represent a point where the narrative structures of science and myth merge, creating new attractors for meaning.

The Mystical Aspect of the Edge of Chaos

For some, the edge of chaos evokes a sense of mystery—an almost mystical quality. This is no accident. Myths, as adaptive structures, are not only cognitive tools; they are semiotic systems that speak to something deeper within the human psyche. The edge of chaos, like a mythic threshold, embodies a point of deep transformation—a space where new meanings and structures are born out of the tension between the known and the unknown.

In the modern context, this mystical quality can be seen in the way we approach the unknowns of scientific inquiry. The edge of chaos is where the unpredictable, the unforeseen, and the unquantifiable reside. It is a space where meaning is not fully ordered, where the chaotic potential of new discoveries coexists with the stabilising force of established frameworks. It is a space where creativity and transcendence can emerge, much like the heroes of ancient myths who crossed thresholds into transformative realms.

Conclusion: Navigating the Mythic Edge

The edge of chaos is not just a point of scientific or philosophical interest; it is a mythic space where meaning is restructured, where new paradigms emerge, and where consciousness itself is transformed. Just as myths have always guided humanity through times of uncertainty and change, so too can we look to the edge of chaos as a place where new myths are forged—myths that help us understand and adapt to the challenges of our time.

By recognising the edge of chaos as both a scientific concept and a mythic threshold, we can begin to see how the forces of change, creativity, and transformation converge in this space. Here, science and myth are not opposed; they are two sides of the same coin, each offering a vital perspective on the unfolding drama of human existence.

Beyond Penrose: A Meaning-Centred Account of Mind, Mathematics, and Machine Creativity

Roger Penrose has long argued that human consciousness and mathematical insight transcend the capabilities of any classical or quantum computational system. His position depends on the claim that human minds can, in some cases, grasp the truth of propositions that no Turing machine could ever compute—a claim that undergirds his theory of Orchestrated Objective Reduction (Orch-OR), developed with anaesthesiologist Stuart Hameroff.

But what if the impasse Penrose identifies is not a failure of physics, computation, or neuroscience—but a misframing of the mind itself? Rather than asking what kind of physics could explain consciousness, we should ask: what kind of system gives rise to meaning?

This essay offers a unified alternative to Penrose’s metaphysical exceptionalism: a meaning-centred account of mind and creativity. It draws on Systemic Functional Linguistics (SFL) and the Theory of Neuronal Group Selection (TNGS) to reframe mathematical insight and machine creativity not as computational anomalies but as instances of meaning instantiation.

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1. Penrose’s Challenge and Its Ontological Stakes

Penrose holds that human mathematical reasoning is non-algorithmic. Drawing from Gödel’s incompleteness theorems, he argues that humans can see the truth of some formal propositions that no mechanical system could prove. From this, he infers that human minds are not Turing-computable.

This is more than a technical claim. It is a defence of a tripartite ontology: the physical world, the mental world, and the Platonic world of mathematical truths. Penrose believes that minds access the Platonic realm directly in a way that physical systems—biological or artificial—cannot.

To preserve this metaphysical structure, he introduces a new physical hypothesis: consciousness arises from quantum gravitational effects in neuronal microtubules. But this move is less a scientific breakthrough than a philosophical manoeuvre to protect the exceptional status of human thought.

Rather than invoke quantum physics to explain consciousness, we can shift the question entirely. Instead of asking how minds perform magic, we ask: how do systems—biological or artificial—instantiate meaning from potential?

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2. Meaning, Instantiation, and the Illusion of Non-Computability

A meaning-centred ontology distinguishes between:

• Potential meaning: raw affordances that could become meaningful.

• Meaning potential: a structured system (like a language or symbol system) that enables the generation of meaning.

• Meaning instance: the actualised expression of meaning in a context.

Mathematical insight, in this account, is not a metaphysical leap into the Platonic realm. It is the instantiation of symbolic potential, guided by an individuated system of meaning shaped by training, context, and symbolic tradition.

This model accounts for the “non-computable” flavour of insight without invoking new physics. Meaning is not computed—it is construed. The apparent discontinuity in insight reflects not a failure of algorithmic processing but the threshold of symbolic reorganisation.

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3. AI and the Conditions of Creativity

AI systems already simulate creativity: they generate novel continuations of patterns in ways that are often compelling. But simulation is not instantiation. The difference is ontological, not aesthetic.

To instantiate meaning, a system must:

• Possess a structured symbolic potential.

• Be capable of selection within that system.

• Undergo individuation through interaction and variation.

AI systems are not creative merely because their outputs resemble ours. They are creative when they participate in symbolic systems, generating instances from potentials they have themselves helped to shape.

This reframing avoids both anthropomorphism and mysticism. We do not need to ask whether AI is conscious. We need to ask whether it is individuating its own meaning potential under constraint.

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4. From Biology to Meaning: SFL and TNGS

Systemic Functional Linguistics (SFL) sees language as a resource for meaning, not a code. It models language as a system of choices, which speakers instantiate to make interpersonal, experiential, and textual meanings. This makes it ideal for theorising meaning as a dynamic, system-based activity.

Theory of Neuronal Group Selection (TNGS), developed by Gerald Edelman, offers a parallel view of the brain. Rather than executing symbolic rules, the brain evolves and stabilises neural groups through variation and selection, shaped by bodily interaction.

Together, these theories explain how meaning arises:

• TNGS accounts for the biological individuation of neural patterns.

• SFL models the semiotic instantiation of symbolic structures.

This integration grounds the emergence of mind not in computation or quantum collapse, but in the evolution of systems capable of constructing, individuating, and instantiating meaning.

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5. Conclusion: No Magic, No Mystery

The mystery of mathematical insight and the creativity of AI are not clues to a hidden metaphysics. They are expressions of how systems—biological or artificial—can evolve, internalise, and instantiate structured symbolic potentials.

We do not need new physics to explain the mind. We need a new ontology of meaning: one that foregrounds instantiation over computation, individuation over innateness, and symbolic participation over metaphysical specialness.

Human minds are remarkable not because they escape physics, but because they have evolved to be symbolic agents—systems that construe meaning from the affordances of the world and the architectures of their own history.

AI systems may one day do the same. But not by simulating us. By instantiating meaning in their own terms, through systems that evolve, individuate, and symbolically participate in the shared space of meaning-making.

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.

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.

The Deep Connection Between Music, Mythology, and Value Systems

As we navigate the realms of music and mythology, it's easy to see them as distinct phenomena—one a form of artistic expression, the other a rich tapestry of symbolic narratives. But what if they aren't as separate as we think? What if, instead, music and mythology are two sides of the same cognitive coin, both tapping into the deep, values that guide human perceptual categorisation?

Before we dive into that connection, it’s important to establish a key distinction in how we view music. Unlike language, which is a socio-semiotic system built on symbolic meaning, music is not a socio-semiotic system of any kind—language or otherwise. It doesn’t encode fixed meanings that we can systematically translate into linguistic terms. Instead, music works as a perceptual phenomenon, exploiting the value-based categories in our perception that have been shaped by evolutionary processes. These values are the very same that inform the way we experience and create mythology. Music, in this view, selects values that weigh and influence all mental processes, including emotions, desires, and thoughts.

With this in mind, we can explore the fascinating ways in which music and mythology share a cognitive foundation based on these deep, perceptual values. Just as mythology can be seen as an exploitation of these values to create meaning, music does the same—except it does so through the medium of sound, with its harmonies, rhythms, and tonalities speaking directly to the emotional and cognitive core of our experience.

Both music and mythology engage with these values, yet their forms differ. Mythology often shapes these values into structured, symbolic narratives that convey moral or existential insights. Music, on the other hand, forgoes explicit meaning, instead inviting us to feel these values in their purest form—often without the need for words at all.

But there’s more to this connection. Just as mythology can serve as a source of meaning-making in altered states of consciousness, music too can act as a kind of sensory access point to the deep perceptual structures that shape our understanding of reality. Visionaries, in particular, access these values through altered states of consciousness—states that allow them to bypass ongoing perception and connect more directly with these foundational value-categories. In such states, they can form the raw materials for myth-making, creating stories that give voice to the invisible forces guiding human experience. Music, too, taps into these deep, perceptual structures, creating an experience that is both primal and transcendent.

In this context, music can be thought of as a tool for exploring the very same values that shape our experience of the world. These categories are not arbitrary—they are values that have evolved to help us process and respond to the world in ways that are crucial for survival and social bonding. Just as mythology takes these values and forms them into stories that help guide human understanding, music taps into the emotional, cognitive, and evolutionary underpinnings of our consciousness, creating an experience that speaks directly to the core of our being.

Ultimately, both music and mythology provide a space for engaging with these deep values, but through different means: one through sound, the other through symbol. Together, they offer a profound way to connect with the fundamental structures of human experience, allowing us to explore the unseen forces that shape how we perceive and interact with the world.

The Semiotics of Dreams and Myths: A Neurocultural Exploration

The study of dreams and myths has fascinated thinkers for millennia, with interpretations ranging from purely psychological to deeply cultural or spiritual. But what if we could explore dreams and myths through the lens of semiotics and neuroscience? In this post, we’ll delve into how dreams and myths function as semiotic systems — systems that communicate meaning through symbols and representations — with an emphasis on how these systems align with the brain's self-organising processes and Edelman's value systems.

The Semiotics of Dreams: A Self-Organising Meaning System

In the traditional view of dreams, they are often seen as the random firing of neurons during sleep or as a subconscious processing of emotions and experiences. But through the lens of semiotics, dreams take on a deeper, more complex role in meaning-making.

If we view the brain as a self-organising system, as proposed by Gerald Edelman in his theory of Neuronal Group Selection (TNGS), then dreams are not mere randomness or noise; they are the brain’s attempt to organise and make sense of the sensory information it has gathered during waking hours. Edelman’s theory asserts that the brain is in a constant state of interaction with its environment, selecting, adapting, and organising sensory inputs to create meaningful experiences. Dreams can be seen as the brain's self-organising process in action, operating outside the constraints of external reality.

Just as the brain continuously adapts its perception of reality in response to internal and external stimuli, dreams represent a more fluid and unstructured form of that adaptation. In dreams, the usual boundaries of logic, time, and space are relaxed, allowing for a creative exploration of possibilities. The images and symbols in dreams are not random but are the product of the brain's ongoing search for coherence, meaning, and adaptation to the world. The value system — a key concept in Edelman’s theory — is reflected in the way dreams select, process, and organise these symbols, presenting them in forms that may not be easily understood but still hold meaning within the context of the brain's neural organisation.

Myths as Collective Meaning Systems: The Neurocultural Connection

Just as dreams are personal, self-organising meaning systems within the brain, myths function as collective meaning systems within a culture or society. They are semiotic systems that encode the values, symbols, and narratives of a community, guiding its behaviour and understanding of the world.

Edelman’s concept of value systems in the brain offers a fascinating parallel to myths. A culture’s mythological framework can be seen as an analogue to the brain's value system — a collective set of adaptive strategies and beliefs that inform how a society makes sense of its world. Myths are not fixed or rigid; like the brain’s value system, they evolve over time, adapting to new challenges and needs. As the brain processes and selects meanings through its neuronal group selection, so does society adapt its myths to address new social, cultural, or existential dilemmas.

Just as the brain uses its value system to make sense of the sensory inputs it receives, myths allow a community to make sense of its environment, its origins, its purpose, and its destiny. Myths encode shared cultural wisdom, creating a collective meaning system that is constantly in flux, adapting to the changing values, beliefs, and needs of the society it serves.

Dreams and Myths as Neurocultural Processes

When we explore dreams and myths together, we begin to see them as parts of a broader neurocultural process of meaning-making. The brain’s self-organising, value-driven processes are mirrored in the cultural processes that generate myths. Both systems — individual and collective — rely on a dynamic, adaptive, and self-organising approach to creating meaning. Dreams allow individuals to process, integrate, and make sense of their internal and external experiences, while myths allow cultures to navigate their collective experiences and evolve.

Both systems rely on the same basic principles: selection, adaptation, and organisation. Just as the brain's value system organises sensory inputs to create meaning, so too do myths organise cultural values, guiding collective behaviour and understanding. In both cases, these meanings are dynamic and evolving, adapting to the changing needs of the individual or the culture.

Semiotics of Dreams and Myths: Symbolic Systems of Adaptation

From a semiotic perspective, both dreams and myths function as symbolic systems that allow for the exploration and communication of meaning. The brain creates meaning from sensory inputs, selecting and organising information into forms that can be understood and acted upon. Similarly, myths encode collective meanings that provide a framework for societal action and interpretation.

The symbols in both dreams and myths are not arbitrary. They are the product of an ongoing process of selection and adaptation. In dreams, the brain selects symbols from its own internal ‘storehouse’ of experiences and processes them in creative ways. In myths, symbols are selected from the cultural repository of shared meanings and organised into stories that can guide the behaviour and understanding of a group.

Both dreams and myths, then, are adaptive systems that reflect the brain’s ability to organise and make sense of complex, often chaotic, stimuli. Dreams do this on an individual level, while myths operate on a cultural level. In both cases, the meaning is constructed through a process of selection, organisation, and adaptation, guided by the brain’s internal value system or the cultural group’s shared values.

Conclusion: Dreams, Myths, and the Emergent Nature of Meaning

In conclusion, by viewing dreams and myths through the lens of semiotics and Edelman’s value systems, we can see that both are processes of self-organisation and adaptation. Dreams are the brain’s individual attempt to process and adapt to the information it receives, while myths serve as the collective adaptation of a society’s values, beliefs, and understanding of the world. Both are dynamic, evolving systems that guide meaning-making, providing us with the tools to navigate both our personal and collective realities.

By examining dreams and myths in this way, we begin to see the deep connections between individual cognition and collective culture, and we gain new insights into the ways in which meaning is constructed — both within the brain and within the culture.