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.
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