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.
No comments:
Post a Comment