25 September 2026

Systemic Functional Neuroscience: Relational Research Agenda

Goal: To operationalise SFN as a transdisciplinary, multi-stratal, relational science of meaning, integrating neural, phenomenological, and symbolic strata while accommodating collective construals.

1. Mapping Neural Meaning Potential

Objective: Identify instantial systems and patterns of semiotic potential in the brain.

Approach:

  • Use multi-site, high-density neural recordings (cortex, thalamus, limbic system) not as causal traces, but as snapshots of instantiated potential.

  • Analyse co-activation patterns as relational networks of neural options.

  • Track variability across contexts to model potential spaces rather than fixed circuits.

Relational Note: Neural ensembles are substrates of meaning, not generators; recorded patterns are perspectival instantiations, not outputs.


2. Semiotic-Neural Modelling

Objective: Translate neural data into meaning structures.

Approach:

  • Apply Field–Tenor–Mode (FTM) analysis:

    • Field: What type of experience or phenomenon is being construed?

    • Tenor: What relational stance, affective orientation, or social positioning is enacted?

    • Mode: How is the experience structured temporally and thematically?

  • Model neural ensembles as clauses of meaning, mapping how metafunctions (ideational, interpersonal, textual) are realised in neural dynamics.

Relational Note: This method captures cross-stratal instantiations, linking neural activity to phenomenological and symbolic experience.


3. Reconfiguring Experimental Design

Objective: Shift from stimulus-response paradigms to semiotic instantiation elicitation.

Approach:

  • Design tasks that require active meaning-making, e.g.:

    • Narrative generation (spoken or written)

    • Gesture-based enactments

    • Collaborative decision-making or problem-solving

  • Include socially distributed tasks to observe collective construal.

  • Measure not just behaviour or neural signals, but how participants instantiate semiotic potential.

Relational Note: Participants are enactors of meaning, with each trial being a perspectival cut from their potential networks.


4. Triangulating Across Strata

Objective: Connect neural, phenomenological, and symbolic strata for coherent multi-level analysis.

Approach:

  • Combine:

    • Neural recordings (e.g., EEG, fMRI, multi-electrode arrays)

    • Phenomenological reports (introspective, experience sampling)

    • Symbolic outputs (language, gesture, social interaction)

  • Analyse cross-stratal coherence and inter-stratal influence.

  • Include metasemiotic reflection, i.e., participants’ reports on their own construals, to capture second-order meaning.

Relational Note: Strata co-actualise in instantiation; triangulation allows mapping of multi-stratal semiotic patterns.


5. Modelling Collective Construals

Objective: Capture shared semiotic potential and relational alignment in groups.

Approach:

  • Observe multi-agent tasks, communication, and co-construction of meaning.

  • Model alignment vectors: how individual potentials converge to form collective instantiations.

  • Analyse interpersonal resonance, collective memory, and distributed attention patterns.

Relational Note: Collective construals are higher-order instantiations that modulate individual potentials and create emergent coherence.


6. Analytical Principles

  • Treat neural data as ensembles of semiotic options, not as deterministic codes.

  • Focus on variation and selection across potential spaces, capturing the dynamics of instantiation.

  • Emphasise perspectival cuts: every measurement or observation is itself an instantiation.

  • Map metafunctional balance (ideational, interpersonal, textual) across strata and collective scales.


7. Methodological Ethics

  • Respect participants as co-constructors of meaning, not experimental objects.

  • Incorporate reflexivity: researchers’ interpretations are part of the semiotic ecology.

  • Maintain openness to emergent patterns, avoiding reduction to linear causality.


8. Vision for SFN Research

  • Generate multi-stratal, relational atlases of meaning potential in individuals and collectives.

  • Develop neural–semiotic computational models of instantiation dynamics.

  • Produce experimental paradigms that foreground meaning-making, relationality, and social alignment.

  • Move beyond reductionist neuroscience toward a transdisciplinary, relational understanding of consciousness.


This research agenda directly operationalises the relational SFN manifesto, giving concrete guidance for experiments, data interpretation, and collective analysis.

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