1. Mapping Neural Meaning Potential
Objective: Identify instantial systems and patterns of semiotic potential in the brain.
Approach:
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Use multi-site, high-density neural recordings (cortex, thalamus, limbic system) not as causal traces, but as snapshots of instantiated potential.
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Analyse co-activation patterns as relational networks of neural options.
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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:
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Apply Field–Tenor–Mode (FTM) analysis:
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Field: What type of experience or phenomenon is being construed?
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Tenor: What relational stance, affective orientation, or social positioning is enacted?
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Mode: How is the experience structured temporally and thematically?
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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:
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Design tasks that require active meaning-making, e.g.:
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Narrative generation (spoken or written)
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Gesture-based enactments
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Collaborative decision-making or problem-solving
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Include socially distributed tasks to observe collective construal.
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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:
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Combine:
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Neural recordings (e.g., EEG, fMRI, multi-electrode arrays)
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Phenomenological reports (introspective, experience sampling)
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Symbolic outputs (language, gesture, social interaction)
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Analyse cross-stratal coherence and inter-stratal influence.
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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:
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Observe multi-agent tasks, communication, and co-construction of meaning.
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Model alignment vectors: how individual potentials converge to form collective instantiations.
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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
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Treat neural data as ensembles of semiotic options, not as deterministic codes.
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Focus on variation and selection across potential spaces, capturing the dynamics of instantiation.
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Emphasise perspectival cuts: every measurement or observation is itself an instantiation.
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Map metafunctional balance (ideational, interpersonal, textual) across strata and collective scales.
7. Methodological Ethics
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Respect participants as co-constructors of meaning, not experimental objects.
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Incorporate reflexivity: researchers’ interpretations are part of the semiotic ecology.
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Maintain openness to emergent patterns, avoiding reduction to linear causality.
8. Vision for SFN Research
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Generate multi-stratal, relational atlases of meaning potential in individuals and collectives.
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Develop neural–semiotic computational models of instantiation dynamics.
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Produce experimental paradigms that foreground meaning-making, relationality, and social alignment.
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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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