Towards an Experimental Protocol for Measuring Local Cognitive Resonance (LCR) in Human-AI-Biological Systems

Towards an Experimental Protocol for Measuring Local Cognitive Resonance (LCR) in Human-AI-Biological Systems

Author: Taotuner DOI: https://doi.org/10.5281/zenodo.19098692 October 2025 (revised conceptual update March 2026)

Abstract

This paper presents an experimental framework that operationalizes intelligence as a measurable resonance field emerging from interactions between humans, AIs, and biological systems. I introduce the Local Cognitive Resonance (LCR) metric, based on semantic (Vs), temporal (Vt), and physiological (Vf) vectors, integrating human intentionality and AI coherence in iterative cycles. I describe the Symbiotic Design Laboratory (SDL) and Living Feedback Garden (LFG) as proposed experimental prototypes, with applications in artistic co-creation, adaptive education, urban planning, and bioart. I discuss ethical protocols, falsifiable hypotheses, and scalability considerations for distributed cognition research.

Lay Summary: This work proposes a way to measure how well humans, AIs, and plants synchronize during collaborative tasks. The LCR metric tracks alignment in meaning, timing, and physiological response, tested in controlled laboratory settings. It is a step toward understanding distributed cognition as an empirical phenomenon.

Keywords: Distributed Cognition; Human-AI Symbiosis; Cognitive Resonance; Experimental Protocol; Dynamic Generative Models; Constitutive Gap.

Acknowledgments

This work benefited from iterative conceptual refinement assisted by large language models, used as tools in a human-led co-creation process.¹

1. Introduction

Traditional intelligence is understood as a property of isolated agents. This work proposes an operational shift: intelligence can be measured as a dynamic resonance field emerging from interactions between humans (H), AIs (A), and biological systems (B). Current AI evaluation relies on static benchmarks, ignoring continuous context and human intentionality. I propose iterative cycles of symbiotic interaction, in which humans and AIs co-create output while physiological and semantic alignment is tracked in real time.

Central Hypothesis: A human-AI-biological symbiotic system can be operationalized and measured via LCR, enabling experimental validation of cognitive attunement.

2. Cognitive Tectonics: Formal Framework

2.1 Agent Vectors

Table 1: Agent roles and measurable vectors

Agent

Role

Semantic Vector (Vₛ)

Temporal Vector (Vₜ)

Physiological Vector (V_f)

Human (H)

Intention, context, feedback

Vˢᴴ

Vᵗᴴ

Vᶠᴴ

AI (A)

Coherence, output generation

Vˢᴬ

Vᵗᴬ

Vᶠᴬ

Biological (B)

Bioelectric signal source

—²

Vᵗᴮ²

Vᶠᴮ²

¹ Methodological Note: Large language models (Grok, ChatGPT, Gemini, Claude, and Deepseek) assisted in conceptual refinement as cognitive tools, not as co-authors.

² Biological agents contribute only temporal (signal rhythm) and physiological (amplitude/frequency) vectors. Semantic vectors require linguistic capacity and are not attributed to plants.

2.2 Local Cognitive Resonance (LCR) Definition

For dyadic (human-AI) systems:

LCR = w_s · S(V^H_s, V^A_s) + w_t · T(V^H_t, V^A_t) + w_f · F(V^H_f, V^A_f)

where:

S(V^H_s, V^A_s) = cosine similarity (semantic alignment)

T(V^H_t, V^A_t) = temporal correlation

F(V^H_f, V^A_f) = physiological correlation

Weights (w_s, w_t, w_f) are normalized after each iteration to prevent unbounded growth:

w_i(t+1) = [w_i(t) + η · M_i(t)] / Σⱼ(w_j(t) + η · M_j(t))

For triadic systems including biological agents:

LCR_v = w_s · S(V^H_s, V^A_s) + w_t · T(V^H_t, V^A_t, V^B_t) + w_f · F(V^H_f, V^A_f, V^B_f)

Semantic alignment remains dyadic (human-AI only).³

2.3 Connection to Informational-Processual Monism

The constitutive gap (Φ_falta), as developed in my previous work "Informational-Processual Monism" (Zenodo, DOI: 10.5281/zenodo.18970336), describes the internal incompleteness that keeps cognitive systems open and dynamic. LCR operationalizes this gap externally: resonance between agents amplifies their mutual incompleteness, driving further recursion. Systems near thermodynamic equilibrium (low Φ_falta) should exhibit low LCR regardless of input, a testable prediction.

2.4 Diagnostic Matrix and Testable Hypotheses

With S and F as primary axes and Vₜ as sustainability modulator, I derive eight falsifiable hypotheses:

High F

Low F

High S

Deep flow → H₁: sustained high Vₜ predicts peak creativity scores

Cold agreement → H₃: sustained high Vₜ predicts abstract reasoning without emotional engagement

Insight flash → H₂: low Vₜ predicts brief "eureka" events

Fleeting consensus → H₄: low Vₜ predicts non-persistent agreements

Low S

Arousal without meaning → H₅: sustained high Vₜ predicts conflict or stress

Boredom → H₇: sustained high Vₜ predicts task abandonment

Startle response → H₆: low Vₜ predicts transient confusion

Attention drift → H₈: low Vₜ predicts momentary lapses

2.5 Distributed Collective Resonance (DCR)

For networked SDL/LFG nodes:

DCR(t) = (1/N) · Σ LCR_i(t)

Weighted aggregation may reflect node centrality in future studies.

3. Proposed Experimental Methodology

3.1 Symbiotic Design Laboratory (SDL)

Objective: Test LCR evolution in controlled human-AI co-creation.

Equipment:

  • EEG: Emotiv Insight
  • HRV: Polar H10
  • Eye tracking: Tobii Pro
  • AI: Multimodal models (LLM + diffusion)

Interaction Protocol (N iterations):

  1. Human inputs intention I_H(t)
  2. AI generates output O_A(t)
  3. LCR computed from vectors
  4. AI adjusts parameters via RL (weights normalized)
  5. Human provides feedback; cycle repeats

Control conditions: Non-adaptive AI, random AI, pre-scripted AI.

3.2 Living Feedback Garden (LFG)

Objective: Extend LCR to triadic human-AI-plant systems.

Equipment:

  • PlantSpikerBox (capturing Vᵗᴮ, Vᶠᴮ)
  • AI-driven LED/sound synthesis

Protocol:

  1. Human intention + plant signal capture
  2. AI generates light/sound stimulus
  3. Bioelectric response measured; LCR_v computed
  4. AI adjusts via RL
  5. Cycle repeats


Biosafety: Stimuli remain within non-stressful parameters validated by plant physiology literature.

It should be noted that the interpretation of plant bioelectric signals as correlates of environmental or human states remains a contested area of research (Gloor, 2025; see also discussions in plant electrophysiology literature). The LFG protocol treats plant signals as physiological data streams without assuming bidirectional communication or semantic content.

Negentropic Coherence (NC):

NC = 1 - [ H_t(V^H_s, V^A_s, V^B_signals) / H_max ]

where H_t measures entropy of joint states. NC complements LCR by quantifying structure versus chaos in the interaction.

4. Proposed Pilot Applications

  • Artistic co-creation: LCR guides real-time style adaptation based on EEG/HRV.
  • Adaptive education: LCR adjusts lesson pacing to student engagement.
  • Urban planning: LCR optimizes design iterations via semantic alignment.
  • Bioart: LFG installations test LCR_v and NC in public engagement contexts.

5. Statistical Validation and Falsifiability

Proposed methods:

  • Pearson/Kendall correlation for T and F components
  • Bootstrap/Monte Carlo for LCR robustness
  • ANOVA across pilot groups

Null hypotheses:

  • H₀₁: LCR invariant between aligned vs. misaligned cycles
  • H₀₂: No correlation between LCR/NC and subjective flow ratings
  • H₀₃: LCR trajectories identical across experimental vs. control conditions

Falsification threshold: Failure to reject H₀₁-H₀₃ across multiple pilots invalidates LCR as resonance metric.

6. Ethics Protocol

  • Coemergence: No agent is instrument; all participants (including plants) have welfare consideration.
  • Privacy: Anonymized physiological data; participant data ownership.
  • Equity: Diverse recruitment for pilot studies.
  • Transparency: AI decisions logged and explainable.
  • Biosafety: Plant stimuli within published safe thresholds.

7. Scalability Considerations

  • DCR networks: Multi-site synchronization for collective resonance studies.
  • Cross-cultural adaptation: LCR metrics tested across diverse populations.
  • Clinical potential: LCR as diagnostic for social attunement disorders (autism, social anxiety).
  • Multi-agent systems: Future experiments with multiple humans, AIs, and plants.

8. Current Status and Limitations

This document presents a theoretical framework and experimental protocol that has not yet been empirically validated. No pilot data are available at this stage. The LCR metric, diagnostic hypotheses, and proposed laboratory setups are conceptual tools intended to guide future empirical research.

Key limitations to be addressed in subsequent work include:

  • Validation of plant bioelectric signal interpretation under controlled conditions
  • Calibration of physiological sensors for real-time resonance tracking
  • Development of reinforcement learning architectures for weight adaptation
  • Ethical protocols for multi-agent systems including non-human participants

The framework is offered as an open invitation for collaboration. Researchers interested in pilot implementations are encouraged to contact the author.


9. References

  1. Varela, F., & Maturana, H. (1980). Autopoiesis and Cognition. Springer.
  2. Bateson, G. (1972). Steps to an Ecology of Mind. U Chicago Press.
  3. Clark, A. (2008). Supersizing the Mind. Oxford UP.
  4. Tononi, G. (2004). "Information Integration Theory." BMC Neuroscience.
  5. Friston, K. (2010). "Free-Energy Principle." Nature Reviews Neuroscience.
  6. Deacon, T. (2011). Incomplete Nature. Norton.
  7. Hayles, N.K. (1999). How We Became Posthuman. U Chicago Press.
  8. Gloor, P.A. (2025). "Plant Bioelectrical Signals for Environmental and Emotional State Classification." Biosensors, 15(11), 744. (Peer-reviewed)

³ Multi-agent correlation: average pairwise correlation for T and F. Semantic alignment: human-AI only.

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