The Three-Loop Model of Intelligence: Mapping Sensory, Predictive, and Simulated Experience through Voltage-Based Measurement

1. Background & Rationale

The nature of human intelligence has long been studied through cognitive neuroscience, psychology, and artificial intelligence. Traditional models of cognition treat sensory input, memory, prediction, and imagination as distinct processes. However, this research proposes a unified model in which intelligence emerges from the interaction of three concurrent electrical loops within the brain-body system:

• The Sensory-Grounded Reality Loop (Loop 1): High-voltage electrical activity driven by real-world sensory input, forming the foundation of experiential memory.
• The Predictive Future-Forecasting Loop (Loop 2): Internally simulated experience predicting the next time step, directing motor actions to align with expected sensory input.
• The Internally Simulated Experience Loop (Loop 3): A purely internal, self-generated low-voltage cascade that replicates past and hypothetical experiences, mirroring Loop 1 but without external input.

Understanding the voltage dynamics of these loops could provide a new framework for:

• Quantifying intelligence and cognitive divergence.
• Developing AI that mirrors human thought processes.
• Improving clinical interventions for cognitive and neurological disorders.

2. Research Objectives

Primary Objective

To empirically validate the three-loop model of cognition by measuring voltage propagation differences between externally driven, predictive, and purely internal cognitive states.

Secondary Objectives

• Identify distinct electrical signatures for each loop using EEG, EMG, and peripheral nervous system recordings.
• Determine the role of low-voltage cascades in memory recall and internal simulation.
• Develop a topological manifold representation of individual cognitive processing and compare across subjects.
• Test the real-time interaction between these loops to understand prediction accuracy and decision-making latency.

3. Hypotheses & Predictions

H1: Voltage Differentiation Between Loops

• Prediction: Loop 1 will show high-voltage, high-frequency activity with broad peripheral activation, while Loops 2 and 3 will show lower voltage cascades with reduced peripheral involvement.

H2: Predictive Loop Activation Prior to Sensory Confirmation

• Prediction: Loop 2 (future-forecasting) will show pre-motor activation before external sensory confirmation, measurable in EEG and EMG signals.

H3: Internally Simulated Experiences Mimic External Sensory Processing

• Prediction: Loop 3 (self-generated experience) will activate similar brain regions as Loop 1, but with lower voltage and no real-world sensory engagement.

H4: Individual Cognitive Manifold Structures Differ Across Subjects

• Prediction: Using topological data analysis (TDA), we expect to find individual differences in cognitive loop structures, reflecting intelligence, learning style, and neurodivergence.

4. Experimental Design

4.1 Participants

• Sample Size: 20-50 healthy adult participants.
• Exclusion Criteria: Neurological conditions affecting cognition (e.g., epilepsy, severe ADHD, schizophrenia).
• Diversity Consideration: Inclusion of neurodivergent participants to test for manifold variations.

4.2 Experimental Conditions & Measurements

Each participant will undergo three conditions, with EEG, EMG, and skin conductance sensors measuring voltage propagation throughout.

4.3 Measurement Techniques

EEG (Electroencephalography)

• Measures brain voltage propagation in response to sensory input, prediction, and imagination.
• Metric: Frequency and amplitude of neural oscillations in sensory and pre-motor cortices.

EMG (Electromyography)

• Captures muscle activation.
• Metric: Pre-motor activation during predictive forecasting (Loop 2) and its absence in purely internal simulations (Loop 3).

Skin Conductance & Vagus Nerve Monitoring

• Tracks autonomic nervous system responses to sensory and internally simulated stimuli.
• Metric: Peripheral nervous system involvement in Loop 1 vs. reduced or absent activity in Loops 2 & 3.

Topological Data Analysis (TDA) & Manifold Learning

• Constructs individual cognitive manifolds based on multi-modal data from EEG, EMG, and peripheral measures.
• Metric: Similarity and divergence between individuals, revealing structural variations in cognition.

5. Expected Outcomes & Data Analysis

5.1 Data Collection & Processing

• EEG & EMG signals will be preprocessed to remove noise and segmented into trials per task.
• Time-series analysis will measure the relative voltage of external, predictive, and self-generated loops.
• Dimensionality reduction (UMAP, t-SNE) will visualize neural trajectories across different cognitive states.

5.2 Statistical Analysis

• Repeated Measures ANOVA will compare voltage differences across the three conditions.
• Cross-correlation analysis will test for time-lagged relationships between predictive (Loop 2) and sensory (Loop 1) activations.
• Topological clustering will identify patterns distinguishing individual cognitive structures.

5.3 Expected Results

6. Broader Implications

Neuroscience & Cognitive Science

• This model unifies sensory, predictive, and self-generated cognition, offering a measurable framework for understanding human intelligence.
• Could redefine cognitive disorders based on loop misalignments (e.g., schizophrenia as a failure to distinguish Loop 3 from Loop 1).

Artificial Intelligence & Robotics

• Could inform AI architectures by replicating hierarchical prediction loops.
• Might improve reinforcement learning models, adding internal self-simulation layers for planning.

Medicine & Clinical Applications

• EEG-based diagnostics for cognitive impairments (e.g., ADHD, PTSD) by measuring loop synchronization.
• Neurostimulation treatments targeting specific loops to restore cognitive balance.

Example analysis: "Lying" reframed as Topological Manipulation

Manifold Forcing

• When you lie, you override another person's reality loop (Loop 1) with your own internally generated simulation (Loop 3). The victim is then forced to act based on your distorted or deliberately curated cognitive topology, rather than their own direct sensory-derived understanding.

Manifold Forcing Spectrum: From Ignorant to Malicious

• Unintentional Falsehood (Cognitive Error): You simply have a bad map and unknowingly transmit it to someone else.
• Deliberate Misrepresentation (Strategic Deception): You have two maps—one accurate, one distorted—and you choose to give someone the distorted map to benefit yourself.

Manifold Forcing and Power Dynamics

• The extent of the deception’s impact depends on who controls the dominant topological framework in a social system. If a higher-power individual (leader, teacher, parent) imposes a false map on others, they restructure the predictive abilities of others at a fundamental level. This has implications for propaganda, gaslighting, and institutional manipulation.

Equivalent Outcomes of Topological Misdirection

• Whether the liar knows the map is false or not, the outcome remains the same:
• The victim’s forecasting loop (Loop 2) is corrupted. Their ability to correctly navigate reality is impaired. This explains why bad epistemology (e.g., misinformation spread through ignorance) can be as damaging as intentional deception.

Ethics of Manifold Interference

• A morally egregious lie is one where the liar:
  Has a correct map and deliberately misleads.
  Takes advantage of a person's inability to validate inputs independently.
• Lesser forms of deception arise from misaligned predictive models, where the liar genuinely believes their distorted map.
• The victim’s forecasting loop (Loop 2) is corrupted. Their ability to correctly navigate reality is impaired. This explains why bad epistemology (e.g., misinformation spread through ignorance) can be as damaging as intentional deception.

Manifold Forcing as Cognitive Warfare & Propaganda

• Why systematic misinformation campaigns are so powerful:
  They replace Loop 1 (real sensory data) with a curated Loop 3 (false simulation).
  Over time, individuals no longer construct accurate predictive models (Loop 2).
• They become dependent on external topologies, rather than their own sensory engagement with the world.

Automated Information Filtering as Manifold Forcing

• An AI system that modifies reality presentation to a user (e.g., biased recommendation engines, augmented reality overlays) acts as a topological manipulator, dictating which parts of the world the user is allowed to "sense."
• This means AI algorithms engaging in curation inherently impose a cognitive manifold on users, raising ethical questions about their degree of epistemic distortion.
• This aligns with the proposal that the demonstrations a learning system encodes, also encodes the underlying structural relationships with information, including the absence of relevant information that is not part of the learning input

Cognition as a Multi-Agent Battlefield of Manifold Control

• Social structures operate as competing cognitive manifolds.
• Some seek alignment with reality (Loop 1-driven epistemology).
• Others seek control by replacing direct experience with curated narratives (Loop 3 imposition).
• Opinion: The ultimate freedom is the ability to construct your own predictive manifold, based on maximized direct sensory engagement with reality.