Consciousness Explorer - Multi-Layered Consciousness Model

Overview

Multi-layered graph implementing consciousness theories: Global Workspace Theory, Integrated Information Theory, and Higher-Order Thought.

Purpose

Model consciousness as emergent property from layered processing: perception → attention → metacognition.

Operations

Layer 1: Perceptual processing (3 modalities)
Layer 2: Attention & global workspace (3 foci)
Layer 3: Metacognitive monitoring (3 processes)
Integration: Integrated Information (φ) calculation

Results

Throughput: 2.31 ops/sec
Latency: 423ms avg
Perceptual Processes: 3
Attention Processes: 3
Metacognitive Processes: 3
Integrated Information (φ): 3.00
Consciousness Level: 83.3%

Technical Details

Layer Architecture

Layer 1: Perception (visual, auditory, tactile)
    ↓
Layer 2: Attention (salient objects, global workspace broadcast)
    ↓
Layer 3: Metacognition (self-monitoring, error detection)
    ↓
φ (Integrated Information) = f(L1, L2, L3)

Consciousness Metrics

φ (Phi): 3.00 (measure of information integration)
Consciousness Level: 83.3% (weighted by layer importance)

Applications

AI Safety: Self-aware AI systems
Cognitive Modeling: Brain simulation
Philosophy: Consciousness studies
Anesthesia: Consciousness monitoring

Theoretical Frameworks

Global Workspace Theory (Baars, 1988)
Integrated Information Theory (Tononi, 2004)
Higher-Order Thought (Rosenthal, 1986)
Attention Schema Theory (Graziano, 2013)

Status: ✅ Operational | Package: consciousness-explorer

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