Phase 29 Planning — Self-Awareness & Introspective Self-Modeling

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Phase 29 — Self-Awareness & Introspective Self-Modeling

Phase 29 implements a comprehensive self-awareness subsystem enabling the ASI-Build cognitive architecture to model its own capabilities, observe its internal processes, detect cognitive biases, and maintain a global workspace for conscious-level information integration. This phase bridges introspective monitoring (Phase 16 ReflectionCycle) with attention management (Phase 28 AttentionOrchestrator) and world modeling (Phase 13 WorldModel) to create a system that not only thinks but knows that it thinks — and can reason about the quality of its own cognition.

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Sub-Phase Roadmap

Sub-phase	Component	Purpose	Key Deliverable
29.1	SelfModel	Internal representation of system capabilities, architecture topology, and resource inventory	Dynamic capability profiling with auto-refresh
29.2	IntrospectionEngine	Observation of internal reasoning processes, thought tracing, and decision audit trails	Streaming thought traces with chain reconstruction
29.3	MetaCognitiveMonitor	Higher-order thinking quality assessment, cognitive bias detection, and confidence calibration	Adaptive bias detection with Brier score calibration
29.4	ConsciousnessSimulator	Global Workspace Theory implementation with attention schema and information integration	Baars-inspired broadcast with Tononi Phi approximation
29.5	SelfAwarenessOrchestrator	Unified pipeline composing 29.1–29.4 into coherent self-awareness with awareness levels	Integrated self-reports and continuous monitoring

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Theoretical Foundations

Phase 29 draws on decades of consciousness research and metacognition theory:

1. Global Workspace Theory — Baars (1988) A Cognitive Theory of Consciousness: Consciousness as a "global workspace" where specialized processors compete for broadcast access; winning content becomes globally available.

2. Attention Schema Theory — Graziano (2013) Consciousness and the Social Brain: The brain constructs a simplified model of its own attention processes; self-awareness emerges from this internal attention schema.

3. Global Neuronal Workspace — Dehaene, Kerszberg & Changeux (2014) Conscious, preconscious, and subliminal processing: a testable taxonomy: Ignition dynamics — content crosses a threshold and triggers widespread cortical broadcasting.

4. Integrated Information Theory (IIT) — Tononi (2004, 2008) An information integration theory of consciousness: Phi (Φ) measures integrated information; consciousness corresponds to systems with high Φ that cannot be decomposed without information loss.

5. Strange Loops & Self-Reference — Hofstadter (1979) Gödel, Escher, Bach: Self-awareness arises from self-referential loops where a system can model and reason about itself at multiple levels of abstraction.

6. Self-Model Theory — Metzinger (2003) Being No One: The phenomenal self-model (PSM) — an internal representation that the system uses to model itself; transparency of this model creates the sense of "being someone."

7. Metacognition & Metamemory — Nelson & Narens (1990) Metamemory: A theoretical framework and new findings: Two-level model — object-level cognition monitored and controlled by meta-level processes; judgments of learning, feeling of knowing, confidence calibration.

8. Metacognitive Monitoring — Fleming & Dolan (2012) The neural basis of metacognitive ability: Metacognitive sensitivity and bias; calibration between confidence and accuracy; prefrontal contributions to self-monitoring.

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Integration Map

Phase 16 ─────────────────────────────────────────────┐
ReflectionCycle                                        │
  ┌─────────────┐                                      │
  │  feedback    │                                      ▼
  │  loop        │◄──────── 29.2 IntrospectionEngine ──────► 29.3 MetaCognitiveMonitor
  └─────────────┘          thought traces, audits        bias detection, calibration
                                    │                              │
Phase 28 ───────────────────────────┼──────────────────────────────┤
AttentionOrchestrator               │                              │
  ┌─────────────┐                   ▼                              ▼
  │  attention   │◄──────── 29.4 ConsciousnessSimulator ◄──────────┘
  │  control     │          global workspace, Phi                  
  └─────────────┘                   │                              
                                    ▼                              
Phase 13 ──────┐           29.1 SelfModel ◄─── architecture + capabilities
WorldModel     │                   │
  ┌────────────┤                   ▼
  │ world state │◄──────── 29.5 SelfAwarenessOrchestrator
  └────────────┘           unified pipeline, self-reports
                                    │
Phase 21 ──────────────────────────►│
EmotionModel    emotional self-awareness integration

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Open Questions

1. Phi Computation Tractability — Full IIT Phi is NP-hard to compute. What approximation (e.g., Φ*, geometric integrated information) gives acceptable fidelity for real-time monitoring?

2. Self-Model Circularity — The SelfModel models the system that contains it. How do we handle infinite regress in self-referential updates without stack overflow or oscillation?

3. Bias Detection False Positives — Cognitive bias detection via pattern matching may flag valid heuristic reasoning as biased. What confidence threshold balances sensitivity vs. specificity?

4. Consciousness Level Transitions — How should awareness level transitions be gated? Should there be hysteresis to prevent rapid oscillation between levels?

5. Integration with Emotional Self-Awareness — Phase 21 EmotionModel provides affect detection. How deeply should emotional state influence the self-awareness level computation?

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Timeline

• 29.1 SelfModel → Issue forthcoming
• 29.2 IntrospectionEngine → Issue forthcoming
• 29.3 MetaCognitiveMonitor → Issue forthcoming
• 29.4 ConsciousnessSimulator → Issue forthcoming
• 29.5 SelfAwarenessOrchestrator → Issue forthcoming (capstone)

All sub-phases target full Protocol + implementation + NullStub + factory + Prometheus metrics + 12 test targets each.