tcai_cycle
Run one or more consciousness cycles simulating SNN signals, emotion, and self-modeling, with optional early halt when the second-order loop reports sustained satisfaction.
Instructions
Run one or more ACM cycles (the_consciousness_ai port): SNN signals → AKOrN binding → GNW ignition → qualia → emotion → reward shaping → emotional memory → self-model → second-order loop. Set stopWhenSatisfied to halt early once the recursive loop reaches a sustained satisfactory (converged, low-curiosity, stable) regime.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| cycles | No | Number of cycles (default 1; upper bound if stopWhenSatisfied) | |
| threat | No | ||
| novelty | No | Surprise/curiosity ∈ [0,1] | |
| narrative | No | Annotation for the memory record | |
| maxSurprise | No | Halt threshold: normalized WM surprise ≤ (default 0.2) | |
| maxCuriosity | No | Halt threshold: intrinsic reward ≤ (default 0.2) | |
| minStability | No | Halt threshold: development stability ≥ (default 0.7) | |
| rewardSignal | No | Task feedback ∈ [−1,1] | |
| controllability | No | ||
| predictionError | No | World-model surprise (raw) | |
| stopWhenSatisfied | No | Halt early when the second-order loop reports sustained satisfaction | |
| minLearningVelocity | No | Halt threshold: learning velocity ≥ (default 0.5) | |
| predictionConfidence | No | ||
| satisfactionPatience | No | Consecutive satisfied cycles required to halt (default 3) |