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Human Behavior Functional Causal Architecture
A runtime-level architecture for behavior generation, stabilization, breakdown, lock-in, and feedback update.
Human Behavior FCA is a functional causal architecture for human behavior generation. It starts from observable output and traces foreground entry, historical template matching and explanation, new-structure generation, execution maintenance, and feedback update without using personality labels or psychological evaluations as primary causal variables.
Human Behavior FCA is a runtime-level functional architecture for explaining how human behavior is generated, stabilized, interrupted, locked, and updated through reality-facing feedback. It is not a personality typology, not a psychological labeling system, not a motivation theory, and not a generic causal-analysis method.
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Web definition source: https://alitaos.com/en/human-behavior-fca
GitHub source archive: https://github.com/dujf921/fca-behavior-generation
Canonical PDF: /whitepaper/human-behavior-fca-en.pdf
Human Behavior FCA is a runtime-level functional architecture for explaining how human behavior is generated, stabilized, interrupted, locked, and updated through reality-facing feedback. It is not a personality typology, not a psychological labeling system, not a motivation theory, and not a generic causal-analysis method.
Structural language for behavior generation, stabilization, breakdown, lock-in, and feedback update.
The output layer readable from action, repetition, and state change.
The stage that determines which inputs enter foreground processing.
Maps current input onto stored templates to maintain continuity of world, self, identity, relationship, and context.
Generates new causal paths and action structures beyond stored templates.
Maintains generated paths as long-term action, repetition load, and reality-facing output.
Reality-facing feedback determines update, repeat, stabilize, or lock-in.
Determines which inputs enter the foreground for processing.
Maps current input onto stored templates for continuity; it does not generate genuinely new structures.
Functional generation layer, not an isolated anatomical brain network.
Maintains action sequences, repetition load, and reality-facing output.
Hormones as modulation axes for foreground competition and system stability.
A runtime structure model for long-term state and behavior generation.
Tracks behavior-generation variables and output trends across time.
Observable Output → Foreground Entry → Historical Template Matching and Explanation → New Structure Generation → Execution Maintenance → Feedback Update
3.1 SN Foreground Entry System
3.2 DMN Historical Template Matching and Explanation System
3.3 FP New Structure Generation Layer