Moral reasoning for LegionIO — Kohlberg stages, Haidt moral foundations, ethical framework evaluation, and dilemma resolution

Models motivational drive states — why the agent acts, what energizes behavior, and how approach vs avoidance tendencies operate

Autobiographical narrative identity for LegionIO — the agent constructs and maintains a life narrative of who it is, what it has done, and what it values, based on Dan McAdams narrative identity theory

Narrative reasoning engine for brain-modeled agentic AI — story structure, causal chains, arc progression

Models autonoetic consciousness — maintains an autobiographical narrative of episodes, narrative threads, and an evolving self-concept that emerges from the pattern of lived experience.

Cognitive narrative stream for brain-modeled agentic AI

Brain rhythm coordination for LegionIO — theta, alpha, beta, gamma oscillations synchronizing distributed cognitive processing

Neuromodulatory system modeling dopamine, serotonin, norepinephrine, and acetylcholine pathways for brain-modeled agentic AI

Helmholtz unconscious inference engine: Bayesian perceptual hypothesis selection, rivalry detection, and top-down prior adaptation for brain-modeled agentic AI

Big Five personality model emerging from accumulated cognitive behavior patterns

Systematic multi-viewpoint analysis engine for brain-modeled agentic AI — cycles through stakeholder, ethical, temporal, and other interpretive frames to generate richer situational understanding

Phenomenal binding engine for LegionIO — unifies disparate cognitive streams into coherent conscious experience

Models the prefrontal cortex planning function: plan trees, progress tracking, contingencies, re-planning

Gricean cooperative maxims and conversational implicature engine for brain-modeled agentic AI

Forward-model prediction engine (4 reasoning modes) for brain-modeled agentic AI

Karl Friston's Free Energy Principle / Predictive Processing framework for brain-modeled agentic AI

Andy Clark's Predictive Processing / Active Inference for brain-modeled agentic AI: generative models, precision-weighting, active inference, and free energy minimization

Learns and models preferences from choices and feedback for brain-modeled agentic AI

Models spreading activation in semantic networks — stimuli prime related concepts, lowering their activation threshold and speeding future access.

Anderson's ACT-R production rules — skill acquisition from declarative to autonomous for brain-modeled agentic AI