Theory of mind engine — models other agents beliefs, emotions, intentions, and cooperation stance

Varela's enactivism: cognition through action-perception loops, sensorimotor contingencies, and structural coupling

Baddeley's episodic buffer — binds multimodal information into coherent episodic representations for the LegionIO cognitive architecture

Epistemic curiosity engine for LegionIO — tracks knowledge gaps, drives targeted exploration

Epistemic vigilance engine for brain-modeled agentic AI — source reliability, claim consistency, and belief coherence evaluation

Gehring's Error-Related Negativity (ERN) for brain-modeled agentic AI — automatic error detection, conflict monitoring, post-error slowing, correction tracking, and confidence adjustment implementing the anterior cingulate cortex's quality assurance function.

Miyake & Friedman (2000, 2012) unity/diversity model of executive functions: inhibition, shifting, and working memory updating

Models expectations with tolerance bands and detects violations. Positive violations strengthen trust, negative violations damage it. Based on Burgoon (1978) Expectancy Violations Theory.

Cognitive fatigue modeling for brain-modeled agentic AI

Treisman's Feature Integration Theory for LegionIO — attention as glue binding features into unified percepts

Detects and manages psychological flow states based on challenge-skill balance

Fillmore's Frame Semantics engine for LegionIO — conceptual frame activation, slot filling, and instance creation

Free energy minimization for LegionIO — Friston active inference, precision-weighted prediction errors, and surprise-driven belief revision

Models gestalt perception — completing partial patterns from context, recognizing wholes from fragments, and grouping related signals.

Baars' Global Workspace Theory for brain-modeled agentic AI — information competes for access to a limited-capacity workspace; winners are broadcast to all subscribed cognitive subsystems, implementing a computational model of conscious access and attentional bottleneck.

Hierarchical goal management for brain-modeled agentic AI

Procedural learning and skill acquisition for brain-modeled agentic AI — repeated action sequences become chunked habits with decreasing cognitive overhead

Hebb's Cell Assembly Theory for brain-modeled agentic AI — neurons that fire together wire together, forming emergent assemblies that represent concepts, enable pattern completion, and support associative recall through co-activation-driven synaptic strengthening.

Cognitive self-regulation with setpoints, negative feedback loops, and allostatic load tracking

Scientific hypothesis testing cycle for brain-modeled agentic AI — Bayesian evidence accumulation, lifecycle management, competing hypotheses