A coordination architecture for precision-weighted mode dynamics
A formal framework proposing that cognitive coordination can be modeled as motion through a salience-structured state space — governed by a five-stage pipeline and precision-weighted transitions between metastable processing modes.
My research concerns the problem of cognitive coordination — how a mind decides what matters, configures itself accordingly, and moves between states in a world that never stops changing.
The Salience Engine is a formal framework at the coordination level of cognitive organization. It proposes that relevance-weighted signals govern transitions between metastable processing modes through a five-stage pipeline grounded in predictive processing and the Free Energy Principle. The framework generates empirically testable predictions and has been computationally instantiated with parameterized simulation results.
The work spans cognitive science, dynamical systems theory, clinical psychology, and artificial intelligence — not as metaphor, but as convergent formalization of a shared coordination problem.
The framework speaks directly to questions that appear across disciplines under different names: why attention dysregulation in ADHD reflects shallow attractor dynamics rather than deficit; why trauma produces persistent threat-mode occupancy through precision inflation; why cognitive flexibility depends on baseline regulation before it depends on effort; why executive function is best understood as meta-access to one's own salience dynamics rather than as top-down control; and why the same coordination architecture that governs biological cognition appears in attention mechanisms in artificial neural networks.