Embodied Neural Rendering in Self-Aware Networks: A Falsifiable Model of Vision, Sensorimotor Feedback, Body Maps, and the Gamma Consideration Sandwich

This theory-and-methods companion paper develops the embodied-vision case of Self-Aware Networks. It defines a closed visual-motor-reafferent loop, a branch-preserving dendritic receive-transform-re-express model, a Gaussian radial-basis dendritic field, and the target-specific Gamma Consideration Sandwich hypothesis. It specifies body-map transfer, closed-loop virtual-hand delay, laminar model discrimination, phase perturbation and rescue, dendritic-geometry, harmonic-control, and social cue-conflict active-sampling experiments. Conditional surprise and Bayesian information gain are distinguished from clarity, semantic relevance, and causal interaction. The architecture is translated into a bounded Spatial Abstraction Learner proposal. No neural or SAL empirical result is reported.