Addiction as Empirical Learning Linked to Action: Architecture of the Sensory Matrix and the Counter-Signal Theorem

This theoretical preprint proposes an architectural model of addiction as empirical learning linked to action. Starting from the principle that the brain functions as a sensory matrix that encodes information empirically, the article reformulates addiction as an over-trained, pre-coded learning network maintained by repeated sensory acquisition and reactivation density. The model preserves clinical and neurobiological descriptions of addiction, including dependence, loss of control, craving, relapse and reward-circuit dysregulation, while proposing a complementary architectural interpretation. It introduces the notions of vertical topology, output singularity, captured learning plasticity, fragmentable sensory sequence, counter-signal and counter-precoding. The article is theoretical and does not provide medical or clinical recommendations. The proposed protocols are presented as research hypotheses and as a minimal architectural reference framework for future cumulative comparison with clinical definitions, behavioural observations and neurobiological models.