Art as Neuroplastogens

Abstract

Pharmacological neuroplastogens---psychedelics, ketamine, MDMA---open transient windows of enhanced neural plasticity that can catalyze therapeutic change in mood disorders and beyond. Their clinical promise, however, is constrained by safety concerns, regulatory barriers, and unsuitability for vulnerable populations such as adolescents. Here we argue, from first principles within the Kolmogorov Theory (KT) framework, that \textbf{immersive algorithmic art} can function as a \emph{digital neuroplastogen}: a non-pharmacological intervention that enhances neural plasticity through the same computational mechanism---sustained, structured prediction-error signaling---that underlies the action of psychedelics. In the KT agent architecture, the brain's Modeling Engine (ME) continuously generates compressive predictions of sensory input; mismatches at the Comparator propagate prediction errors that drive model updating via synaptic plasticity. Algorithmic art---dynamic, generative visual environments that weave recognizable patterns with surprising disruptions---is engineered to \emph{maximize} these errors while keeping the stimulus within a compressible, emotionally rewarding regime (the ``Goldilocks zone''). The Objective Function (OF) registers the resulting pattern-discovery as positive valence, creating a self-reinforcing loop: engagement $\to$ prediction error $\to$ plasticity $\to$ model updating $\to$ positive valence. We formalize this ``art-as-neuroplastogen'' hypothesis within KT, connect it to the REBUS (Relaxed Beliefs Under Psychedelics) model, review convergent evidence from psychedelic neuroimaging, predictive-coding electrophysiology, and VR-based interventions, and outline a translational pathway---the ENAKD/Tx platform---that combines closed-loop EEG-driven algorithmic art with cognitive behavioral therapy for adolescent depression. The paper provides the theoretical backbone for a new class of computationally optimized, drug-free plasticity enhancers.