Supercomplexity: bridging the gap between aesthetics and cognition

Abstract

This study presents a cognitive neuroscience framework for understanding what we term "supercomplex experiences," a concept describing experiences that simultaneously engage multiple neural networks and cognitive faculties in ways that resist decomposition into simpler processes. Drawing on recent advances in network neuroscience, we argue that these experiences emerge from the coordinated activity of distributed brain systems, including the salience network, default mode network, and central executive network. These experiences are distinguished by five essential characteristics: (1) simultaneous engagement of multiple neural networks, (2) specialized neural architectures developed through training, (3) specialized conceptual frameworks and vocabularies, (4) emergent properties from dynamic interactions, and (5) coherent gestalt properties. Through examination of expert performance in domains such as wine tasting, musical performance, visual art, perfumery, and several others we reveal how these experiences are characterized by sophisticated integration of sensory, emotional, and cognitive processes, implemented through dynamic network interactions and expertise-dependent neural plasticity. Our framework emphasizes three key mechanisms underlying supercomplex experiences: predictive processing architectures that generate and update multi-level predictions, expertise-dependent network reorganization that enables enhanced sensory discrimination and conceptual integration, and dynamic network flexibility that supports adaptive processing of complex stimuli. While acknowledging debates between different theoretical approaches, we show how interoceptive predictions and embodied simulations, implemented through the anterior insula and related networks, provide a foundation for integrating bodily signals with external sensory input. The development of expertise in domains characterized by supercomplex experiences involves significant modifications of neural architecture, from local circuit refinement to large-scale network reorganization. This work extends beyond existing frameworks in cognitive neuroscience by providing a mechanistic account of how the brain processes and generates richly textured, multifaceted experiences that have previously been studied primarily through separate disciplinary lenses. The framework has implications for understanding expertise development, individual differences in complex skill acquisition, and the neural bases of sophisticated cognitive-perceptual capabilities.

Keywords: expertise; network neuroscience; neural plasticity; predictive processing; supercomplexity.