Network representations reveal structured uncertainty in music

Music, as a structured yet perceptually rich experience, can be modeled as a network to uncover how humans encode and process auditory information. While network-based representations of music are increasingly common, the impact of feature selection on structural properties and cognitive alignment remains underexplored. In this study, we evaluated eight network models, each constructed from symbolic representations of piano compositions using distinct combinations of pitch, octave, duration, and interval, designed to be representative of existing approaches in the literature. By comparing these models through topological metrics, entropy analysis, and divergence with respect to inferred cognitive representations, we assessed both their structural and perceptual efficiency. Our findings reveal that simpler, feature-specific models better match human perception, whereas complex, multidimensional representations introduce cognitive inefficiencies. These results support the view that humans rely on modular, parallel cognitive networks--an architecture consistent with theories of predictive processing and free energy minimization. Moreover, we find that musical networks are structurally organized to guide attention toward transitions that are both uncertain and inferable. The resulting structure concentrates uncertainty in a few frequently visited nodes, creating local entropy gradients that alternate between stable and unpredictable regions, thereby enabling the expressive dynamics of tension and release that define the musical experience. These findings show that network structures make the organization of uncertainty in music observable, offering new insight into how patterned flows of expectation shape perception, and open new directions for studying how musical structures evolve across genres, cultures, and historical periods through the lens of network science.