Predictive processes shape individual musical preferences.

Current models suggest that musical pleasure is tied to the intrinsic reward of learning, as it relies on predictive processes that challenge our minds. According to predictive coding, optimal learning, which maximizes epistemic value, depends on balancing predictability and uncertainty, implying that musical pleasure should also reflect this equilibrium. We tested this idea in two independent large samples using a novel decision-making paradigm, where participants indicated preferences for melodies varying in surprise and entropy. Consistent with prior research, we found an inverted U-shaped relationship between predictability and preference. Moreover, our results revealed an interaction between predictability and entropy, with smaller surprises preferred in low-entropy melodies and larger surprises favored in high-entropy music, consistent with predictive coding principles. Computational models incorporating this interaction predicted individuals' genre preferences and pleasure responses to real compositions, highlighting its applicability to real-world music experiences. These findings advance our understanding of the cognitive mechanisms driving music preferences and the role of predictive processes in affective responses.