Modeling Individual Differences in Chord Pleasantness Judgments

A foundational question in empirical music aesthetics concerns how certain note combinations (chords) are perceived to be more pleasant than others. While normative patterns of chord pleasantness judgments are well studied, relatively little is known about how listeners vary in these judgments. We address this question using a set of variance decomposition techniques: structural equation modeling to disentangle preference variance from response noise, and Q-type principal component analysis to identify latent dimensions of participant variation. We apply these techniques to a new dataset where 106 online participants rated a set of 68 chords for pleasantness. Structural equation modeling shows that the chords vary in both preference variance and response noise; while response noise varies relatively unsystematically, preference variance increases with familiarity and decreases with spectral interference (i.e., beating resulting from interactions between neighboring partials). Q-type principal component analysis meanwhile shows that listener preferences vary on two primary dimensions: one corresponding to spectral interference, the other to cardinality (the number of notes in the chord). The former component is associated with musical expertise (more expertise means more disliking of interference), whereas the latter component does not correlate with any measured background variables. These findings confirm the previously established effect of musical expertise on chord preferences but contrast with previous studies’ claims that responses to interference are undifferentiated across participants. Our study provides a potential model for using variance decomposition techniques to study individual differences in other aesthetic domains.