I discuss three fundamental questions underpinning the study of consonance: 1) What features cause a particular chord to be perceived as consonant? 2) How did humans evolve the ability to perceive these features? 3) Why did humans evolve to attribute particular aesthetic valences to these features (if they did at all)? The first question has been addressed by several recent articles, including Friedman, Kowalewski, Vuvan, and Neill (2021) , with the common conclusion that consonance in Western listeners is driven by multiple features such as harmonicity, interference between partials, and familiarity. On this basis, it seems relatively straightforward to answer the second question: each of these consonance features seems to be grounded in fundamental aspects of human auditory perception, such as auditory scene analysis and auditory long-term memory. However, the third question is harder to resolve. I describe several potential answers, and argue that the present evidence is insufficient to distinguish between them, despite what has been claimed in the literature. I conclude by discussing what kinds of future studies might be able to shed light on this problem.

Voice leading is a common task in Western music composition whose conventions are consistent with fundamental principles of auditory perception. Here we introduce a computational cognitive model of voice leading, intended both for analyzing voice-leading practices within encoded musical corpora and for generating new voice leadings for unseen chord sequences. This model is feature-based, quantifying the desirability of a given voice leading on the basis of different features derived from Huron’s (2001) perceptual account of voice leading. We use the model to analyze a corpus of 370 chorale harmonizations by J. S. Bach, and demonstrate the model’s application to the voicing of harmonic progressions in different musical genres. The model is implemented in a new R package, “voicer,” which we release alongside this paper.