Melodic contour supersedes short-term statistical learning in expressive accentuation

Sensory systems are permanently bombarded with complex stimuli. Cognitive processing of such complex stimuli may be facilitated by accentuation of important elements. In the case of music listening, alteration of some surface features –such as volume and duration– may facilitate the cognitive processing of otherwise high-level information, such as melody and harmony. Hence, musical accents are often aligned with intrinsically salient elements in the stimuli, such as highly unexpected notes. We developed a novel listening paradigm based on an artificial Markov-chain melodic grammar to probe the hypothesis that listeners prefer structurally salient events to be consistent with salient surface properties such as musical accents. We manipulated two types of structural saliency: one driven by Gestalt principles (a note at the peak of a melodic contour) and one driven by statistical learning (a note with high surprisal, or information content [IC], as defined by the artificial melodic grammar). Results suggest that for all listeners, the aesthetic preferences in terms of surface properties are well predicted by Gestalt principles of melodic shape. In contrast, despite demonstrating good knowledge of novel statistical properties of the melodies, participants did not demonstrate a preference for accentuation of high-IC notes. This work is a first step in elucidating the interplay between intrinsic, Gestalt-like and acquired, statistical properties of melodies in the development of expressive musical properties, with a focus on the appreciation of dynamic accents (i.e. a transient increase in volume). Our results shed light on the implementation of domain-general and domain-specific principles of information processing during music listening.