bioRxivNeurosciencePreprint
Surprise gates two distinct mechanisms to support memorability in music
Mathieu Pham Van CangiD, Paul RobertiD, Manuel R. MercieriD, Agnès TrébuchoniD, Fabrice BartoloméiiD, Benjamin MorilloniD, Keith B. DoellingiD, Luc H. ArnaliD
Abstract
Music is a uniquely memorable human creation that, when skillfully composed, can persist in individual memory (as in e.g., earworms) and cultural transmission (e.g. global hits or anthems). While both acoustic and statistical properties are known to influence a songs memorability, the neural mechanisms that facilitate the engramming of certain musical sequences remain unclear. Current theories suggest that memory systems function as predictive internal models, enhancing learning when expectations are violated. Yet expected stimuli, by aligning with and reinforcing prior knowledge, also enhance memorability. How these two opposing processes arise from the brains sensitivity to statistical regularities, especially in naturalistic sequences, is not well understood. Here, we leveraged musics intrinsic balance between expectancy and surprise, and examined the neural correlates of minutes-scale memorability using intracranial EEG recordings from nine patients with epilepsy performing a musical memory task. Quantifying the statistical surprise of each musical excerpt with PolyRNN, a polyphonic model of musical expectations, we uncovered a U-shaped relationship between musical surprise and memory performance: both highly expected and highly surprising melodies led to greater memorability. While neural pattern similarity between song repetitions was enhanced for low-surprise stimuli, high-surprise stimuli enhanced neural pattern separability in medial temporal regions, each mediating memorability in distinguishable ways. These findings reveal complementary neural mechanisms through which statistical structure shapes musical sequence memory, clarifying how the brain encodes complex, ecologically valid stimuli.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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