Biology subjects

Doelling, K.

Publications and source records attributed to Doelling, K..

3 recordsLinked to original sources

bioRxiv2026

Surprise gates two distinct mechanisms to support memorability in music

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.

neuroscience↗

bioRxiv2024

Multi-stream predictions in human auditory cortex during natural music listening

Musical expectations shape how we perceive and process music, yet current computational models are limited to monophonic or simplified stimuli. The study of the neural processes underlying musical expectations in real-world music therefore requires significant advances in our statistical modeling of these stimuli. We present PolyRNN, a recurrent neural network designed to model expectations in naturalistic, polyphonic music. We recorded neurophysiological activity non invasively (MEG) and within the human brain (intracranial EEG) while participants listened to naturally expressive piano recordings. The musical expectations estimated by the model are encoded in evoked P2- and P3-like components in auditory regions. Comparing PolyRNN to a state-of-the-art generative music model, we show that piano roll representations are best suited to represent expectations in polyphonic contexts. Overall, our approach provides a new way to capture the musical expectations emerging from natural music listening, and enables the study of predictive processes in more ecologically valid settings.

neuroscience↗

bioRxiv2024

Tempo-dependent selective enhancement of neural responses at the beat frequency can be explained by both an oscillator and an evoked model

A crucial mechanism for the brain to make sense of the auditory environment is the synchronization of neural responses to external temporal regularities, such as a musical beat. It is debated whether this synchronization and the resulting beat percept reflect phase alignment of endogenous neural oscillations to the external regularity (entrainment), or evoked responses to the rhythmic stimulus (tracking). Here, we use the tempo-dependent properties of beat processing to differentiate between the two accounts. Participants listened to a repeating rhythmic pattern at different speeds. Behaviorally, they consistently tapped at the preferred beat rate (around 2 Hz) across tempi, shifting to higher metrical levels as tempo increased. We found a similar shift in EEG data, where the metrical level at which neural synchronization was strongest depended on tempo. This selective enhancement is consistent with entrainment accounts and could indeed be mimicked by an oscillator model. However, importantly, the results were also captured by a model simulating evoked responses. Together, our findings demonstrate that while neural responses to rhythm are selectively enhanced at the beat rate, this enhancement need not be taken as evidence for entrainment, but can also be explained by successive evoked responses.

neuroscience↗