Biology subjects

Ahluwalia, V.

Publications and source records attributed to Ahluwalia, V..

2 recordsLinked to original sources

bioRxiv2026

Cross-Modal Scaffolding: Music Enhances Hippocampal Binding and Separation for Visual Sequential Memory

The human brain continuously segments experience into meaningful episodes while also encoding temporal relationships between events, yet the mechanisms that optimize this dual challenge remain poorly understood. Here we tested a theoretical framework in which structured temporal context from one modality (music) can organize such memory computations in another (visual) through coordinated modulation of the hippocampus. Using fMRI and a sequence learning paradigm, we show that musical accompaniment enhanced both boundary detection and sequential organization of visual event memory. Mechanistically, musical context accelerated development of neural responses to boundaries in hippocampus and prefrontal cortex while simultaneously optimizing hippocampal representational patterns for learning: strengthening pattern similarity for within-sequence items while reducing computational demands for discriminating representations of different sequences. Critically, musical context created conditions where contextual similarity became a stronger predictor of memory success than before, transforming similarity from an interference signal into a beneficial learning mechanism. Multivariate analysis further revealed that musical scaffolding enhanced hippocampal encoding of the sequential position of visual stimuli, demonstrating cross-modal transfer of temporal structure to visual sequence learning. Finally, we demonstrate functional specificity across hippocampal subfields, revealing how temporal structure cues can coordinate distinct computational processes within the memory circuit. These findings establish a framework for understanding how structured context signals like music can simultaneously optimize multiple aspects of memory organization, and provide mechanistic insight for educational and clinical interventions that have leveraged cross-modal temporal enhancement to improve human cognitive function.

neuroscience↗

bioRxiv2025

Music Scaffolds Visual Statistical Sequence Learning Through Network-Level Reorganization in the Brain

Statistical learning--the ability to extract patterns from noisy continuous experiences--is fundamental to human cognition. Yet, how contextual factors shape this process remains poorly understood. Music is an important example of such contextual factors, because it is ubiquitous in human experience and provides a rich temporally-structured stimulus that can co-occur with other learning processes. Here we demonstrate that pairing music fundamentally enhances visual statistical learning, and this is correlated with systematic reorganization of large-scale brain networks. Using fMRI and a novel probabilistic sequence learning paradigm, we show that familiar melodies significantly improved participants ability to segment continuous visual streams into events and learn sequential relationships. Neuroimaging analyses revealed that the presence of music fundamentally altered the neural network organization that coordinates learning mechanisms: while sequence learning in silence engaged frontal-parietal networks associated with explicit pattern extraction, providing musical temporal structure as a context shifted learning toward MTL-vmPFC circuits recently implicated in schema-guided memory processing. Machine learning analyses confirmed these architectural differences, with the music condition achieving optimal neural prediction of behavioral performance through distributed connectivity patterns while control condition relied on concentrated processing. Our findings support a Cross-Modal Temporal Scaffolding Theory, demonstrating that structured temporal context signals from one modality (here, music) can create more efficient neural states for sequence processing in another through dual mechanisms: enhanced memory integration through schema-guided learning and reduced demands on explicit control resources. These results identify network-level principles for optimizing statistical learning, with broad implications for understanding how environmental context shapes human learning capacity.

neuroscience↗