Music-evoked reactivation during continuous perception is associated with enhanced subsequent recall of naturalistic events
Williams, J. A.; Margulis, E. H.; Baldassano, C.; Hasson, U.; Chen, J.; Norman, K. A.
Show abstract
Music is a potent cue for recalling personal experiences, yet the neural basis of music-evoked memory remains elusive. We address this question by using the full-length film Eternal Sunshine of the Spotless Mind to examine how repeated musical themes reactivate previously encoded events in cortex and shape next-day recall. Participants in an fMRI study viewed either the original film (with repeated musical themes) or a no-music version. By comparing neural activity patterns between these groups, we found that music-evoked reactivation of neural patterns linked to earlier scenes in the default mode network was associated with improved subsequent recall. This relationship was specific to the music condition and persisted when we controlled for a proxy measure of initial encoding strength (spatial intersubject correlation), suggesting that music-evoked reactivation may play a role in making event memories stick that is distinct from what happens at initial encoding.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Schema representations in distinct brain networks support narrative memory during encoding and retrieval 97%
- A generalized cortical activity pattern at internally-generated mental context boundaries during unguided narrative recall 96%
- Multimodal Object Representations Rely on Integrative Coding 95%
Similar papers in this journal
- Temporal dynamics of competition between statistical learning and episodic memory in intracranial recordings of human visual cortex 96%
- Rapid memory reactivation at movie event boundaries promotes episodic encoding 95%
- Distinct neural representations of content and ordinal structure in auditory sequence memory 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.