Back

Isolating the effect of beat salience on rhythmic auditory stimulation outcomes

Von Handorf, K. M.; Jagadeesan, R.; Grahn, J. A.

2024-10-16 neuroscience
10.1101/2024.10.15.618556 bioRxiv
Show abstract

Rhythmic auditory stimulation (RAS) is an intervention for gait-disordered populations that involves synchronizing footsteps to regular auditory cues. Previous research has shown that high-groove music (music that induces the desire to move or dance to it) improves gait relative to low-groove music, but how this effect occurs is unclear. Greater beat salience in high-groove music may improve gait because salient beats are easier to synchronize with. Here, we manipulated beat salience by embedding metronome tones to emphasize beat onsets in both high- and low-groove music. We expected that, if beat salience drives gait improvements to high-groove music, then embedding metronome in low-groove music would elicit similar gait improvements (e.g. increased stride velocity). Here, we quantified gait synchronization in terms of period-matching (overall step rate to the cue pace) and phase-matching (individual step onsets to beat onsets). We tested a sample of healthy younger and older adults, with auditory cues matched to 10% faster than baseline. Low-groove music with embedded metronome, compared to without, elicited better period-matching; there were no differences between metronome conditions in high-groove music. These findings suggest gait improvements to high-groove music could be due to its high beat salience. On the other hand, embedded metronome did not improve phase-matching accuracy, but high-groove music did. This suggests that beat salience may not improve gait via easing step-to-beat synchronization, but rather through an overall increase in movement vigor.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.