ReviewAnnals of the New York Academy of Sciences2026
From Finger Taps to Footsteps: Gait as a Model for Investigating and Training Rhythmic Abilities.
Review in Annals of the New York Academy of Sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 authors.
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Abstract
Synchronization of movements to auditory rhythmic cues, such as music or metronomes, often occurs spontaneously. Nonetheless, important interindividual differences exist in auditory-motor synchronization (AMS). Effects of rhythm on movements are partly modulated by rhythmic abilities, which include beat perception, motor production, and sensorimotor integration. These rhythmic abilities are often assessed using finger-tapping tasks, which can be performed in highly controlled environments and are easy to implement. In this article, we present limitations associated with finger-tapping tasks and propose gait as an alternative model for investigating and training rhythmic abilities. We focus on three key elements that differentiate gait from tapping and are critical in assessing AMS: the need to coordinate multiple effectors, emergent timing associated with continuous actions, and movement automaticity. Interestingly, cued-gait interventions (i.e., walking to rhythmic auditory cues for several weeks) have shown positive effects on all aspects of rhythmic abilities, while tapping interventions (e.g., playing tablet-based serious games) might lead to more limited transfer. In sum, gait offers a functionally rich behavioral model that can capture the complexity and ecological validity necessary to study and train AMS.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.