Evidence map›Paper›PMID 39506663›Full record

ReviewBMC neuroscience2024

Beat-based dancing to music has evolutionary foundations in advanced vocal learning.

Aniruddh D Patel

Abstract readReview
In one paragraph

Review in BMC neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Behavior-Relevant Periodized Neural Representation of Acoustic But Not Tactile Rhythm in Humans.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Editorial: The musical brain, volume II.Frontiers in neuroscience · 2024
    Article
  15. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Aniruddh D PatelDepartment of Psychology, Tufts University, 490 Boston Ave., Medford, MA, 02155, USA. a.patel@tufts.edu.

Funding

Investigating Auditory-Motor Interactions During Rhythm Perception in a Small Animal ModelR01NS129695 · NINDS · TUFTS UNIVERSITY MEDFORD · PI Mimi Hsiao Feng Kao, Aniruddh D Patel · 2023 to 2026
$1.5M
NINDS NIH HHS R01 NS129695
6 · The paper itself

Abstract

Dancing to music is ancient and widespread in human cultures. While dance shows great cultural diversity, it often involves nonvocal rhythmic movements synchronized to musical beats in a predictive and tempo-flexible manner. To date, the only nonhuman animals known to spontaneously move to music in this way are parrots. This paper proposes that human-parrot similarities in movement to music and in the neurobiology of advanced vocal learning hold clues to the evolutionary foundations of human dance. The proposal draws on recent research on the neurobiology of parrot vocal learning by Jarvis and colleagues and on a recent cortical model for speech motor control by Hickock and colleagues. These two lines of work are synthesized to suggest that gene regulation changes associated with the evolution of a dorsal laryngeal pitch control pathway in ancestral humans fortuitously strengthened auditory-parietal cortical connections that support beat-based rhythmic processing. More generally, the proposal aims to explain how and why the evolution of strong forebrain auditory-motor integration in the service of learned vocal control led to a capacity and proclivity to synchronize nonvocal movements to the beat. The proposal specifies cortical brain pathways implicated in the origins of human beat-based dancing and leads to testable predictions and suggestions for future research.

Indexed as

Biological EvolutionDancingLearningMusicAnimalsAuditory PerceptionHumansVocalization, AnimalBrainDanceEvolutionParietal cortexRhythmSpeechSynchronyVocal learning

Identifiers

PMID39506663
PMCPMC11539772

What OpenQuestion holds

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Registered trials

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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.