OSCR

Distinct Rhythmic Competencies Identified via Internet-Based Assessment.

Code ↔ Paper

12 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 12 matches
  1. [1] § Results › Links With Questionnaire Data ↔ Scripts and Data/04_Correlation_Regression_Analysis_OSF.R, lines 44–84 · score 0.87 · trouble recognizing songs, logistic regression, predicted cluster, good dancer, weak rhythm, strong rhythm
  2. [2] § Materials and Methods › Participants ↔ Scripts and Data/04_Correlation_Regression_Analysis_OSF.R, lines 1–41 · score 0.80 · formal dance training, high school, formal music training, bachelor, college, master
  3. [3] § Materials and Methods › Tasks › Questionnaire ↔ Scripts and Data/04_Correlation_Regression_Analysis_OSF.R, lines 154–199 · score 0.73 · trouble recognizing songs, good dancer, musical beat, lyrics, rarely, tune
  4. [4] § Materials and Methods › Analysis › Principal Component and Hierarchical Cluster Analyses ↔ Scripts and Data/03_MachineLearning_MultinomialRegression_PerceptionANDProduction_OSF.R, lines 107–151 · score 0.69 · Multinominal logistic regression, cross validated, LOOCV, model, accuracy, trained
  5. [5] § Results › Principal Component Analysis—Perception Tasks ↔ Scripts and Data/01_PCA_Analysis_OSF.R, lines 174–217 · score 0.69 · Paced_Music1_rev, Paced_Music2_rev, Unpaced_rev, principal components, perception variables, BbMAT
  6. [6] § Results › Principal Component Analysis—Perception Tasks ↔ Scripts and Data/01_PCA_Analysis_OSF.R, lines 220–286 · score 0.64 · complex beat extraction, general rhythm perception, beat alignment, sequence, memory, Dimension
  7. [7] § Materials and Methods › Analysis › Principal Component and Hierarchical Cluster Analyses ↔ Scripts and Data/02_Cluster_testing_n=300_OSF.R, lines 229–273 · score 0.63 · Games Howell, oneway.test, musical training, unequal, Cohen, Levene
  8. [8] § Materials and Methods › Analysis › Principal Component and Hierarchical Cluster Analyses ↔ Scripts and Data/03_MachineLearning_MultinomialRegression_PerceptionANDProduction_OSF.R, lines 2–44 · score 0.60 · multinomial logistic regression, cluster membership, predicted, Hierarchical, PCA
  9. [9] § Materials and Methods › Analysis › Principal Component and Hierarchical Cluster Analyses ↔ Scripts and Data/01_PCA_Analysis_OSF.R, lines 44–82 · score 0.55 · good dancer, production variables, reversed, ITI, PM2, PM1
  10. [10] § Results › Descriptive Results ↔ Scripts and Data/05_Supp_Material_OSF.R, lines 102–173 · score 0.55 · FA rates, response bias, phase, hits, accuracy, prime
  11. [11] § Results › Principal Component Analysis—Perception Tasks ↔ Scripts and Data/03_MachineLearning_MultinomialRegression_PerceptionANDProduction_OSF.R, lines 46–105 · score 0.54 · Paced_Music1_rev, Paced_Music2_rev, Unpaced_rev, BbMAT, scores, variables
  12. [12] § Materials and Methods › Analysis › Principal Component and Hierarchical Cluster Analyses ↔ Scripts and Data/04_Correlation_Regression_Analysis_OSF.R, lines 154–199 · score 0.52 · good dancer, musical beat, production tasks, ITI, PM2, PM1

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 201 lines · 10 KB · no license · 4 matches

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It can be read at the source: Scripts and Data/04_Correlation_Regression_Analysis_OSF.R.

Overview

Authors: Anna Fiveash1,2,3, Nicholas E. V. Foster4,5,6, Reyna L. Gordon7,8, Simone Dalla Bella4,5,6,9, Barbara Tillmann1,2
  1. Université Bourgogne Europe, CNRS, LEAD, UMR5022 Dijon France
  2. Université Claude Bernard Lyon 1, INSERM, CNRS, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292 Bron France
  3. The MARCS Institute for Brain, Behaviour and Development Western Sydney University Sydney Australia
  4. International Laboratory for Brain, Music, and Sound Research (BRAMS) Montreal Canada
  5. Department of Psychology University of Montreal Montreal Canada
  6. Centre for Research on Brain Language and Music (CRBLM) Montreal Canada
  7. Department of Otolaryngology‐Head & Neck Surgery Vanderbilt University Medical Center Nashville Tennessee USA
  8. Department of Hearing and Speech Sciences, VUMC Vanderbilt Brain Institute Vanderbilt University Nashville Tennessee USA
  9. VIZJA University Warsaw Poland
Institutions: Lyon 1 Université (France); Centre National de la Recherche Scientifique (France); Inserm (France); Centre de Recherche en Neurosciences de Lyon (France); Laboratoire d'étude de l'apprentissage et du développement (France); Western Sydney University (Australia); Centre for Research on Brain Language and Music (Canada); International Laboratory for Brain, Music and Sound Research (Canada); Université de Montréal (Canada); Vanderbilt University (United States); Vanderbilt University Medical Center (United States); VIZJA University (Poland)
Journal: Annals of the New York Academy of Sciences, volume 1562, issue 1, article e70372
Dates: received 13 January 2026; accepted 11 June 2026; published online 11 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/nyas.70372 · PMID 42579275 · PMCID PMC13460242 · OpenAlex W7202200013
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Single-unit activity, calcium imaging
Keywords: beat, memory, music, perception, production, rhythm, rhythmic competencies
MeSH: Auditory Perception*, Internet*, Music*, Periodicity*, Adult, Female, Humans, Male, Memory, Psychomotor Performance, Time Perception (* major topic)
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Department of Education and Training | Australian Research Council (ARC) (DE220100783); National Institutes of Health (R01DC016977); Conseil régional de Bourgogne‐Franche‐Comté (MusiC); Agence Nationale de la Recherche (French National Research Agency) (ANR‐24‐CPJ1‐0133‐01)
Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

Rhythm is a fundamental element of music that recruits perception and production pathways in the brain, and occurs across different timescales. Research suggests that rhythmic processing is composed of several related, yet potentially distinct competencies, including perception, production, beat‐based processing, and sequence memory‐based processing. Based on a recent lab‐based study by Fiveash et al., the current experiment investigated whether these distinctions could be replicated with more participants (n = 300) completing an internet‐based protocol, with the potential to be scaled up to larger samples. Production tasks included unpaced tapping and paced tapping to music (from the Battery for the Assessment of Auditory Sensorimotor and Timing Abilities). Perception tasks included the beat alignment test, the Burgundy best Musical Aptitude Test (synchronization), and the beat‐based advantage task. Principal component analyses revealed strong overlap across tests. However, separations were shown between perception and production tasks, and unpaced and paced tapping tasks. Cluster analyses revealed participants with different rhythmic profiles, for example, (1) strong beat‐based rhythm perception, weak sequence memory‐based rhythm perception; and (2) strong production, weak perception. These results have implications for future research investigating individual differences in general and clinical populations, with the potential to guide clinical interventions.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 12 matches between paragraphs and lines of code.

OSF 4r5hd

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: R (5)
Size: 17 files, 5 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), tidyverse (4 files), reshape2 (3 files), car (2 files), psych (2 files), caret (1 file), easystats (1 file), ggpubr (1 file), rstatix (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
5 files, to read at the source

This repository has no license: its authors keep all rights. Read it at the source.

At the source: osf.io/4r5hd/overview?view_only=58f4733b79a84624928edd59c25d6ef0

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 5 scripts, each with its path and the digest of its content;
  • 12 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

All data and scripts used in the current analysis are available here: https://osf.io/4r5hd/overview?view_only=58f4733b79a84624928edd59c25d6ef0.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 3, 28 September 2026

  • Publisher: — → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 7 keywords, 11 MeSH terms, 4 funders, 71 references.

Cite

This paper

Fiveash, A., Foster, N. E. V., Gordon, R. L., Dalla Bella, S., & Tillmann, B. (2026). Distinct Rhythmic Competencies Identified via Internet-Based Assessment. Annals of the New York Academy of Sciences, 1562(1), e70372. https://doi.org/10.1111/nyas.70372

BibTeX

@article{fiveash2026distinct,
author = {Fiveash, Anna and Foster, Nicholas E. V. and Gordon, Reyna L. and Dalla Bella, Simone and Tillmann, Barbara},
title = {{Distinct Rhythmic Competencies Identified via Internet-Based Assessment}},
journal = {Annals of the New York Academy of Sciences},
year = {2026},
month = aug,
volume = {1562},
number = {1},
pages = {e70372},
publisher = {Wiley},
issn = {0077-8923},
doi = {10.1111/nyas.70372},
url = {https://doi.org/10.1111/nyas.70372},
pmid = {42579275},
pmcid = {PMC13460242}
}

RIS

TY - JOUR
AU - Fiveash, Anna
AU - Foster, Nicholas E. V.
AU - Gordon, Reyna L.
AU - Dalla Bella, Simone
AU - Tillmann, Barbara
TI - Distinct Rhythmic Competencies Identified via Internet-Based Assessment
T2 - Annals of the New York Academy of Sciences
J2 - Ann N Y Acad Sci
PY - 2026
DA - 2026/08/01
VL - 1562
IS - 1
SP - e70372
SN - 0077-8923
PB - Wiley
DO - 10.1111/nyas.70372
UR - https://doi.org/10.1111/nyas.70372
LA - en
ER -

CSL-JSON

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