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Record W3204635018 · doi:10.3389/fpsyg.2021.709979

Accuracy of Tempo Judgments in Disk Jockeys Compared to Musicians and Untrained Individuals

2021· article· en· W3204635018 on OpenAlexaff
Nicholas E.V. Foster, Lauriane Beffa, Alexandre Lehmann

Bibliographic record

VenueFrontiers in Psychology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill UniversityInternational Laboratory for Brain, Music and Sound ResearchCentre for Research on Brain Language and Music
Fundersnot available
KeywordsMetronomeMelodyPsychologyRhythmAudiologyCognitive psychologyPerceptionMusicalPopulationCommunicationSpeech recognitionComputer scienceVisual artsArtNeuroscienceAesthetics

Abstract

fetched live from OpenAlex

Professional disk jockeys (DJs) are an under-studied population whose performance involves creating new musical experiences by combining existing musical materials with a high level of temporal precision. In contemporary electronic dance music, these materials have a stable tempo and are composed with the expectation for further transformation during performance by a DJ for the audience of dancers. Thus, a fundamental aspect of DJ performance is synchronizing the tempo and phase of multiple pieces of music, so that over seconds or even minutes, they may be layered and transitioned without disrupting the rhythmic pulse. This has been accomplished traditionally by manipulating the speed of individual music pieces "by ear," without additional technological synchronization aids. However, the cumulative effect of this repeated practice on auditory tempo perception has not yet been evaluated. Well-known phenomena of experience-dependent plasticity in other populations, such as musicians, prompts the question of whether such effects exist in DJs in their domain of expertise. This pilot study examined auditory judgments of tempo in 10 professional DJs with experience mixing by ear, compared to 7 percussionists, 12 melodic instrumental musicians, and 11 untrained controls. Participants heard metronome sequences between 80 and 160 beats per minute (BPM) and estimated the tempo. In their most-trained tempo range, 120-139 BPM, DJs were more accurate (lower absolute percent error) than untrained participants. Within the DJ group, 120-139 BPM exhibited greater accuracy than slower tempos of 80-99 or 100-119 BPM. DJs did not differ in accuracy compared to percussionists or melodic musicians on any BPM range. Percussionists were more accurate than controls for 100-119 and 120-139 BPM. The results affirm the experience-dependent skill of professional DJs in temporal perception, with comparable performance to conventionally trained percussionists and instrumental musicians. Additionally, the pattern of results suggests a tempo-specific aspect to this training effect that may be more pronounced in DJs than percussionists and musicians. As one of the first demonstrations of enhanced auditory perception in this unorthodox music expert population, this work opens the way to testing whether DJs also have enhanced rhythmic production abilities, and investigating the neural substrates of this skill compared to conventional musicians.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.346
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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