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Record W2895717642 · doi:10.30535/mto.24.3.5

What Can the Temporal Structure of Auditory Perception Tell Us about Musical “Timelessness”?

2018· article· en· W2895717642 on OpenAlexaff
Jason D. K. Noble

Bibliographic record

VenueMusic Theory Online · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill University
Fundersnot available
KeywordsMusicalPerceptionRepertoireMusical formRepetition (rhetorical device)Time perceptionPsychologyDuration (music)Variety (cybernetics)Key (lock)Cognitive psychologyAestheticsCommunicationCognitive scienceArtLiteraturePhilosophyLinguisticsComputer scienceNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

“Timelessness” is an area of intense interest for many composers and authors interested in 20th- and 21st-century music, but it is not always clear exactly what the term denotes. In particular, the distinction between the induction of timelessness (the listener’s subjective experience of time is altered or suspended by music) and the perception of timelessness (the listener recognizes that the music expresses altered or suspended time) has yet to be clarified. This paper argues that, while experiences of timelessness may be induced by a wide variety of musics and are not necessarily contingent on specific musical qualities, the perception of musical timelessness involves relationships between music’s temporal organization and the temporal structure of auditory perception. Of particular interest are segmentation, sequence, pulse, meter, and repetition. Music whose temporal organization optimizes human information processing and embodiment expresses “human time,” and music whose temporal organization subverts or exceeds human information processing and embodiment points outside of human time, to timelessness. This hypothesis is illustrated with examples from the 20th-century repertoire by Truax, Ligeti, Crumb, Reich, Tenney, Messiaen, and Grisey, music that has been associated with timelessness.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.027
GPT teacher head0.282
Teacher spread0.255 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations9
Published2018
Admission routes1
Has abstractyes

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