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Record W1524921585

Can silence affect perception? Duration and frequency of occurrence in perceived pitch structure

2006· article· en· W1524921585 on OpenAlexaffvenue
Michael E. Lantz, Lola L. Cuddy

Bibliographic record

VenueCanadian acoustics · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsQueen's UniversityUniversity of Prince Edward Island
Fundersnot available
KeywordsDuration (music)PerceptionTone (literature)SilenceSequence (biology)PsychologyAffect (linguistics)Speech recognitionAcousticsAudiologyFundamental frequencyMathematicsCommunicationComputer sciencePhysicsLinguistics
DOInot available

Abstract

fetched live from OpenAlex

The duration and frequency of occurrence of tones and the silence between the tones are manipulated to understand their effect on the perceived organization or pitch structure of tone sequences. Duration and frequency of occurrence are both elemental in music and act upon a single mechanism that in turn increments a magnitude accumulator. Within a sequence of tones the longer tones are judged to be more salient than the shorter but more frequent tones. Sequences were generated from six-tone tonesets, each of which contained tones of two major triads from maximally distant keys. Three sequence conditions that were created from the tonesets include the tones of one major triad that were longer than the other tones, modification of the first sequence, and modification of the original sequence. The finding suggests that the duration of an event has perceptual priority over the frequency of occurrence of the event.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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

Citations2
Published2006
Admission routes2
Has abstractyes

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