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Record W2164295891 · doi:10.1037/a0016456

Pitch and time, tonality and meter: How do musical dimensions combine?

2009· article· en· W2164295891 on OpenAlex
Jon B. Prince, William Forde Thompson, Mark A. Schmuckler

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTonalitySalience (neuroscience)Relative pitchSpeech recognitionPitch contourPsychologyContext (archaeology)MusicalPitch (Music)AcousticsCognitive psychologyComputer sciencePerceptionPhysicsArt

Abstract

fetched live from OpenAlex

The authors examined how the structural attributes of tonality and meter influence musical pitch-time relations. Listeners heard a musical context followed by probe events that varied in pitch class and temporal position. Tonal and metric hierarchies contributed additively to the goodness-of-fit of probes, with pitch class exerting a stronger influence than temporal position (Experiment 1), even when listeners attempted to ignore pitch (Experiment 2). Speeded classification tasks confirmed this asymmetry. Temporal classification was biased by tonal stability (Experiment 3), but pitch classification was unaffected by temporal position (Experiment 4). Experiments 5 and 6 ruled out explanations based on the presence of pitch classes and temporal positions in the context, unequal stimulus quantity, and discriminability. The authors discuss how typical Western music biases attention toward pitch and distinguish between dimensional discriminability and salience.

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.

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 categoriesnone
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 score0.905
Threshold uncertainty score0.728

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.358
Teacher spread0.307 · 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