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Record W4237866352 · doi:10.1162/jocn.2008.20001

Interactive Processing of Timbre Dimensions: An Exploration with Event-related Potentials

2007· article· en· W4237866352 on OpenAlexaff
Anne Caclin, Stephen McAdams, Bennett K. Smith, Marie‐Hélène Giard

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsTimbrePsychologyPerceptionStimulus (psychology)Event-related potentialPsychoacousticsCommunicationCognitive psychologySpeech recognitionElectroencephalographyComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Abstract Timbre characterizes the identity of a sound source. On psychoacoustic grounds, it has been described as a multidimensional perceptual attribute of complex sounds. Using Garner's interference paradigm, we found in a previous behavioral study that three timbral dimensions exhibited interactive processing. These timbral dimensions acoustically corresponded to attack time, spectral centroid, and spectrum fine structure. Here, using event-related potentials (ERPs), we sought neurophysiological correlates of the interactive processing of these dimensions of timbre. ERPs allowed us to dissociate several levels of interaction, at both early perceptual and late stimulus identification stages of processing. The cost of filtering out an irrelevant timbral dimension was accompanied by a late negative-going activity, whereas congruency effects between timbre dimensions were associated with interactions in both early sensory and late processing stages. ERPs also helped to determine the similarities and differences in the interactions displayed by the different pairs of timbre dimensions, revealing in particular variations in the latencies at which temporal and spectral timbre dimensions can interfere with the processing of another spectral timbre dimension.

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)0.001
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.005
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 head0.061
GPT teacher head0.354
Teacher spread0.293 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations26
Published2007
Admission routes1
Has abstractyes

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