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Record W2127973509 · doi:10.1162/jocn.2006.18.12.1959

Separate Neural Processing of Timbre Dimensions in Auditory Sensory Memory

2006· article· en· W2127973509 on OpenAlexaff
Anne Caclin, Elvira Brattico, Mari Tervaniemi, Risto Näätänen, Dominique Morlet, Marie‐Hélène Giard, Stephen McAdams

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsTimbreMismatch negativitySensory memoryPsychologyEchoic memoryPerceptionSensory systemStimulus (psychology)EngramLoudnessAuditory perceptionSpeech recognitionCommunicationCognitive psychologyAudiologyElectroencephalographyComputer scienceCognitionNeuroscience

Abstract

fetched live from OpenAlex

Timbre is a multidimensional perceptual attribute of complex tones that characterizes the identity of a sound source. Our study explores the representation in auditory sensory memory of three timbre dimensions (acoustically related to attack time, spectral centroid, and spectrum fine structure), using the mismatch negativity (MMN) component of the auditory event-related potential. MMN is elicited by a discriminable change in a sound sequence and reflects the detection of the discrepancy between the current stimulus and traces in auditory sensory memory. The stimuli used in the present study were carefully controlled synthetic tones. MMNs were recorded after changes along each of the three timbre dimensions and their combinations. Additivity of unidimensional MMNs and dipole modeling results suggest partially separate MMN generators for different timbre dimensions, reflecting their mainly separate processing in auditory sensory memory. The results expand to timbre dimensions a property of separation of the representation in sensory memory that has already been reported between basic perceptual attributes (pitch, loudness, duration, and location) of sound sources.

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 score0.259
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.044
GPT teacher head0.316
Teacher spread0.273 · 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

Citations121
Published2006
Admission routes1
Has abstractyes

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