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Record W2064702209 · doi:10.1037//0096-1523.27.5.1072

Bottom-up and top-down influences on auditory scene analysis: Evidence from event-related brain potentials.

2001· article· en· W2064702209 on OpenAlex

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.

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2001
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsBaycrest Hospital
Fundersnot available
KeywordsAuditory scene analysisPerceptionPsychologyMistuningAuditory perceptionNeuroscienceActive listeningTemporal lobeAudiologyAssociation (psychology)Event-related potentialElectroencephalographyCognitive psychologyCommunicationAcousticsPhysicsMedicine

Abstract

fetched live from OpenAlex

The physiological processes underlying the segregation of concurrent sounds were investigated through the use of event-related brain potentials. The stimuli were complex sounds containing multiple harmonics, one of which could be mistuned so that it was no longer an integer multiple of the fundamental. Perception of concurrent auditory objects increased with degree of mistuning and was accompanied by negative and positive waves that peaked at 180 and 400 ms poststimulus, respectively. The negative wave, referred to as object-related negativity, was present during passive listening, but the positive wave was not. These findings indicate bottom-up and top-down influences during auditory scene analysis. Brain electrical source analyses showed that distinguishing simultaneous auditory objects involved a widely distributed neural network that included auditory cortices, the medial temporal lobe, and posterior association cortices.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.382
Teacher spread0.330 · 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