MétaCan
Menu
Back to cohort
Record W2153000987 · doi:10.1162/0898929053747621

Hearing Two Things at Once: Neurophysiological Indices of Speech Segregation and Identification

2005· article· en· W2153000987 on OpenAlexaff
Claude Alain, Karen S. Reinke, Yu He, Chenghua Wang, Nancy J. Lobaugh

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsPsychologyNeurophysiologyIdentification (biology)Speech perceptionCognitive scienceCognitive psychologyCommunicationAudiologyNeurosciencePerception

Abstract

fetched live from OpenAlex

The discrimination of concurrent sounds is paramount to speech perception. During social gatherings, listeners must extract information from a composite acoustic wave, which sums multiple individual voices that are simultaneously active. The observers' ability to identify two simultaneously presented vowels improves with increasing separation between the fundamental frequencies (f0) of the two vowels. Event-related potentials to stimuli presented during attend and ignore conditions revealed activity between 130 and 170 msec after sound onset that reflected the f0 differences between the two vowels. Another, more posterior and right-lateralized, negative wave maximal at 250 msec, and a central-parietal slow negativity were observed only during vowel identification and may index stimulus categorization. This sequence of neural events supports a multistage model of auditory scene analysis in which the spectral pattern of each vowel constituent is automatically extracted and then matched against representations of those vowels in working memory.

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)0.003
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 score0.197
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
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 head0.063
GPT teacher head0.336
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

Citations69
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cognitive NeuroscienceSame topicNeuroscience and Music PerceptionFrench-language works237,207