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Record W1998407076 · doi:10.1162/jocn.2010.21521

Sound Processing Hierarchy within Human Auditory Cortex

2010· article· en· W1998407076 on OpenAlexaff
Hidehiko Okamoto, Henning Stracke, Patrick Bermudez, Christo Pantev

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsMcGill University
FundersDeutsche Forschungsgemeinschaft
KeywordsAuditory cortexMagnetoencephalographyPsychologyAuditory maskingAuditory perceptionPerceptionSensory systemMasking (illustration)Auditory scene analysisAuditory systemComputational auditory scene analysisAudiologyCommunicationNeuroscienceElectroencephalographySpeech recognitionComputer science

Abstract

fetched live from OpenAlex

Both attention and masking sounds can alter auditory neural processes and affect auditory signal perception. In the present study, we investigated the complex effects of auditory-focused attention and the signal-to-noise ratio of sound stimuli on three different auditory evoked field components (auditory steady-state response, N1m, and sustained field) by means of magnetoencephalography. The results indicate that the auditory steady-state response originating in primary auditory cortex reflects the signal-to-noise ratio of physical sound inputs (bottom-up process) rather than the listener's attentional state (top-down process), whereas the sustained field, originating in nonprimary auditory cortex, reflects the attentional state rather than the signal-to-noise ratio. The N1m was substantially influenced by both bottom-up and top-down neural processes. The differential sensitivity of the components to bottom-up and top-down neural processes, contingent on their level in the processing pathway, suggests a stream from bottom-up driven sensory neural processing to top-down driven auditory perception within human auditory cortex.

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)0.004
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 score0.220
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.309
Teacher spread0.274 · 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

Citations58
Published2010
Admission routes1
Has abstractyes

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