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Record W2008483971 · doi:10.3758/s13414-011-0097-7

A model of top-down gain control in the auditory system

2011· article· en· W2008483971 on OpenAlexaff
Bruce A. Schneider, Scott Parker

Bibliographic record

VenueAttention Perception & Psychophysics · 2011
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsNipissing UniversitySchneider Electric (Canada)University of Toronto
Fundersnot available
KeywordsAutomatic gain controlNonlinear systemTone (literature)AmplifierStimulus (psychology)Auditory systemMathematicsAcousticsSpeech recognitionControl theory (sociology)Computer scienceAudiologyPhysicsPsychologyControl (management)TelecommunicationsArtificial intelligenceBandwidth (computing)

Abstract

fetched live from OpenAlex

To evaluate a model of top-down gain control in the auditory system, 6 participants were asked to identify 1-kHz pure tones differing only in intensity. There were three 20-session conditions: (1) four soft tones (25, 30, 35, and 40 dB SPL) in the set; (2) those four soft tones plus a 50-dB SPL tone; and (3) the four soft tones plus an 80-dB SPL tone. The results were well described by a top-down, nonlinear gain-control system in which the amplifier's gain depended on the highest intensity in the stimulus set. Individual participants' identification judgments were generally compatible with an equal-variance signal-detection model in which the mean locations of the distribution of effects along the decision axis were determined by the operation of this nonlinear amplification system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.059
GPT teacher head0.283
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations12
Published2011
Admission routes1
Has abstractyes

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