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Record W2131588844 · doi:10.1093/cercor/bhs090

Cerebral Processing of Voice Gender Studied Using a Continuous Carryover fMRI Design

2012· article· en· W2131588844 on OpenAlexaff
Ian Charest, Cyril Pernet, Marianne Latinus, Frances Crabbe, Pascal Belin

Bibliographic record

VenueCerebral Cortex · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité de MontréalMcGill UniversityInternational Laboratory for Brain, Music and Sound Research
FundersBiotechnology and Biological Sciences Research CouncilDirectorate for Biological SciencesAgence Nationale de la Recherche
KeywordsPsychologyFunctional magnetic resonance imagingAuditory cortexCategorizationPerceptionN100Stimulus (psychology)AudiologyPosterior cingulateCognitive psychologyNeuroscienceCognitionEvent-related potentialComputer scienceMedicine

Abstract

fetched live from OpenAlex

Normal listeners effortlessly determine a person's gender by voice, but the cerebral mechanisms underlying this ability remain unclear. Here, we demonstrate 2 stages of cerebral processing during voice gender categorization. Using voice morphing along with an adaptation-optimized functional magnetic resonance imaging design, we found that secondary auditory cortex including the anterior part of the temporal voice areas in the right hemisphere responded primarily to acoustical distance with the previously heard stimulus. In contrast, a network of bilateral regions involving inferior prefrontal and anterior and posterior cingulate cortex reflected perceived stimulus ambiguity. These findings suggest that voice gender recognition involves neuronal populations along the auditory ventral stream responsible for auditory feature extraction, functioning in pair with the prefrontal cortex in voice gender perception.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.130
GPT teacher head0.322
Teacher spread0.192 · 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 designObservational
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

Citations78
Published2012
Admission routes1
Has abstractyes

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