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Understanding Voice Perception

2011· review· en· W1781854261 on OpenAlexaff
Pascal Belin, Patricia E.G. Bestelmeyer, Marianne Latinus, Rebecca Watson

Bibliographic record

VenueBritish Journal of Psychology · 2011
Typereview
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversité de MontréalMcGill UniversityInternational Laboratory for Brain, Music and Sound Research
FundersBiotechnology and Biological Sciences Research Council
KeywordsPsychologyPerceptionCognitive psychologyNeuroimagingAuditory perceptionAffect (linguistics)Speech perceptionFace (sociological concept)Identity (music)Face perceptionInformation processingCommunicationNeuroscienceLinguistics

Abstract

fetched live from OpenAlex

Voices carry large amounts of socially relevant information on persons, much like 'auditory faces'. Following Bruce and Young (1986)'s seminal model of face perception, we propose that the cerebral processing of vocal information is organized in interacting but functionally dissociable pathways for processing the three main types of vocal information: speech, identity, and affect. The predictions of the 'auditory face' model of voice perception are reviewed in the light of recent clinical, psychological, and neuroimaging evidence.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
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)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.491
GPT teacher head0.477
Teacher spread0.015 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations328
Published2011
Admission routes1
Has abstractyes

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