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Record W2193381975 · doi:10.1093/scan/nsv074

Pitch underlies activation of the vocal system during affective vocalization

2015· article· en· W2193381975 on OpenAlexaff
Michel Belyk, Steven Brown

Bibliographic record

VenueSocial Cognitive and Affective Neuroscience · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychologyPhonationProsodyAnterior cingulate cortexFunctional magnetic resonance imagingPerceptionStimulus (psychology)NeuroscienceCognitive psychologyAudiologyCognitionSpeech recognition

Abstract

fetched live from OpenAlex

Affective prosody is that aspect of speech that conveys a speaker's emotional state through modulations in various vocal parameters, most prominently pitch. While a large body of research implicates the cingulate vocalization area in controlling affective vocalizations in monkeys, no systematic test of functional homology for this area has yet been reported in humans. In this study, we used functional magnetic resonance imaging to compare brain activations when subjects produced affective vocalizations in the form of exclamations vs non-affective vocalizations with similar pitch contours. We also examined the perception of affective vocalizations by having participants make judgments about either the emotions being conveyed by recorded affective vocalizations or the pitch contours of the same vocalizations. Production of affective vocalizations and matched pitch contours activated a highly overlapping set of brain areas, including the larynx-phonation area of the primary motor cortex and a region of the anterior cingulate cortex that is consistent with the macro-anatomical position of the cingulate vocalization area. This overlap contradicts the dominant view that these areas form two distinct vocal pathways with dissociable functions. Instead, we propose that these brain areas are nodes in a single vocal network, with an emphasis on pitch modulation as a vehicle for affective expression.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.373
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.000
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 head0.059
GPT teacher head0.301
Teacher spread0.242 · 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

Citations36
Published2015
Admission routes1
Has abstractyes

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