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Record W2108827278 · doi:10.1093/scan/nst124

Perception of affective and linguistic prosody: an ALE meta-analysis of neuroimaging studies

2013· review· en· W2108827278 on OpenAlexafffund
Michel Belyk, Steven Brown

Bibliographic record

VenueSocial Cognitive and Affective Neuroscience · 2013
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyNeuroimagingProsodyPerceptionLinguistic analysisCognitive psychologyLinguisticsMeta-analysisNeuroscience

Abstract

fetched live from OpenAlex

Prosody refers to the melodic and rhythmic aspects of speech. Two forms of prosody are typically distinguished: 'affective prosody' refers to the expression of emotion in speech, whereas 'linguistic prosody' relates to the intonation of sentences, including the specification of focus within sentences and stress within polysyllabic words. While these two processes are united by their use of vocal pitch modulation, they are functionally distinct. In order to examine the localization and lateralization of speech prosody in the brain, we performed two voxel-based meta-analyses of neuroimaging studies of the perception of affective and linguistic prosody. There was substantial sharing of brain activations between analyses, particularly in right-hemisphere auditory areas. However, a major point of divergence was observed in the inferior frontal gyrus: affective prosody was more likely to activate Brodmann area 47, while linguistic prosody was more likely to activate the ventral part of area 44.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.289
GPT teacher head0.449
Teacher spread0.160 · 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 designMeta-analysis
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

Citations145
Published2013
Admission routes2
Has abstractyes

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