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Record W4224305565 · doi:10.1525/mp.2022.39.4.341

Song Imitation in Congenital Amusia

2022· article· en· W4224305565 on OpenAlexaboutno aff
Ariadne Loutrari, Cunmei Jiang, Fang Liu

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
FundersEconomic and Social Research Council
KeywordsPsychologyLyricsImitationAudiologyPerceptionPitch (Music)SyllablePitch perceptionTimbreCognitive psychologySpeech recognitionMusicalNeuroscienceAcousticsComputer science

Abstract

fetched live from OpenAlex

Congenital amusia is a neurogenetic disorder of pitch perception that may also compromise pitch production. Despite amusics’ long documented difficulties with pitch, previous evidence suggests that familiar music may have an implicit facilitative effect on their performance. It remains, however, unknown whether vocal imitation of song in amusia is influenced by melody familiarity and the presence of lyrics. To address this issue, thirteen Mandarin speaking amusics and 13 matched controls imitated novel song segments with lyrics and on the syllable /la/. Eleven out of these participants in each group also imitated segments of a familiar song. Subsequent acoustic analysis was conducted to measure pitch and timing matching accuracy based on eight acoustic measures. While amusics showed worse imitation performance than controls across seven out of the eight pitch and timing measures, melody familiarity was found to have a favorable effect on their performance on three pitch-related acoustic measures. The presence of lyrics did not affect either group’s performance substantially. Correlations were observed between amusics’ performance on the Montreal Battery of Evaluation of Amusia and imitation of the novel song. We discuss implications in terms of music familiarity, memory demands, the relevance of lexical information, and the link between perception and production.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.065
GPT teacher head0.342
Teacher spread0.277 · 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.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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