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Record W2516230309 · doi:10.1525/mp.2017.34.3.335

Perceptual Learning of Pitch Direction in Congenital Amusia

2017· article· en· W2516230309 on OpenAlexaboutno aff
Fang Liu, Cunmei Jiang, Tom Francart, Alice H. D. Chan, Patrick C. M. Wong

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAudiologyPerceptionPitch (Music)Speech recognitionComputer scienceNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Congenital amusia is a lifelong disorder of musical processing for which no effective treatments have been found. The present study aimed to treat amusics’ impairments in pitch direction identification through auditory training. Prior to training, twenty Chinese-speaking amusics and 20 matched controls were tested on the Montreal Battery of Evaluation of Amusia (MBEA) and two psychophysical pitch threshold tasks for identification of pitch direction in speech and music. Subsequently, ten of the twenty amusics undertook 10 sessions of adaptive-tracking pitch direction training, while the remaining 10 received no training. Post training, all amusics were retested on the pitch threshold tasks and on the three pitch-based MBEA subtests. Trained amusics demonstrated significantly improved thresholds for pitch direction identification in both speech and music, to the level of non-amusic control participants, although no significant difference was observed between trained and untrained amusics in the MBEA subtests. This provides the first clear positive evidence for improvement in pitch direction processing through auditory training in amusia. Further training studies are required to target different deficit areas in congenital amusia, so as to reveal which aspects of improvement will be most beneficial to the normal functioning of musical processing.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
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
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.003
Open science0.0010.001
Research integrity0.0000.001
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 head0.066
GPT teacher head0.359
Teacher spread0.293 · 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 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

Citations25
Published2017
Admission routes1
Has abstractyes

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