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Record W2083781174 · doi:10.3389/fnhum.2014.00401

Neurobiological, Cognitive, and Emotional Mechanisms in Melodic Intonation Therapy

2014· review· en· W2083781174 on OpenAlexaff
Dawn L. Merrett, Isabelle Peretz, Sarah J. Wilson

Bibliographic record

VenueFrontiers in Human Neuroscience · 2014
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyMelodyCognitionMusic therapyCognitive psychologyIntonation (linguistics)PsychotherapistNeuroscienceLinguisticsMusicalArt

Abstract

fetched live from OpenAlex

Singing has been used in language rehabilitation for decades, yet controversy remains over its effectiveness and mechanisms of action. Melodic Intonation Therapy (MIT) is the most well-known singing-based therapy; however, speculation surrounds when and how it might improve outcomes in aphasia and other language disorders. While positive treatment effects have been variously attributed to different MIT components, including melody, rhythm, hand-tapping, and the choral nature of the singing, there is uncertainty about the components that are truly necessary and beneficial. Moreover, the mechanisms by which the components operate are not well understood. Within the literature to date, proposed mechanisms can be broadly grouped into four categories: (1) neuroplastic reorganization of language function, (2) activation of the mirror neuron system and multimodal integration, (3) utilization of shared or specific features of music and language, and (4) motivation and mood. In this paper, we review available evidence for each mechanism and propose that these mechanisms are not mutually exclusive, but rather represent different levels of explanation, reflecting the neurobiological, cognitive, and emotional effects of MIT. Thus, instead of competing, each of these mechanisms may contribute to language rehabilitation, with a better understanding of their relative roles and interactions allowing the design of protocols that maximize the effectiveness of singing therapy for aphasia.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.100
GPT teacher head0.349
Teacher spread0.249 · 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 designOther design
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

Citations68
Published2014
Admission routes1
Has abstractyes

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