MétaCan
Menu
Back to cohort

Multisensory Processing in Music

2018· reference-entry· en· W2897985595 on OpenAlexaff
Frank Russo

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMultisensory integrationTimbrePsychologyCognitive psychologyModularity (biology)Focus (optics)Perspective (graphical)Cognitive scienceSensory processingNeuroscienceSensory systemComputer scienceMusicalArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter provides an overview of multisensory processing in music by individuals with normal hearing, hearing loss, and deafness. The first section provides an account of theory and evidence regarding the neural mechanisms underpinning multisensory processing. The second section considers auditory-only processing of music with a focus on lateralization, basic modularity, and pathways. The final section considers non-auditory and multisensory processing of pitch, timbre, and rhythm. For each dimension, psychophysical evidence is presented before reviewing the extant neuroscientific evidence. Where no neuroscientific evidence exists, proposals have been made about the types of neural mechanisms that may be involved. Neuroplastic changes following deafness are also considered. The chapter ultimately argues that although most individuals will justifiably focus on sound as the core of music processing, a more inclusive and nuanced consideration of music takes a multisensory perspective, involving the integration of inputs from auditory, visual, somatosensory, vestibular, and motor areas.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.319
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1310.012

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 head0.135
GPT teacher head0.387
Teacher spread0.252 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicMultisensory perception and integrationFrench-language works237,207