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Record W2289333796 · doi:10.4324/9781315775845.ch8

Music Perception and Embodied Music Cognition

2015· book-chapter· en· W2289333796 on OpenAlexaff
Marc Leman, Pieter‐Jan Maes

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsEmbodied cognitionPerceptionMusic psychologyMusic perceptionPsychologyCognitionCognitive scienceCognitive psychologyMusic and emotionAestheticsCommunicationArtMusicologyMusic educationComputer scienceMusic historyNeuroscienceArtificial intelligencePedagogy

Abstract

fetched live from OpenAlex

Music has the capacity to affect humans’ affective, social and cognitive abilities in different ways. For example, music may be used to regulate a person’s mood after a stressful day driving home. Music may be used to connect with other people during rituals, or dancing parties. And many people use music to enjoy the different flavors of sound in aesthetic experiences at home, to learn about the quality of organized sounds and interpret their meanings along with its flow. The different ways of interacting with music have in common that music is most engaging and thus attractive and alluring. People get involved with music and want to be fully absorbed by it. Apparently, there are few things in our environment that touch human nature so profoundly. Why is it? And what is the possible benefit of this musical power? These questions turn out to be extremely difficult and hard to answer because musical experience is subtle and ineffable. Moreover, there are many variable factors that play a role, such as the energetic level or mood of the subject involved with music, the previous preoccupations and conditioning, personality, familiarity, cultural background, context, or educational level. In short, the elusive character of music and the variable context in which music is dealt with make it a challenging research topic. Nevertheless, the power of music touches the core of our human abilities and its understanding necessitates an interdisciplinary research approach. As a matter of fact a core factor of musical power is based on the listener’s ability to interact with music. And a major precondition of this ability is that music is perceived, that is, processed through the senses, perhaps on the basis of previous perception, perhaps involving awareness, conceptualization and interpretation. The concept of perception thus involves a range from sensation to cognition, emotion and even interpretation. In what follows we first consider music perception from a cognitive viewpoint. Then we provide a critique of this cognitive approach and we look for direct evidence for the hypothesis that music perception is integrated with other modalities of human behavior, such as movement and emotion. In the final section we present a dynamic framework in which music perception is tightly linked with body movement, action and environmental interaction.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.004
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.146
GPT teacher head0.290
Teacher spread0.144 · 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 designTheoretical or conceptual
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

Citations20
Published2015
Admission routes1
Has abstractyes

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