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Record W2606223328 · doi:10.1037/pmu0000126

Unlocking the mysteries of music in your brain, Dr. Daniel Levitin public lecture.

2015· article· en· W2606223328 on OpenAlexaboutno aff
David J. Baker

Bibliographic record

VenuePsychomusicology Music Mind and Brain · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
Fundersnot available
KeywordsSymphonyTheme (computing)Active listeningVisual artsMedia studiesSociologyPsychologyArtArt historyCommunicationComputer science

Abstract

fetched live from OpenAlex

On the second day of the 2015 British Broadcasting Corporation (BBC) Proms, Dr. Daniel Levitin, James McGill Professor of Neuroscience at McGill University, gave the first public lecture in the history of the Proms entitled Unlocking the Mysteries of in Your Brain. Never before in the history of the BBC Proms (an annual festival that aims to make classical music more accessible to a wider audience) has a large-scale lecture been included, so the choice of music cognition as the inaugural topic was particularly significant.The lecture featured Levitin in his familiar role as the general public ambassador for our field. His popular books such as Is Your Brain on Music (Levitin, 2011), which has been translated into over 18 languages, and World in Six Songs (Levitin, 2008), as well as his frequent radio and TV exposure, have garnered a large following, as was evident from the packed concert hall at the Royal College of Music, where the lecture was held. The talk was inspired by the theme of memory, and in particular, by a unique Prom where the Aurora Orchestra would play the entirety of Beethoven's Pastoral Symphony from memory.Levitin hooked his audience in with the often cited finding of music's ubiquity in human culture and its ability to engage large amounts of neural resources, even speculating that one day music researchers might be able to know what piece of music one is listening to by simply looking at brain scans. After laying this general groundwork, he introduced the idea that different aspects of music are processed independently of each other using the modular model of Peretz and Coltheart (2003) to illustrate how neuropsychological case studies, and especially double dissociations, can inform us about how music is processed in the brain.In the spirit of an entertaining public lecture, a crash course on neuroimaging was followed by some audience participation. Levitin first asked the audience to imagine the opening of Beethoven's Fifth Symphony, before conducting them to sing in unison. The example served to demonstrate the existence of so-called latent Absolute Pitch-because audience members roughly converged on the same opening note G-the note that is performed and consequently experienced and remembered (as demonstrated under stricter controlled condition in Levitin, 1994). This led into the anticipated discussion on music, memory, and its component parts of anticipation and pleasure.To illustrate the brain's remarkable ability remember, recognize, and extrapolate information from new musical material Levitin played a clip of a mandolin ensemble playing some Tchaikovsky. This demonstrated that melody can be recognized regardless of whether its surface features (e.g., timbre) match those under which the original tune was encoded. Levitin then went on to play a splice of Elton John's Benny and the Jets to conversely demonstrate how timbre could, on the other hand, convey a wealth of information about song identity, presumably referring to Carol Krumhansl's (2010) work concerning Thin Slices.After a few of these ears-on demonstrations, Levitin started to tease apart the question that prompted the lecture: how an orchestra could memorize such enormous quantities of music. …

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.174
GPT teacher head0.329
Teacher spread0.154 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2015
Admission routes1
Has abstractyes

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