Unlocking the mysteries of music in your brain, Dr. Daniel Levitin public lecture.
Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.034 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".