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Record W2912525810 · doi:10.1073/pnas.1811878116

Dopamine modulates the reward experiences elicited by music

2019· article· en· W2912525810 on OpenAlexaff
Laura Ferreri, Ernest Mas‐Herrero, Robert J. Zatorre, Pablo Ripollés, Alba Gómez-Andrés, Helena Alicart, Guillem Olivé, Josep Marco‐Pallarés, Rosa Antonijoan, Marta Valle, Jordi Riba, Antoni Rodríguez‐Fornells

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill UniversityInternational Laboratory for Brain, Music and Sound ResearchMontreal Neurological Institute and Hospital
FundersInstituto de Salud Carlos IIIMinisterio de Economía y CompetitividadRotary Foundation
KeywordsDopamineSingingActive listeningPsychologyDopaminergicMusicalNeuroscienceCognitive psychologyCommunicationArtVisual arts

Abstract

fetched live from OpenAlex

= 27) were engaged in music listening. We orally administrated to each participant a dopamine precursor (levodopa), a dopamine antagonist (risperidone), and a placebo (lactose) in three different sessions. We demonstrate that levodopa and risperidone led to opposite effects in measures of musical pleasure and motivation: while the dopamine precursor levodopa, compared with placebo, increased the hedonic experience and music-related motivational responses, risperidone led to a reduction of both. This study shows a causal role of dopamine in musical pleasure and indicates that dopaminergic transmission might play different or additive roles than the ones postulated in affective processing so far, particularly in abstract cognitive activities.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.056
GPT teacher head0.303
Teacher spread0.247 · 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 designObservational
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

Citations443
Published2019
Admission routes1
Has abstractyes

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