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Record W2152838500 · doi:10.1080/09658211.2013.770871

The effects of emotion on memory for music and vocalisations

2013· article· en· W2152838500 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMemory · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité de MontréalDouglas Mental Health University InstituteMcGill UniversityInternational Laboratory for Brain, Music and Sound Research
FundersCanadian Institutes of Health Research
KeywordsPsychologySadnessHappinessCognitive psychologyContext (archaeology)MusicalAngerSocial psychology

Abstract

fetched live from OpenAlex

Music is a powerful tool for communicating emotions which can elicit memories through associative mechanisms. However, it is currently unknown whether emotion can modulate memory for music without reference to a context or personal event. We conducted three experiments to investigate the effect of basic emotions (fear, happiness, and sadness) on recognition memory for music, using short, novel stimuli explicitly created for research purposes, and compared them with nonlinguistic vocalisations. Results showed better memory accuracy for musical clips expressing fear and, to some extent, happiness. In the case of nonlinguistic vocalisations we confirmed a memory advantage for all emotions tested. A correlation between memory accuracy for music and vocalisations was also found, particularly in the case of fearful expressions. These results confirm that emotional expressions, particularly fearful ones, conveyed by music can influence memory as has been previously shown for other forms of expressions, such as faces and vocalisations.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.032
GPT teacher head0.262
Teacher spread0.229 · 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