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Record W2896129807 · doi:10.1121/1.5068106

The experience of music emotion in older adults with hearing loss and hearing aids

2018· article· en· W2896129807 on OpenAlexaff
Frank Russo, Emma Scholey

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsArousalAudiologyHearing lossPsychologyValence (chemistry)Skin conductanceDevelopmental psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

The current study examined the effect of hearing loss and hearing aids in older adults on the experience of music emotion. Fifty-four participants aged 60 and above were recruited, including 18 with normal hearing (NH), 18 with hearing impairment (HI), and 18 with hearing impairment who use hearing aids (HA). Arousal ratings and skin conductance responses were obtained from participants across 24 extracts of film music that were previously validated as conveying one of four emotions: happy, sad, fearful, and tender. These four emotions represent a crossing of arousal (high and low) and valence (positive and negative). For each group, an “arousal range” was calculated as the average arousal ratings of happy and fearful excerpts, minus the average arousal ratings of tender and sad excerpts. A similar scheme was used for each group to determine a “skin conductance range”. For negatively-valenced music, groups did not differ with regard to arousal of skin conductance. For positively valenced music, NH and HA yielded a larger arousal range than did HI. A similar pattern emerged from the analysis of skin conductance range. Results will be discussed with regard to the acoustic cues to emotion in music and signal processing methods in hearing aids.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.022
GPT teacher head0.331
Teacher spread0.310 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

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