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Record W2793658297 · doi:10.1163/22134468-00002096

The Effect of Emotional Spoken Words on Time Perception Depends on the Gender of the Speaker

2018· article· en· W2793658297 on OpenAlexaff
Giovanna Mioni, Vincent Laflamme, Massimo Grassi, Simon Grondin

Bibliographic record

VenueTiming & Time Perception · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsValence (chemistry)PsychologyArousalPerceptionEmotional valenceAudiologyDevelopmental psychologySocial psychologyCognitionMedicine

Abstract

fetched live from OpenAlex

The aim of the present study was to investigate the influence of the emotional content of words marking brief intervals on the perceived duration of these intervals. Three independent variables were of interest: the gender of the person pronouncing the words, the gender of participants, and the valence (positive or negative) of the words in conjunction with their arousing properties. A bisection task was used and the tests, involving four different combinations of valence and arousing conditions (plus a neutral condition), were randomized within trials. The main results revealed that when the valence is negative, participants responded ‘short’ more often when words were pronounced by women rather than by men, and this effect occurred independently of the arousal condition. The results also revealed that overall, males responded ‘longer’more often than females. Finally, in the negative and low arousal condition, the Weber ratio was higher (lower sensitivity) when a male voice was used than when a female voice was used. This study shows that the gender of the person producing the stimuli whose duration is to be judged should be taken into account when analyzing the effect of emotion on time perception.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.037
GPT teacher head0.281
Teacher spread0.244 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations9
Published2018
Admission routes1
Has abstractyes

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