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Record W2139923019 · doi:10.1080/1357650x.2014.953956

Perceptual asymmetries in a time estimation task with emotional sounds

2014· article· en· W2139923019 on OpenAlexaff
Daniel Voyer, Emily Reuangrith

Bibliographic record

VenueLaterality Asymmetries of Body Brain and Cognition · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAngerHappinessPsychologyBisectionTime perceptionTone (literature)PerceptionAudiologyTask (project management)Interval (graph theory)Cognitive psychologyMathematicsSocial psychology

Abstract

fetched live from OpenAlex

Four experiments investigated potential interactions between emotional content and perceptual asymmetries in the estimation of short time intervals. In all experiments, the word "bower" was presented monaurally to the left or right ear in an emotional tone and participants performed a temporal bisection task. In Experiment 1, angry and neutral stimuli ranged in duration from 260 to 440 ms (in steps of 20 ms) whereas in Experiments 2-4, durations ranged from 260 to 480 ms (in steps of 20 ms). In Experiment 3, the emotional tone of happiness replaced anger. In Experiment 4, anger and happiness were used as stimuli. In all experiments, results showed a larger bisection point for the right compared to the left ear. In addition, in all experiments, the constant error was farther away from zero for the right than for the left ear. The bisection point was also longer for the angry (Experiments 1 and 2) or happy (Experiment 3) than for the neutral emotional tone. Finally, happiness produced a shorter bisection point than anger in Experiment 4. Results are discussed in terms of their implications for time perception mechanisms and their potential cerebral representation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.245
Teacher spread0.231 · 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 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

Citations14
Published2014
Admission routes1
Has abstractyes

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