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Record W2140616900

Perceiving filled vs. empty time intervals: A comparison of adjustment and magnitude estimation methods

2012· article· en· W2140616900 on OpenAlexaff
Emi Hasuo, Yoshitak Nakajima, Erika Tomimatsu, Simon Grondin, Kazuo Ueda

Bibliographic record

VenueProceedings of Fechner Day · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsIllusionDuration (music)Time perceptionStatisticsInterval (graph theory)Magnitude (astronomy)PerceptionMathematicsOffset (computer science)EstimationPsychophysicsAudiologyPsychologyCognitive psychologyComputer scienceAcousticsMedicinePhysicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

A time interval between the onset and the offset of a continuous sound (filled interval) is often perceived to be longer than a time interval between two successive brief sounds (empty interval) of the same physical duration. The present study examined the occurrence of such phenomenon, sometimes called the filled duration illusion, for time intervals of 40-520 ms with the method of adjustment and the method of magnitude estimation. When the method of adjustment was used, the filled duration illusion appeared clearly for a few participants, while it did not appear for the majority of participants. With magnitude estimation, the filled duration illusion was more likely to occur. The amounts of the illusion did not correlate between the two methods, and it was suggested that even for the same participant, the perception of the empty and the filled intervals can be influenced by the experimental methods.

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)0.004
metaresearch head score (Gemma)0.041
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.374
Teacher spread0.318 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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Same venueProceedings of Fechner DaySame topicNeuroscience and Music PerceptionFrench-language works237,207