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Record W2040042520 · doi:10.1080/17470210600988976

Judging Multi-Minute Intervals Retrospectively

2006· article· en· W2040042520 on OpenAlexaff
Simon Grondin, Marilyn Plourde

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDuration (music)Value (mathematics)StatisticsPsychologyCognitionTime perceptionSession (web analytics)RecallAudiologyCognitive psychologyMathematicsComputer scienceMedicinePsychiatry

Abstract

fetched live from OpenAlex

A total of 50 participants were asked to perform five different cognitive tasks lasting 120, 210, 300, 390 and 480 s, respectively. After completing the series of tasks, they were asked to estimate retrospectively the duration of each one. Psychophysical analyses linking psychological time to physical time revealed that the value of the power law exponent was about .47, but was .79 when the estimate of the total duration of the session was taken into account--a value lower than unity, indicating that shorter durations have been overestimated, and longer durations underestimated. The Weber fraction, or the ratio of variability to time, ranged from .59 (at 120 s) to .21 (at 480 s). Overall, the study shows that it is possible to make certain changes in the traditional retrospective timing method and thus adapt it for further investigations of the mechanisms involved in memory for the duration of past events.

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)0.001
metaresearch head score (Gemma)0.019
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 score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.370
Teacher spread0.312 · 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

Citations50
Published2006
Admission routes1
Has abstractyes

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