Dynamics of retrospective timing: A big data approach
Bibliographic record
Abstract
Most interval timing research has focused on prospective timing tasks, in which participants are explicitly asked to pay attention to time as they are tested over multiple trials. Our current understanding of interval timing primarily relies on prospective timing. However, most real-life temporal judgments are made without knowing beforehand that the durations of events will need to be estimated (i.e., retrospective timing). The current study investigated the retrospective timing performance of ~24,500 participants with a wide range of intervals (5-90 min). Participants were asked to judge how long it took them to complete a set of questionnaires that were filled out at the participants' own pace. Participants overestimated and underestimated durations shorter and longer than 15 min, respectively. They were most accurate at estimating 15-min long events. The between-subject variability in duration estimates decreased exponentially as a function of time, reaching the lower asymptote after 30 min. Finally, a considerable proportion of participants exhibited whole number bias by rounding their duration estimates to the multiples of 5 min. Our results provide evidence for systematic biases in retrospective temporal judgments, and show that variability in retrospective timing is relatively higher for shorter durations (e.g., < 30 min). The primary findings gathered from our dataset were replicated based on the secondary analyses of another dataset (Blursday). The current study constitutes the most comprehensive study of retrospective timing regarding the range of durations and sample size tested.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".