An Ecological Approach to Prospective and Retrospective Timing of Long Durations: A Study Involving Gamers
Bibliographic record
Abstract
To date, most studies comparing prospective and retrospective timing have failed to use long durations and tasks with a certain degree of ecological validity. The present study assessed the effect of the timing paradigm on playing video games in a "naturalistic environment" (gaming centers). In addition, as it involved gamers, it provided an opportunity to examine the effect of gaming profile on time estimation. A total of 116 participants were asked to estimate prospectively or retrospectively a video game session lasting 12, 35 or 58 minutes. The results indicate that time is perceived as longer in the prospective paradigm than in the retrospective one, although the variability of estimates is the same. Moreover, the 12-minute session was perceived as longer, proportionally, than the 35- and 58-minute sessions. The study also revealed that the number of hours participants spent playing video games per week was a significant predictor of time estimates. To account for the main findings, the differences between prospective and retrospective timing are discussed in quantitative terms using a proposed theoretical framework, which states that both paradigms use the same cognitive processes, but in different proportions. Finally, the hypothesis that gamers play more because they underestimate time is also discussed.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".