The effect of variability in temporal information on the control of a dynamic task
Bibliographic record
Abstract
The present study is about the effect of temporal uncertainty on the control of a dynamic task. Two types of temporal uncertainty are defined: Data Uncertainty (DU), the variability in temporal information, and Knowledge Uncertainty (KU), the complexity in the temporal structure of a situation. The effect of temporal data uncertainty on the control of a dynamic task with high knowledge temporal uncertainty is tested experimentally. Fifty-seven participants practiced the computerized game ‘Save the Whale’, with three levels of data uncertainty about the moment of occurrence of critical events, DU0, DU1 and DU2. Results show that performance is better in the DU0 than in the other two conditions, which do not differ from each other. The performance improves with practice at the same rate, regardless of the level of uncertainty. It is also shown that the control strategies reported by the participants become more variable with an increase in uncertainty. It is concluded that temporal data uncertainty does not limit temporal pattern learning.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".