A plausible link between the time-on-task effect and the sequential task effect
Bibliographic record
Abstract
Mental fatigue can be studied by using either the time-on-task protocol or the sequential task protocol. In the time-on-task protocol, participants perform a long and effortful task and a decrease in performance in this task is generally observed over time. In the sequential task protocol, a first effortful or control task is followed by a second effortful task. The performance in the second task is generally worse after the effortful task than after the control task. The principal aim of the present experiment is to examine the relationship between these two decrements in performance while concomitantly using a sequential task protocol and assessing the performance of the first effortful task as a function of time-on-task. We expect a positive correlation between these two decrements in performance. A total of 83 participants performed a 30-min fatiguing mental task (i.e., a modified Stroop task) or a control task followed by a time-to-exhaustion handgrip task. As expected, this protocol combining the time-on-task and sequential task protocols allowed us to observe (1) a decrease in performance over time during the Stroop task, (2) a worst performance in the handgrip task after the Stroop task by comparison to the control task, (3) a positive correlation between these two effects. The decrease in performance during the Stroop task also correlated with the subjective measures of boredom and fatigue, whereas the detrimental effect observed in the handgrip task did not. Our findings suggest that the two fatigue-related phenomena share a common mechanism but are not completely equivalent.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".