Young Basketball Players’ Multiple Object Tracking Skills Were Unaffected by Stroop-Induced Mental Fatigue
Bibliographic record
Abstract
We aimed to examine the acute effect of mental fatigue on young basketball players’ three-dimensional multiple object tracking (3D-MOT) skills. Our participants were 12 adolescent basketball players ( M age = 16.66, SD = 1.87 years; M years of practice = 2.66, SD = 1.07 years). In nine lab visits, we used visits 1 to 7 to familiarize participants with 3D-MOT, a subjective scale of mental fatigue, and a Stroop task involving mental set shifting. In the last two visits, participants performed in both experimental (EXP) and control (CON) conditions that were presented in randomized order. In the EXP condition, participants performed 3D-MOT pre- and post-60 minutes of induced mental fatigue; in the CON condition, they watched a documentary. After each condition, B participants performed the National Aeronautics and Space Administration Task Load Index (NASA-TLX). 3D-MOT performance measures were the “score” and “fastest trial score success.” The response time on the Stroop tasks increased throughout the mental fatigue inducement in the experimental condition ( p = .0037). The NASA-TLX responses were higher following the EXP condition than following the CON condition for mental demand, temporal demand, and performance (all ps < .05). Still, there were no significant EXP versus CON differences on the 3D-MOT performance indicators.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".