The effects of mental fatigue on explicit and implicit contributions to visuomotor adaptation
Bibliographic record
Abstract
The goal of the current research was to establish the impact of mental fatigue on the contributions of explicit (i.e., conscious strategy) and implicit (unconscious) processes to visuomotor adaptation. Participants were divided into two groups, a Mental Fatigue (MF) group who completed a cognitively demanding 32-minute time load dual back task (TLDB), and a Control group who watched a documentary for a similar length of time. Following the TLDB task or documentary watching, participants trained to reach with a visuomotor distortion, such that cursor feedback was rotated 40º clockwise relative to hand motion. Explicit and implicit contributions to visuomotor adaptation were assessed following 3 blocks of 45 rotated reach training trials and again following a 20-minute rest. Levels of mental fatigue, as indicated on a self-report scale, increased significantly for the MF group following the TLDB task. The Control group did not display a similar increase in mental fatigue following the documentary watching. Results then revealed a decrease in visuomotor adaptation early in training for the MF group compared to the Control group, as well as decreased retention of visuomotor adaptation immediately following the 20-minute rest. Furthermore, correlational analyses revealed that greater levels of mental fatigue reported by participants in the MF group were associated with less explicit adaptation and greater implicit adaptation. Similar trends were not observed for the Control group. Taken together, the decreased visuomotor adaptation observed early in training, as well as the moderate correlation between increased mental fatigue and decreased explicit adaptation, suggest that mental fatigue decreases one's ability to engage in explicit processing, limiting the overall extent of initial visuomotor adaptation achieved.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".