Mental fatigue limits explicit contributions to visuomotor adaptation
Bibliographic record
Abstract
To date, mental fatigue has been shown to lead to a general decline in cognitive and motor control processing. The goal of the current research was to establish the impact of mental fatigue on the contribution of explicit (i.e., conscious strategy) and implicit (unconscious) processes to visuomotor adaptation. Participants were divided into a mental fatigue (MF) and control group. Mental fatigue was induced through a time load dual back task (TLDB), in which participants were required to respond as quickly as possible to digits displayed on the screen in a choice reaction time task, as well as respond to letters based on recall of previously presented letters. The TLDB task lasted for 32 minutes, and the control group watched a documentary for a similar length of time. Subjective feelings of mental fatigue, as indicated on a self-report questionnaire, demonstrated that mental fatigue was significantly higher for the MF group after completion of the TLDB task. There was no similar increase for the control group. The increased mental fatigue was associated with decreased visuomotor adaptation to a 40-degree cursor rotation, such that participants in the MF group adapted their reaches to a lesser extent both early and late in training compared to the control group. Furthermore, correlational analyses established that greater mental fatigue reported by participants was associated with less explicit adaptation and greater implicit adaptation. Taken together, these results suggest that mental fatigue decreases the ability to engage in explicit processing, limiting the overall extent of 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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".