It wears me out just imagining it! Mental imagery leads to muscle fatigue and diminished performance of isometric exercise
Bibliographic record
Abstract
The beneficial effects of imagery on performance have been observed across many types of tasks yet, under certain conditions, imagery has been shown to negatively affect physical performances (Beilock et al., 2001). Recently, Macrea et al. (2014) demonstrated that imagining oneself performing acts requiring self-control led to impairments in hypothetical task scenarios involving behavioural self-control. Thus, imagery of physical tasks that require self-control may lead to suboptimal physical performance. The purpose of this study was to investigate the aftereffects of performing imagery of a physically-effortful self-control task on actual performance of a physically-effortful self-control task and muscle fatigue. Participants (N = 50) performed two isometric handgrip endurance trials (50% of maximum contraction) separated by either an imagery manipulation (n = 25) or a quiet rest period (n = 25). The imagery manipulation had participants imagining performing an isometric handgrip task identical to the handgrip task they had performed on trial 1. Forearm muscle activation (EMG) was monitored throughout the experiment. Results showed the imagery group experienced greater negative changes in endurance performance from trial 1 to trial 2, F(1, 48) = 9.54, p = .003, d = 0.87, compared to controls. Furthermore, the imagery group showed greater increases in EMG amplitude at baseline, F(1, 48) = 6.64, p = .01, d = 0.73, and at 25%, F(1, 48) = 4.62, p = .03, d = 0.61, of the second endurance trial compared to control participants. The EMG results indicate greater motor unit recruitment by the imagery group despite task demands remaining constant. These findings imply that self-generated imagery of an effortful physical task activates or depletes neural substrates causing muscle fatigue and impaired muscular endurance. Findings have implications for the timing of imagery relative to endurance task performance, imagery content, and the type of imagery being performed.
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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 | 0.001 |
| 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".