A Distraction Can Impair or Enhance Motor Performance
Bibliographic record
Abstract
Humans have a prodigious capacity to perform multiple tasks simultaneously. Being distracted while, for example, performing a complex motor skill adds complexity to a task and thus leads to a performance impairment. Yet, it may not be just the presence or absence of a distraction that affects motor performance. Instead, the characteristics of the distraction may play a critical role in affecting human motor performance. Here, we show that performance of a motor sequence can be substantially enhanced by simultaneously learning an independent color sequence. In contrast, performance of the same motor sequence was impaired by concurrently counting the number of red cues that were in the color sequence. The color and motor sequences had different lengths (10 vs 12 items), different numbers of elements (five vs four elements), and different temporal patterns (randomly intermittent vs continuous) and thus were independent of one another. These observations show that distracting information does not always impair motor performance, and so is not a sufficient explanation for the impaired performance. Instead, the influence that a distraction exerts upon performance is mediated by the type of processes engaged: when similar core processes are engaged, motor performance is enhanced, whereas when very different processes are engaged (i.e., counting and sequence performance), performance is impaired. Thus, these observations deepen our understanding of how a distraction, depending on its characteristics, can either impair or enhance performance and may offer novel approaches to optimizing human cognition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".