The effects of movement direction on movement time and accuracy during manual control tasks
Bibliographic record
Abstract
The two frames of reference used to position the body in space are termed egocentric, where location and orientation are determined with respect to the individual, and allocentric, where location and orientation are based on the environment (Dassonville, Schlag & Schlag-Rey, 1995). Egocentric movements are used most often within peripersonal space, such as reaching. Kelly & Weaton (2013) showed that tool manipulation is completed more successfully in egocentric space than allocentric space, regardless of hand preference. The current study investigated whether handedness and movement direction influence accuracy and movement time on a manual task without tools. Forty-two young adults (29 right-handers, 13 left-handers) completed the Waterloo Handedness Questionnaire and a tablet-tracing task. An ACER Aspire Switch tablet was placed on a table in front of the participant, who then traced a track slightly larger than finger-width either going toward (egocentric) or away from (allocentric) the body. Results showed that movement time was significantly faster for egocentric trials, and the preferred hand performed significantly faster than the non-preferred hand, regardless of the task. Additionally, a 3-way interaction was found between direction, hand preference, and the hand used, where the preferred hand of right-handers was significantly faster making movements toward the body than the non-preferred hand. Interesting, left-handers showed no difference between the preferred and non-preferred hands. No effect of accuracy was found for movement type or hand preference, suggesting that participants sacrificed speed for accuracy. These results will be discussed in light of current theories regarding hand preference.
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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 | 0.014 |
| 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) | 0.002 | 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".