Influence of handedness on Fitts’ relationship when movements are imagined and perceived
Bibliographic record
Abstract
Humans can perform a wide variety of action. These actions can also be imagined or perceived. The actions performed by the dominant hand are often completed more quickly and accurately than actions with non-dominant hand. The present study investigated if the Fitts’ relationship differs between the two hands when executing, imagining or perceiving an action. Because people perform actions more often with the dominant hand than the non-dominant hand, imagination and perception of dominant hand actions may be more accurate than non-dominant hand actions. Right-hand dominant participants were asked to first execute and then imagine reciprocal aiming movements between two targets of varying widths and amplitudes. Then, participants were presented with images of hands performing the reciprocal aiming movements at different apparent movement times (MT) and participants were asked to verbally state if movement observed was possible to perform at that MT. Preliminary analyses revealed that the Fitts’ relationship is consistent across both hands regardless of the execution or the imagination conditions. MTs are lower for the right-hand compared to the left-hand. Analysis of the perception task revealed similar findings – the shortest perceived possible MTs for the left-hand were longer than for the right-hand. Overall, Fitt’s relationship emerged in both left-and right-hand execution, imagination, and perception. The imagination and perception of these movements were consistent with execution, with the right-hand yielding shorter MTs compared to the left-hand.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".