"Wiggle wiggle, little finger": The impact of eye movements on manual motor overflow during the imagination of a Fitts' aiming task
Bibliographic record
Abstract
When individuals execute a reciprocal aiming task, eye movements tend to precede the hand movements to enhance error correction and accuracy. This coupling also occurs when imagining the motor task. In fact, the eye movements still emerge even when the hand remains relatively still during the imagination of the manual aiming movements. The eye movements may be an expression of motor overflow that arises during the imagination process. Recent work in our lab suggests that suppressing eye movements decreases imagination accuracy. The activation of the internal representation and motor overflow during imagination can also be measured through small accompanying involuntary movements of the finger. The purpose of this study was to understand how the suppression of eye movements affects the manual motor overflow that emerges during the imagination of a Fitts' reciprocal aiming task. Participants first imagined, then executed the task in two conditions: 1) no instructions on eye movement (no-fixation); and, 2) instructions to fixate their eyes on a central target (fixation). During imagination, participants indicated the start and end of imagination by lifting their finger, holding it up, and bringing it back down once they were finished imagining the task. The manual motor overflow was measured as the movement of the participant's finger while it was held in the air. Although Fitts' law still emerged within conditions, the manual motor overflow was similar across the fixation and no fixation conditions. Thus, manual motor overflow does not seem to be influenced by eye movements during action imagination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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".