The use of visual information during a visual saccade for the control of a goal-directed upper limb movement
Bibliographic record
Abstract
There is reduced visual sensitivity during visual saccades (i.e., saccadic suppression: see Bedell, 2001; Irwin, 2002; Michels, 2004) yet small target displacements presented during a saccade lead to limb trajectory corrections without perception of the target jump (Goodale et al. 1986). As such, it could be hypothesized that visual information is unconsciously gathered during the saccade to update the limb trajectory. We designed a study to test this hypothesis by manipulating visual feedback during saccades to a single- and double-step target presentation for a manual aiming task. The experimental task involved single-step trials to a 16° and 23° target (i.e., 10 cm and 15 cm, respectively) and the target was displaced further at peak eye velocity by 2.2° each on 50% of the trials. Eye movements were monitored using EOG and limb movements were captured using an OptoTrak Certus, both sampling at 400 Hz. Liquid crystal goggles were used to manipulate vision only during the primary visual saccade in real-time using five (5) conditions: full vision (FV), vision during high (V_High) or low eye velocity (V_Low), and vision early (V_Early) or late (V_Late) in the saccade. As expected, there was a main effect for Target Step (single, double) on limb movement endpoint, aiming movement time, and aiming movement symmetry. However, this was true across all vision conditions indicating that amendments to the limb trajectory following an unperceived target jump do not appear to be based on information gathered during the primary visual saccade. Interestingly, both the FV and V_Low conditions led to higher maximum limb velocity than in the other experimental conditions (V_High, V_Early, V_Late). As such, some visual information is gathered for manual aiming planning processes during low saccadic velocity.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".