Updating Visual Memory Across Eye Movements for Ocular and Arm Motor Control
Bibliographic record
Abstract
Remembered object locations are stored in an eye-fixed reference frame, so that every time the eyes move, spatial representations must be updated for the arm-motor system to reflect the target's new relative position. To date, studies have not investigated how the brain updates these spatial representations during other types of eye movements, such as smooth-pursuit. Further, it is unclear what information is used in spatial updating. To address these questions we investigated whether remembered locations of pointing targets are updated following smooth-pursuit eye movements, as they are following saccades, and also investigated the role of visual information in estimating eye-movement amplitude for updating spatial memory. Misestimates of eye-movement amplitude were induced when participants visually tracked stimuli presented with a background that moved in either the same or opposite direction of the eye before pointing or looking back to the remembered target location. We found that gaze-dependent pointing errors were similar following saccades and smooth-pursuit and that incongruent background motion did result in a misestimate of eye-movement amplitude. However, the background motion had no effect on spatial updating for pointing, but did when subjects made a return saccade, suggesting that the oculomotor and arm-motor systems may rely on different sources of information for spatial updating.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".