Updating the remembered position of targets following passive lateral translation
Bibliographic record
Abstract
Spatial updating, the ability to track the egocentric position of surrounding objects during self-motion, is fundamental to navigating around the world. However, people make systematic errors when updating the position of objects after linear self-motion. To determine the source of these errors, we measured errors in remembered target position with or without passive lateral translations. Self-motion was presented both visually (simulated in virtual reality) and physically (on a 6-DOF motion platform). People underestimated targets' eccentricity in general even when just asked to remember them for a few seconds (5-7 seconds), with larger underestimations of more eccentric targets. We hypothesized that updating errors would depend on target eccentricity, which was manifested as errors depending not only on target eccentricity but also the observer's movement range. When updating the position of targets within the range of movement (such that their actual locations crossed the viewer's midline), people overestimated their change in position relative to their head/body compared to when judging the location of objects that were outside the range of movement and therefore did not cross the midline. We interpret these results as revealing changes in the efficacy of spatial updating depending on participant's perception of self-motion and the perceptual consequences for targets represented initially in one half of the visual field having to be reconstructed in the opposite hemifield.
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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 | 0.007 |
| 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 | 0.001 |
| 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".