Allocentric information is used for memory-guided reaching in depth: A virtual reality study
Bibliographic record
Abstract
Previous research has demonstrated that humans use allocentric information when reaching to remembered visual targets, but most of the studies are limited to 2D space. Here, we study allocentric coding of memorized reach targets in 3D virtual reality. In particular, we investigated the use of allocentric information for memory-guided reaching in depth and the role of binocular and monocular (object size) depth cues for coding object locations in 3D space. To this end, we presented a scene with objects on a table which were located at different distances from the observer and served as reach targets or allocentric cues. After free visual exploration of this scene and a short delay the scene reappeared, but with one object missing (=reach target). In addition, the remaining objects were shifted horizontally or in depth. When objects were shifted in depth, we also independently manipulated object size by either magnifying or reducing their size. After the scene vanished, participants reached to the remembered target location on the blank table. Reaching endpoints deviated systematically in the direction of object shifts, similar to our previous results from 2D presentations. This deviation was stronger for object shifts in depth than in the horizontal plane and independent of observer-target-distance. Reaching endpoints systematically varied with changes in object size. Our results suggest that allocentric information is used for coding targets for memory-guided reaching in depth. Thereby, retinal disparity and vergence as well as object size provide important binocular and monocular depth cues.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".