Allocentric coding of reach targets in naturalistic visual scenes
Bibliographic record
Abstract
Goal-directed reaching movements rely on both egocentric and allocentric target representations. So far, it is widely unclear which factors determine the use of objects as allocentric cues for reaching. In a series of experiments we asked participants to encode object arrangements in a naturalistic visual scene presented either on a computer screen or in a virtual 3D environment. After a brief delay, a test scene reappeared with one object missing (= reach target) and other objects systematically shifted horizontally or in depth. After the test scene vanished, participants had to reach towards the remembered location of the missing target on a grey screen. On the basis of reaching errors, we quantified to which extend object shifts and thus, allocentric cues, influenced reaching behavior. We found that reaching errors systematically varied with horizontal object displacements, but only when the shifted objects were task-relevant, i.e. the shifted objects served as potential reach targets. This effect increased with the number of objects shifted in the scene and was more pronounced when the object shifts were spatially coherent. The results were similar for 2D and virtual 3D scenes. However, object shifts in depth led to a weaker and more variable influence on reaching and depended on the spatial distance of the reach target to the observer. Our results demonstrate that task relevance and image coherence are important, interacting factors which determine the integration of allocentric information for goal-directed reaching movements in naturalistic visual scenes. Meeting abstract presented at VSS 2016
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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 | 0.002 |
| 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 | 0.001 |
| 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".