Object-based Neglect Varies with Egocentric Position
Bibliographic record
Abstract
Different reference frames have been identified to influence neglect behavior. In particular, neglect has been demonstrated to be related to the contralesional side of the subject's body (egocentric reference frames) as well as to the contralesional side of individual objects irrespective of their position to the patient (object-based reference frame). There has been discussion whether this distinction separates neglect into body- and object-based forms. The present experiment aimed to prove possible interactions between object-based and egocentric aspects in spatial neglect. Neglect patients' eye and head movements were recorded while they explored objects at five egocentric positions along the horizontal dimension of space. The patients showed both egocentric as well as object-based behavior. Most interestingly, data analysis revealed that object-based neglect varied with egocentric position. Although the neglect of the objects' left side was strong at contralesional egocentric positions, it ameliorated at more ipsilesional egocentric positions of the objects. The patients showed steep, ramp-shaped patterns of exploration for objects located on the far contralesional side and a broadening of these patterns as the locations of the objects shifted more to the ipsilesional side. The data fitted well with the saliency curves predicted by a model of space representation, which suggests that visual input is represented in two modes simultaneously: in veridical egocentric coordinates and in within-object coordinates.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".