Bibliographic record
Abstract
The current study investigated which brain areas are involved in allocentric (scene-based) as compared to egocentric (viewer-based) coding for visually guided hand movements. Using an fMRI block-design, we scanned the brains of 14 subjects while they performed hand movements in either egocentric or allocentric tasks. Using a whole brain analysis we found that performance in both tasks elicited reliable BOLD signals in a sensorimotor network encompassing occipito-temporal, parietal and frontal cortices and the cerebellum. Contrasting BOLD between egocentric and allocentric tasks revealed that the allocentric task led to an increase in BOLD signals in portions of the sensorimotor network, in particular the fundus of the left intraparietal sulcus (IPS), posterior right IPS and bilateral dorsal premotor cortex (PMd). The comparison also showed that the allocentric task led to an increase in BOLD in ventral visual stream areas in lateral occipital cortex (LO) and the fusiform gyrus (FFG) that were separate from the sensorimotor network. We did not find activity specific for the egocentric task. The finding that ventral-occipital areas were recruited during the allocentric, but not the egocentric task, is consistent with neuropsychological data that link the integrity of these areas to successful performance in allocentric movement tasks. The data therefore suggest that areas LO and FFG are essential for the processing of visual information in a scene-based reference frame during visually guided movements. In contrast, activity in the IPS has been linked to the representation of magnitude and visual-spatial processing, and activity in PMd has been linked to the representation and selection of movement parameters. Thus an increase in activity in those areas during the allocentric task might suggest that, compared to the egocentric task, the allocentric task places a higher load on mechanisms that transform visual information about extent and spatial layout into movement parameters.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".