Bibliographic record
Abstract
When reaching for an object on a crowded table, visual information about other objects’ positions provides important contextual information to help avoid bumps and spills. Although the addition of visual context has been shown to increase accuracy for movements to visual targets, less is known about its role in guiding movements to the body, such as bringing food to the mouth. When reaching to positions on the body, also known as somatosensory targets, vision is hypothesized to play less of a role than other sources of sensory information (i.e., touch or proprioception). The goal of my research project was to examine the influence of visual context on movements to body positions. Seventeen participants performed reaches to visual targets and somatosensory targets in the presence of contextual cues or in a dark environment while fixed on a central location. Reaction time and error in both the movement direction and movement amplitude axes were compared across context and target conditions. Although there were no differences in reaching errors, reaction times were longer for visual targets when visual contextual cues were present. This suggests that visual contextual information resulted in greater planning for movements to visual targets as compared to movements for somatosensory targets. This dissociation highlights differences in how the brain processes contextual information when planning movements to external space compared with movements directed toward the body.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".