The Effect of Contextual Cues on Goal-Directed Reaches to Multisensory Targets
Bibliographic record
Abstract
When reaching for an object on a crowded table, visual information about the position of other objects should contribute to the movement plan to avoid spills and bumps. Previous research has found that movements to visual targets were more accurate when non-target visual information (e.g., contextual cues) were present in the reaching environment compared to when reaching in a dark environment. Although visual context plays a role in movements to visual targets, it is unknown if this information is also used when making movements to somatosensory targets (e.g., body positions). The goal of this study is to determine if the presence of visual contextual cues also affects movements to somatosensory targets. Eleven neurologically-healthy participants performed upper-limb reaches to unseen somatosensory targets and seen visual targets with and without contextual cues. To assess the impact of contextual information, radial error, angular error and temporal kinematic variables (e.g. time to peak velocity) were computed. Our results indicated that the presence of contextual cues did reduce radial error for movements to both target modalities. These results provide evidence that contextual information may also contribute to movements to somatosensory targets, indicating that external visual cues could play a role in how humans localize body position.
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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 | 0.000 | 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".