Using your neck muscles to reach for a target
Bibliographic record
Abstract
Reaching for a implies transforming a retinal representation of the into a body-centered frame of reference. Once the movement is initiated, it is not clear if humans need to refer again to an eye- and/or head-centered frame of reference. In this study, we perturbed these transformations using neck vibration (NV). Nineteen (19) participants performed discrete reaches towards a virtual target. The perceived influence of NV was first assessed in the dark with a single target. The main conditions involved NV applied prior to (NV-Planning) or/and during the reaching movement (NV-Online/NV-Complete) or not at all (NV-None). These 4 conditions were randomly presented 20 times each. Participants also performed 20 NV-None trials before and after the main experiment. The main dependent variable was the lateral deviation of the reaching finger during the trajectory. Following the initial analysis, two groups of participants were identifiable. The group (i.e., head rotation bias = leftward corrections) and the target group (i.e., shift bias = rightward corrections). Analysis of the main experimental conditions revealed that only the group revealed larger—and super-additive—lateral endpoint biases in the NV-Complete compared to the NV-None condition. These results suggest that head- to body- centered transformations take place when preparing and executing a reaching movement, especially if head motion can be sensed. Acknowledgments: This research was supported by NSERC
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".