Body and gaze centered coding of touch locations during a dynamic task
Bibliographic record
Abstract
We have previously reported that head position affects the perceived location of touch differently depending on the dynamics of the task the subject is involved in. When touch was delivered and responses were made with head rotated touch location shifted in the opposite direction to the head position, consistent with body-centered coding. When touch was delivered with head rotated but response was made with head centered touch shifted in the same direction as the head, consistent with gaze-centered coding. Here we tested whether moving the head in-between touch and response would modulate the effects of head position on touch location. Each trial consisted of three periods, in the first arrows and LEDs guided the subject to a randomly chosen head orientation (90° left, right, or center) and a vibration stimulus was delivered. Next, they were either guided to turn their head or to remain in the same location. In the final period they again were guided to turn or to remain in the same location before reporting the perceived location of the touch on a visual scale using a mouse and computer screen. Reported touch location was shifted in the opposite direction of head orientation during touch presentation regardless of the orientation during response or whether a movement was made before the response. The size of the effect was much reduced compared to our previous results. These results are consistent with touch location being coded in both a gaze centered and body centered reference frame during dynamic conditions.
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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 | 0.000 | 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) | 0.002 | 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".