Greater sensitivity in detecting cross-modal asynchrony for body parts that are seen most often
Bibliographic record
Abstract
We have previously shown that people are more sensitive at detecting asynchrony between a self-generated movement and delayed visual feedback when the perspective of the movement matches the ‘natural view’ suggesting an internal, visual, canonical body representation (Hoover and Harris, 2011). Is there a similar variation in sensitivity for parts of the body that cannot be seen in a first-person perspective? To test this, participants made movements with their hands and head (viewing their face or the back of their head) under four viewing conditions: (1) the natural (or direct) view, (2) mirror-reversed, (3) inverted, and (4) inverted and mirror-reversed. Participants indicated which of two periods (one with a minimum delay, the other with an added delay of 33–264 ms) was delayed and their sensitivity to delay was calculated. A significant linear trend was found when comparing sensitivity to detect cross-modal asynchrony in the ‘natural’ or ‘direct’ view condition across body parts; where sensitivity was greatest when viewing body parts seen most often (hands), intermediary for viewing body parts that are seen only indirectly (moving head while viewing face), and least for viewing body parts that are never seen at all (moving head while viewing back of the head). Further, dependency on viewpoint was most evident for body parts that are seen most often or indirectly, but not for body parts that are never seen. Results are discussed in terms of a visual representation of 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".