Greater sensitivity in detecting cross-modal asynchrony for body parts that are seen most often
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it