A (nother) new way to measure up: the oblique derived subjective visual vertical
Bibliographic record
Abstract
The direction of “‘up’ can be measured by judging the orientation of a line against gravitational vertical: the subjective visual vertical (SVV). An alternative method uses the perceived identity of a character whose identity depends on its orientation (the Oriented CHAracter Recognition Test (Dyde et al., 2006: Exp Brain Res. 173: 612). OCHART derives the perceptual upright (PU) from the average of the two orientations at which the character is maximally ambiguous. PU and SVV are differently influenced by the directions of gravity, the body and visual cues. However SVV is derived from a point of maximum certainty whereas PU is taken from points of maximum uncertainty. Could different results arise from differing methodologies? Methods SVV was measured in two ways, both using constant stimuli. The conventional method determined an orientation of maximum certainty (“was the line tilted clockwise or counterclockwise relative to gravity?”). The new method found two points of maximum uncertainty (making it more comparable with OCHART) by asking whether a tilted line appeared closer to vertical or horizontal - the average of these two orientations indicating another SVV. For eight observers both SVVs were calculated whilst placed against a background rich in polarity cues which was tilted through 360° in 22.5° steps. Results Both SVVs showed a complex but clearly similar influence of the visual background's orientation and the methods showed indistinguishable results when the background was either fully upright or fully inverted - both being within 1° of gravitational vertical. However the SVV derived from the two points of maximum uncertainty showed a significantly larger influence of the background and higher intra-observer variances. Conclusions Both methods generated comparable results, but have differing sensitivities to tilting the visual background with the reliability of the estimate of the orientation of the probe relating to the magnitude of the background effect.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".