Bibliographic record
Abstract
The floor of a room is the surface that is most likely to provide support. What is the contribution of the room's structural features to the perception of which surface this is? Using the Immersive Visual Environment at York (IVY) twelve subjects were placed in three simulated box-like rooms with no features. The rooms had a constant depth, a height-to-width ratio that varied from 1:1 to 1:3 and were presented at different roll orientations in an interleaved manner. The far wall was coloured purple and the other four visible surfaces were randomly assigned one of four colours on each trial and subjects indicated ‘the floor’ by pressing correspondingly coloured buttons on a game-pad. Each surface was described by its normal vector. The vectors of the chosen surfaces were summed to provide the average orientation of the perceived floor for each room orientation. We tested three models of how people determine the floor. Subjects might choose the surface (1) closest to orthogonal to gravity (flipping point of wall-to-floor at 45°), (2) closest to orthogonal to gravity on each side of the room's diagonal (flipping point when diagonal of room vertical), or (3) based on a weighting function dependent on each surface's length and orientation. Contrary to expectations, subjects did not necessarily choose the surface closest to orthogonal to gravity. The weighted-surface model best described the data with each surface being weighted by its relative length raised to the power 1.25 (r2 = 0.9).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".