The effect of stereoscopic viewing in a word‐search task with a layered background
Bibliographic record
Abstract
Abstract— The benefits of stereoscopic viewing were explored in searching in words superimposed over a background. In the first experiment, eight participants searched for text in a normal 2‐D display, a 3‐D display using a parallax barrier, and a darkened 2‐D display of equivalent brightness to the 3‐D display. Word‐search performance was significantly faster for the bright 2‐D display vs. the 3‐D display, but when brightness was controlled, performance on the 3‐D display was better relative to the 2‐D (dim) display. In a second experiment, the effect of floating text vs. sinking background disparity was assessed across four background conditions. Twenty participants saw only the floating‐text (FT) condition and 20 participants saw only the sinking‐background (SB) condition. Performance of the SB group was significantly better than that of FT group, and the advantage of SB disparity was greater with the more‐complex backgrounds. Thus, when a parallax‐barrier 3‐D display is used to view text or other figural information overlaid on a background, it is proposed that the layer of primary interest (foreground) should be displayed with zero disparity (on the physical display surface) with the secondary layer (background) appearing to be sunk beneath that surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".