On the allocation of attention in stereoscopic displays
Bibliographic record
Abstract
It has been shown that disparity can be used as a token for visual search (possibly pre-attentively). However, there has been no systematic investigation of the distribution of attentional resources across the disparity dimension. Here we evaluated whether position in depth, relative to the screen plane, influences attentional allocation. We conducted two experiments using the same visual search task but with different stimuli. In the first experiment, stimuli consisted of four simple geometric shapes and in the second experiment the stimuli consisted of four orientated lines enclosed by a circle. In both cases, the stimuli were arranged in an annulus about a central fixation marker. On each trial, observers indicated whether the target was present or not within the annular array. The distractor number was varied randomly on each trial (2, 4, 6, or 8) and the target was present on half of the trials. On all trials, one element had a disparity offset by 10 arcmin relative to the others. On half of target present trials the target was in the disparate location, on the remainder it was presented at the distractor disparity. Trials were further subdivided such that on equal numbers of trials the disparate element was on or off the plane of the screen. We measured search time, analysing only trials on which observers responded correctly. Both experiments showed that when the target was the disparate item, reaction time was significantly faster when the target was off the screen plane compared to at the screen plane. This was true for a range of crossed and uncrossed disparities. We conclude that there exists a selective attentional bias for stimuli lying off the screen plane. These data are the first evidence of a disparity-selective attentional bias that is not mediated by relative disparity. Meeting abstract presented at VSS 2012
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".