When is stereopsis useful in visual search?
Bibliographic record
Abstract
Does stereoscopic information improve visual search? We know that attention can be guided efficiently by stereopsis, for example, to the near target among far distractors (Nakayama and Silverman, 1986), but, when searching through a real scene, does it help if that scene is presented stereoscopically? Certainly, scenes appear to be more vividly real in 3D. However, we present three experiments in which the addition of stereo did not alter scene search very much. In Experiment 1, 12 observers searched twice for 10 target objects in each of 18 photographic scenes (a ‘repeated’ search task). Note that observers were not cued to search for a target at a specific depth, stereopsis simply added to the vividness of the scene. Reaction time and accuracy did not differ significantly in stereoscopic and monoscopic conditions. In Experiment 2, using similar images, we found no differences between 2D and 3D conditions on time to first fixation on the target or on the average length of saccades. However, gaze durations were significantly shorter in 3D scenes. Since gaze durations are typically taken to measure processing time, this may suggest that it was easier to disambiguate surfaces and/or objects in 3D, although this advantage did not translate to benefits in search times. In a final experiment, we reduced the stimulus array to a set of colored rendered objects distributed in depth against a plain background. In this task, the addition of stereo produced shorter reaction times, even though stereo information was not predictive of target location. Our real scenes may have contained such a rich array of cues to target location that the addition of stereo may not have contributed much additional information. It may be in more difficult searches, including more challenging real world tasks, that stereopsis will be an asset. Meeting abstract presented at VSS 2015
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".