The magnitude of binocular disparity modulates search time for targets defined by a conjunction of depth and colour.
Bibliographic record
Abstract
Nakayama and Silverman (1986) proposed that, when searching for a target defined by a conjunction of color and stereoscopic depth, observers partition 3D space into separate depth planes and then rapidly search each such plane in turn, thereby turning a conjunctive search into a "feature" search. In their study, they found, consistent with their hypothesis, shallow search slopes when searching depth planes separated by large binocular disparities. Here, the authors investigated whether the search slope depends upon the extent of the stereoscopically induced separation between the planes to be searched (i.e., upon the magnitude of the binocular disparity. The obtained slope shows that (1) a rapid search only occurs with disparities greater than 6 min of arc, a value that vastly exceeds the stereo threshold, and that (2) the steepness of this slope increases in a major way at lower disparities. The ability to implement the search mode envisaged by Nakayama and Silverman is thus clearly limited to large disparities; less efficient search strategies are mandated by lower disparity values, as under such conditions items from one depth plane may be more likely to "intrude" upon the other.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".