Bibliographic record
Abstract
Active visual search in the real world has not been investigated in great detail. Whilst the visual search paradigm has been widely used since its popularization, most studies have not moved beyond the 2D, passive version of the task, where immobile subjects search through artificial stimuli presented on a screen. However, how much these studies’ results can be extrapolated to the real world is unclear, since targets may not be immediately visible, forcing observers to select viewpoints in order to search around occluding objects. To investigate whether the classic results hold true in the real world, an active visual search task was conducted in the 3D PESAO setup environment (Solbach & Tsotsos, 2020), with a 3x4m search space furnished with tables and wire cages. Observers moved freely, untethered, and their eye and head movements, reaction time, and accuracy, were precisely synchronized and measured over 12 trials each. The experimental stimuli were miniature everyday objects, scattered semi-randomly on the tables and cages, and observers had to navigate around them to conduct the search. Trial difficulty was manipulated by changing the number of shared features between the target object and distractors, and set size varied between 30-60 objects. Half the present targets were occluded from the starting viewpoint. Results indicate that similar to 2D search tasks, target absent trials take longer than present trials and require more fixations and head travel. Surprisingly, target occlusion has no significant effect on reaction time, and only marginal effects on number of fixations and distance traveled. Objects with non-upright orientations induced motions such as head tilting and crouching before declaring targets as present or absent. Our results provide a novel view at how humans perform unconstrained visual search in a 3D world and help lay foundations for our general ability for visuospatial problem solving.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".