Bibliographic record
Abstract
Most visual search studies use a 2D, passive observation task, where subjects search through artificial stimuli on a screen. In contrast, real world search involves physical 3D scenes and searchers who choose relevant scene viewpoints as the search proceeds. Searchers employ eye, head, and body movements to investigate the scene; they are active observers. To investigate viewpoint selection in active observation during 3D search, an active search task was conducted in a controlled real-world environment, a 3x4m space furnished with tables and wire cage shelving acting as surfaces to place the stimuli. Stimuli were miniature everyday objects, scattered in various orientations on the tables and cages. Targets were placed in upright, sideways, face-up, or diagonally tilted positions, but the target image probe was always presented in an upright orientation. Observers moved freely, untethered, and their eye and head movements, reaction time, and accuracy, were synchronized and measured over 12 trials each. Results indicate that similar to 2D search tasks, target-absent trials take longer than present trials and require more fixations and head travel. Interestingly, efficiency in these metrics was found to increase over time only in target present trials, not in absent trials. Collected eye and head movement data further revealed head tilts for subjects to match canonical orientations of non-upright objects. Indeed, targets placed in non-canonical orientations required more fixations before subjects would confirm them as present. Subjects were also found to crouch in order to fixate on objects placed at lower levels (such as the table surface, which was approximately 70cm high). Our results provide novel analyses on eye and head movement metrics during search in an active observation environment, demonstrating the important nuances of movements that can be induced by requiring viewpoint selection to complete a task.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".