Exploring the different roles of fixations in an active visual search task
Bibliographic record
Abstract
Common visual search paradigms conducted on 2D screens with passive observation do not capture the full breadth and reality of eye and head movements used in real-world search. One is not presented with an image in real-world search; one must determine which images to acquire and in what order using relevant eye, head, and body movements. To investigate viewpoint selection and the role of fixation in active observation, an active visual search task was conducted in a controlled real-world environment. The scene was a physical 3x4m space furnished with tables and wire cages. Stimuli were miniature everyday objects, scattered in various orientations on the tables and cages. Observers moved freely, untethered, to search for a target object, and their eye and head movements, reaction time, and accuracy, were synchronized and measured over 12 trials each. Resulting eye and head movement data naturally seemed divided into “environment”, “look-at”, and “target look-at” fixations. “Look-at” refers to fixations viewing tables or cages with stimuli in view, “target look-at” refers to fixations viewing the target object, and “environment” covers all other fixations. Interestingly, subjects became more efficient at searching with successive target present trials, particularly in the number of look-at fixations. Target look-at fixations were also significantly longer than other fixations. Finally, we discovered that environment fixations often occur between look-at’s while a subject is navigating to a different location to continue their search. This suggests a clear distinction in the role between look-at fixations and environment fixations - one for searching through stimuli, and one for searching and navigating through the environment to achieve the next viewpoint. These results emphasize the importance of conducting search and other visual tasks in the real world, in order to capture the nuances of eye and head movement and strategies not otherwise found from a 2D paradigm.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".