Top-down and bottom-up aspects of active search in a real-world environment.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visual search has been studied intensively in the labouratory, but lab search often differs from search in the real world in many respects. Here, we used a mobile eye tracker to record the gaze of participants engaged in a realistic, active search task. Participants were asked to walk into a mailroom and locate a target mailbox among many similar mailboxes. This procedure allowed control of bottom-up cues (by making the target mailbox more salient; Experiment 1) and top-down instructions (by informing participants about the cue; Experiment 2). The bottom-up salience of the target had no effect on the overall time taken to search for the target, although the salient target was more likely to be fixated and found once it was within the central visual field. Top-down knowledge of target appearance had a larger effect, reducing the need for multiple head and body movements, and meaning that the target was fixated earlier and from further away. Although there remains much to be discovered in complex real-world search, this study demonstrates that principles from visual search in the labouratory influence gaze in natural behaviour, and provides a bridge between these labouratory studies and research examining vision in natural tasks.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it