Top-down and bottom-up aspects of active search in a real-world environment.
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.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".