Where’s Waldo? Exploring Gaze Strategy in a Visual Search Task Online and In-Person
Bibliographic record
Abstract
In this study, we employed a ‘Where’s Waldo’ visual search task to compare the eye movement behaviours of participants in-person and online. Participants were presented with a “Where’s Waldo” scene and their task was to find the ‘Waldo’ target in the scene. They pressed the spacebar to indicate when they had located the target then used the mouse to click on the target’s location. Eye-tracking data was recorded using the online Gazer (n = 28) and the in-person Eyelink 1000 (n = 9). An initial comparison of the results indicates the online participants were less accurate at identifying the target’s location (online: 79% accuracy v. in-lab: 90%). Online participants were also significantly slower to press the space bar to indicate target detection (online: 9555 ms v. in-lab: 7208 ms). However, analysis of eye-tracking data showed an opposite pattern of eye movement behaviours where the online participant’s first fixations on the target location occurred 800ms sooner than the first fixations of the in-lab participants (online: 5604 ms v. in-lab: 6406 ms). Therefore, online participants were fixating on the target more quickly, but were slower to indicate detection. Region of Interest (ROI) analysis indicated that in-lab participants averaged 1.06 fixations on the area with the Waldo target whereas online participants averaged 4.05 fixations. In other words, the in-person participants appeared to respond after a single fixation on the target, but the online participants made several saccades to and away from the target before responding. Online participants may be demonstrating a more conservative strategy in the visual search task by only providing the detection response after confirming target presence in multiple passes. Overall, we recorded compelling eye-tracking and behavioural data both in-person and online and provide evidence that remote participants may use an altered gaze strategy in a visual search task.
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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 | 0.001 | 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 | 0.000 |
| 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".