Title: Where’s Waldo?: Analyzing visual search behaviour with a web-based eye tracking system
Bibliographic record
Abstract
In the current study, we tested the temporal and spatial properties of a new web-based eye tracking program called Gazer, developed at the University of Victoria. Using the Gazer program, we tracked the eye movements of participants during a Where's Waldo visual search task. On each trial, participants were presented with a Where’s Waldo scene and asked to identify the location of the Waldo target. Once they located Waldo, participants terminated the search by pressing the space bar. They were then shown a grid of 3 x 3 squares and asked to indicate the corresponding square in which Waldo appeared. During the visual search, gaze locations were recorded at a sampling rate of 30 Hz and timing was locked to the space bar response. To correlate gaze time with search behaviour, we calculated dwell time as the proportion of gaze spent on the on-target square versus time spent on the off-target squares. Dwell time was analyzed 1000ms prior to response and our results showed participants spent significantly more time looking at the on-target square than off-target squares (p < .001). At a finer grain of temporal and spatial analysis, we computed the average Euclidean distance between the Gazer coordinates and the Waldo target in 20ms time bins. This analysis revealed that participants began to move their gaze toward Waldo’s position approximately 3000ms before their space bar response. At the level of individual differences, we found a robust negative correlation between visual search speed and accuracy where participants who were very fast at finding the Waldo target were also very accurate. In conclusion, we believe that the Gazer program is a viable system for conducting remote eye tracking research. Our findings indicate Gazer has sufficient temporal and spatial resolution to investigate the relationship between eye movements and visual search performance.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".