Unconscious Frustration: Dynamically Assessing User Experience using Eye and Mouse Tracking
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
Eye-tracking has become easier to deploy in user experience (UX) studies to get a sense of where users attend to during interactions. Additionally, mouse tracking grants insights into the cognition driving the user's behaviours and end goals, as can measuring the coordination between the eye and mouse-cursor. We created a menu navigation task based on a popular video game to assess two populations: a local cohort, and a remote cohort. We used two different eye trackers (monitor-mounted hardware, and a webcam-based algorithm; local used both simultaneously, remote used webcam only) with concurrent mouse tracking to detect friction in the UX. We found that both eye trackers had similar performance and revealed a previously undetected friction point. We argue this friction point was only detected because of the use of quantified, coordinated unconscious behaviours (eye and hand movements). The methods demonstrated are easily integrated into current UX studies with minimal cost.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it