Gaze as an Indicator of Input Recognition Errors
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
Input recognition errors are common in gesture- and touch-based recognition systems, and negatively affect user experience and performance. When errors occur, systems are unaware of them, but the user's gaze following an error may provide valuable cues for error detection. A study was conducted using a manual serial selection task to investigate whether gaze could be used to discriminate user-initiated selections from injected false positive selection errors. Logistic regression models of gaze dynamics could successfully identify injected selection errors as early as 50 milliseconds following a selection, with performance peaking at 550 milliseconds. A two-phase gaze pattern was observed in which users exhibited high gaze motion immediately following errors, and then decreased gaze motion as the error was noticed. Together, these results provide the first demonstration that gaze dynamics can be used to detect input recognition errors, and open new possibilities for systems that can assist with error recovery.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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