Follow the Dot: Do we have implicit awareness of our own eye movements?
Bibliographic record
Abstract
When asked where they have previously looked, people rarely report their visual behavior correctly. Similarly, people can seldom identify their own eye movements in recognition tasks, despite the large individual differences in gaze metrics previously established in literature. However, most tasks generally probe explicit awareness or memory of one’s own eye movements. It is unclear whether poor awareness extends to implicit awareness as well, which may speak to individual differences in gaze behaviour. To investigate this question, we designed a tracking paradigm that involved two tasks. First, participants (n=7) completed a classic visual search task while their eye movements were recorded. Next, participants completed a tracking task in which they were instructed to follow a moving red dot on the screen that replayed either their own previously recorded gaze position or that of another participant. During the replay portion, the dot was visible for 50% of the time, randomly disappearing for brief segments of time, and participants were instructed to move their eyes to where they believed the dot would appear next. Furthermore, replayed eye movements were either superimposed on the same stimulus array displayed during the search task, or on a plain grey background. Tracking accuracy was measured by calculating the cross-correlation between the previously recorded gaze position and the tracked positions. Our results show that, on average, people were not significantly better at tracking their own eye movements versus others (p = .20) and that people were not better at tracking the replayed eye movements when it was superimposed on the same stimulus array from the search task (p = .15). While tracking accuracy across conditions was overall very high (Fisher Z range: 0.85-1.11), our results suggest that poor awareness of one’s eye movements may extend to both explicit and implicit measures.
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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 | 0.002 | 0.018 |
| 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 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".