Web-based eye-tracking for remote cognitive assessments: The anti-saccade task as a case study
Bibliographic record
Abstract
Abstract Over the last years, several developments of remote webcam-based eye tracking (ET) prototypes have emerged, testing their feasibility and potential for web-based experiments. This growing interest is mainly explained by the possibility to perform tasks remotely, which allows the study of larger and hard-to-reach populations and potential applications in telemedicine. Nevertheless, a decrease in the quality of the camera and a noisier environment bring new implementation challenges. In this study, we present a new prototype of remote webcam-based ET. First, we introduced improvements to the state-of-the-art remote ET prototypes for cognitive and clinical tasks, e.g. without the necessity of constant mouse interactions. Second, we assessed its spatiotemporal resolution and its reliability within an experiment. Third, we ran a classical experiment, the anti-saccade task, to assess its functionality and limitations. This cognitive test compares horizontal eye movements toward (pro-saccades) or away from (anti-saccades) a target, as a measure of inhibitory control. Our results replicated previous findings obtained with high-quality laboratory ETs. Briefly, higher error rates in anti-saccades compared to pro-saccades were observed, and incorrect responses presented faster reaction times. Our web-ET prototype showed a stable calibration over time and performed well in a classic cognitive experiment. Finally, we discussed the potential of this prototype for clinical applications and its limitations for experimental use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".