EMICS'20: Eye Movements as an Interface to Cognitive State
Bibliographic record
Abstract
Eye movement recording has been extensively used in HCI and offers the possibility to understand how information is perceived and processed by users. Hardware developments provide the ubiquitous accessibility of eye recording, allowing eye movements to enter common usage as a control modality. Recent A.I. developments provide powerful computational means to make predictions about the user. However, the connection between eye movements and cognitive state has been largely under-exploited in HCI. Despite the rich literature in psychology, a deeper understanding of its usability in practice is still required. This EMICS SIG will provide an opportunity to discuss possible application scenarios and HCI interfaces to infer users' mental state from eye movements. It will bring together researchers across disciplines with the goal of expanding shared knowledge, discussing innovative research directions and methods, fostering future collaborations around the use of eye movements as an interface to cognitive state, and providing a solid foundation for an EMICS workshop at CHI 2021.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".