CALM: Cognitive Assessment using Light-insensitive Model
Bibliographic record
Abstract
The demand for cognitive load assessment with low-cost easy-to-use equipment is increasing, with applications ranging from safety-critical industries to entertainment. Though pupillometry is an attractive solution for cognitive load estimation in such applications, its sensitivity to light makes it less robust under varying lighting conditions. Multimodal data acquisition provides a viable alternative, where pupillometry is combined with electrocardiography (ECG) or electroencephalography (EEG). In this work, we study the sensitivity of pupillometry-based cognitive load estimation to light. By collecting heart rate variability (HRV) data during the same experimental sessions, we analyze how the multimodal data reduces this sensitivity and increases robustness to light conditions. In addition to this, we compared the performance in multimodal settings using the HRV data obtained from low-cost fitness-grade equipment to that from clinical-grade equipment by synchronously collecting data from both devices for all task conditions. Our results indicate that multimodal data improves the robustness of cognitive load estimation under changes in light conditions and improves the accuracy by more than 20% points over assessment based on pupillometry alone. In addition to that, the fitness grade device is observed to be a potential alternative to the clinical grade one, even in controlled laboratory settings.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".