{
  "id": 8401703,
  "title": "Time-Series Foundation Models for Cognitive Workload Classification using Eye-Tracking Data",
  "url": "https://urgent.news/2026/09/18/time-series-foundation-models-for-cognitive-workload-classification",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-18T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.13.751282v1?rss=1"
  },
  "original_language": "en",
  "account": "The article explores the use of time-series foundation models (TSFMs) to classify cognitive workload (CWL) using eye-tracking data. CWL assessment is crucial for various applications, including driver fatigue monitoring, pilot attention tracking, surgeon workload measurement during complex procedures, and astronaut cognitive fatigue monitoring during long-duration missions. However, eye-tracking datasets are typically small, which limits the generalizability of AI models. TSFMs, which are pretrained on extensive data, can be fine-tuned with limited task-specific data, potentially overcoming this limitation.\n\nThe study compares two TSFMs, MOMENT and Moirai, against convolutional neural network (CNN), fully connected neural network (FFN), and long short-term memory (LSTM) baselines on two publicly available eye-tracking datasets. Subject-level five-fold cross-validation was employed, where data from each test subject was held out during training. The performance was evaluated using accuracy, area under the curve (AUC), and F1-score, with 95% confidence intervals.\n\nOn the class-balanced EM-COGLOAD dataset, pretrained TSFMs demonstrated significant generalization to unseen subjects, with Moirai achieving a remarkable 0.918 AUC and MOMENT reaching 0.882 AUC. In contrast, task-specific baselines remained around 0.70 AUC. In the class-imbalanced COLET dataset, while the performance of all models decreased, MOMENT showed the highest robustness, attaining a 0.670 AUC. Moreover, both TSFMs exhibited greater stability when evaluated across held-out subjects, suggesting that pretrained representations generalize more consistently across individuals in data-scarce eye-tracking scenarios.",
  "summary": "Cognitive workload (CWL) assessment is relevant to a range of applications, such as monitoring driver fatigue and pilot attention, surgeon workload during complex procedures, and astronaut cognitive fatigue during long-duration missions. Eye-tracking datasets are generally small, which hinders the generalizability of the AI models. Time-series foundation models (TSFMs) have shown promise in…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}