Predicting operator mental workload using a time-based algorithm
Bibliographic record
Abstract
Existing workload algorithms based on the Multiple Resources Theory (MRT) provide an effective approach for diagnosing operator overload caused by interference among concurrent tasks, however, their ability to handle overload in single task conditions is limited. We argue a time-based algorithm, developed on the Information Processing (IP) model of workload (Hendy, Liao, & Milgram, 1997), provides a viable solution to address this limitation. In this study, we proposed a new algorithmic implementation of the IP model in the context of task network modeling. The new algorithm was implemented in a JAVA program and tested on an existing model of a Bakan vigilance task. The results obtained from the new algorithm demonstrated the feasibility of this solution. By integrating resource-based and time-based algorithms, analysts will be able to diagnose more accurately system performance breakdowns caused by operator overload.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".