Quantitative Analysis of the Effort-Fatigue Tradeoff in the Conceptual Design Process: A Multistate EEG Approach
Bibliographic record
Abstract
Physiological signals are at the core of affective and cognitive engineering methods which aim at providing a more humancentric perspective to engineering concepts and systems. Labelling physiological signals with meaningful words such as concentration, fatigue or effort is a hard task. Although those labels are well-documented in the medical and related literature, their meaning gets lost in translation in engineering and design sciences. Multistate analysis of physiological signals aims at alleviating this process by identifying pittfalls and errors backed by hard numerical and statistical evidence. In this paper, we use multistate analysis of EEG signals to revisit the effort-fatigue tradeoff in the conceptual design process. Many rules of thumb and intuitions may exist about the effort-fatigue tradeoff and the goal is to provide a quantitative framework where this tradeoff can bear meaning. Following our multistate analysis, we define different types of fatigue (TYPE 1-5 Fatigue) which behave differently based on our numerical analysis and conclude that fatigue and by extension effort are multidimensional concepts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".