Integration of Structural Equation Models and Bayesian Networks for Cognitive Load Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study offers a causal probabilistic modeling for inferring the relationship between humans' cognitive load, the physiological signal predictors of such load, and personality traits. We selected a subset of such signals (heart rate, intervals between successive heartbeats, galvanic skin response, and temperature) from the CogLoad dataset using wearable devices. Structural Equation Modeling techniques were employed to select the predictors to identify the level of cognitive load, for which the ground truth was assessed using subjective tests such as HEXACO that determine the personality traits of the human subjects. Bayesian networks were deployed to investigate the causal relationship and model the inference scenarios. The proposed model is intended to contribute to developing a Computational Intelligence tool for monitoring social health in scenarios of future potential crises such as pandemics and mass migration,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it