Using Cardiac and Electrodermal Activity as Cognitive Markers for Interruptions and Distraction in a Surveillance Simulation
Bibliographic record
Abstract
Security surveillance is frequently used to increase public safety. Characteristics of the surveillance rooms, however, pose many cognitive challenges pertaining to distraction and interruptions, which may affect surveillance performance. Affective computing could represent a potential solution. It involves the recognition and the interpretation of human states using, for instance, different psychophysiological measures. As a first step toward this goal, the present study aimed at assessing whether cardiac and electrodermal activity, could be used as potential markers of interruptions and distraction during a surveillance simulation. A total of 126 participants went through a simulation involving four 8-min scenarios using a high-fidelity urban security surveillance microworld. Task interruption in the form of a realistic secondary task to perform and distraction in the form of background noise representative of a busy operational centre were also implemented into the simulation. Different features of the electrocardiographic (ECG) signal varied with the presence of distraction, but also as a function of time on task. Electrodermal (EDA) features mainly varied as a function of time. These results suggest that distraction and time on task specifically impacted cognitive functioning, potentially increasing sympathetic activity through cognitive workload, and that EDA and ECG measures may represent relevant markers to use from an affective computing perspective to particularly pinpoint periods of distraction and hypovigilance. Implications for the development of user-adaptive systems are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".