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
OBJECTIVE: The aim of this study was to empirically assess the efficacy of cognitive countermeasures based on the technique of information removal to enhance human operator attentional disengagement abilities when facing attentional tunneling. BACKGROUND: Lessons learned from human factors studies suggest that conflict with automation leads to the degradation of operators' performance by promoting excessive focusing on a single task to the detriment of the supervision of other critical parameters. METHOD: An experimental setup composed of a real unmanned ground vehicle and aground station was developed to test the efficiency of the cognitive countermeasures.The scenario (with and without countermeasure) involved an authority conflict between the participants and the robot induced by a battery failure.The effects of the conflict and, in particular, the impact of cognitive countermeasures on the participants' cognition and arousal were assessed through heart rate measurement and eye tracking techniques. RESULTS: In the control group (i.e., no countermeasure), 8 out of 12 participants experienced attentional tunneling when facing the conflict, leading them to neglect the visual alarms displayed that would have helped them to understand the evolution of the tactical situation. Participants in the countermeasure group showed lower heart rates and enhanced attentional abilities, and 10 out of 11 participants made appropriate decisions. CONCLUSIONS: The use of cognitive countermeasures appeared to be an efficient means to mitigate excessive focus issues in the unmanned ground vehicle environment. APPLICATIONS: The principle of cognitive countermeasures can be applied to a large domain of applications involving human operators interacting with critical systems.
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 | 0.001 | 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 | 0.000 |
| 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 it