Bibliographic record
Abstract
Attentional tunneling is a cognitive bias that arises when an individual is so focused on a task that they are unable to explore alternative solutions apart from their existing belief on how to complete it. This cognitive bias can be disrupted through cognitive countermeasures, which are automated adaptive interventions designed to interrupt an attention tunnel state. , as well as the development and results of a machine learning classifier that uses data collected from CogLog, a platform designed to induce users into an attention tunneled state. This paper presents an experiment conducted with 45 participants that adapts the experimental methods of previous work on detecting attention tunneling using non-invasive triggers and the CogLog platform to train a supervised machine learning model to detect attention tunneling. The results show that the classifier's classification ability can be improved by integrating additional trigger classes into the machine learning model. These findings highlight the feasibility of employing non-invasive triggers to detect occurrences of attention tunnels effectively, ultimately leading to more robust and adaptable solutions to improve user performance and safety in task environments in which external measurement tools may not be feasible.
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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 | 0.001 |
| 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".