An Online Platform for Manipulating and Understanding Attentional Tunneling: The Development and Assessment of CogLog 2.0
Bibliographic record
Abstract
This thesis explores the induction and measurement of attentional tunneling in a display-based visual search task through the development and subsequent analysis of an online platform called CogLog. An overview of the terms and definitions relevant to attentional tunneling, as well as a review of the state-of-the-art of measurement and manipulation of attentional tunneling, are provided. The original CogLog was designed by previous researchers but is no longer accessible for further research as originally intended; therefore, the development and assessment of a more accessible and modular platform called CogLog 2.0 as serves as the major contribution of this thesis. The assessment confirms that CogLog 2.0 performs comparably to its predecessor with the addition of a built-in workload assessment feature. CogLog 2.0 is positioned as a tool for further studying attentional tunneling as well as cognitive countermeasures and adaptive display interventions.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| 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".