Cognitive change measurement through driving navigation ability sensing and analysis
Bibliographic record
Abstract
This paper explores the detection of cognitive change in individuals by sensing a high cognition task (driving). The paper proposes algorithms for the analysis of a set of training trips by a driver to create baseline attributes and features for measurement of baseline navigational performance. Algorithms are proposed for the measurement of subsequent trips through comparison to the baseline performance attributes and the paper shows that trips with common coping mechanisms for cognitive decline can be identified and classified. Common coping mechanisms include use of familiar routes by backtracking to home or reduction in trip complexity through reduction in the variety of stops or in the number of stops are all identified. In addition, algorithms are proposed that identify changes in the navigation ability by indicating routing mistakes or poor choices. The paper shows that the measurement of patient performance can be compared to gold standard Google Maps based routing and navigation choices providing a baseline for a patient's cognitive performance and that cognitive change could be detected in behavior change relative to this baseline including less efficient trip planning, reduced trip complexity or less optimal navigation through use of inefficient but more familiar routes as coping mechanisms.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".