Factors affecting performance on a target monitoring task employing an automatic tracker
Bibliographic record
Abstract
The experiments in this paper examined the extent to which performance on a task employing an automatic tracker was similar to performance on tasks employing other types of automation that have been studied more extensively. Automated target tracking is being used in many sensor and navigation systems to improve performance and help the operator cope with increased data loads. With many automated systems these goals are not met. In particular, the operator often misses errors made by the automated system and may report no decrease in workload. Several hypotheses have been offered for the operator's failure to monitor an automated system adequately. These include lack of experience with the manual task, a vigilance decrement, complacency, and inappropriate level of automation. The relevance of each of these hypotheses to failure to monitor an automatic tracker adequately was examined. Performance and perceived workload on a target tracking task employing an automatic tracker, in which participants had to detect and then update the position of several targets (e.g. ships) at regular intervals, were measured as a function of number of targets, training with the manual task, experience, and time on task. The results suggested that failure to detect errors made by the automated system was due largely to the lack of visibility of the automation errors relative to other errors. However, complacency could not be ruled out entirely. Unlike some other tasks, the availability of a reliable automatic tracker did lead to a substantial reduction in perceived workload.
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 0.001 |
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".