Level of Automation Effects on Situation Awareness and Functional Specificity in Automation Reliance
Bibliographic record
Abstract
This work investigated the relationship between task performance and situation awareness ( SA) at different levels of automation (LOA). The conventional wisdom is that routine performance improves with level of automation but that the consequences of automation failure become more severe. This has been characterized as a routine-failure trade-off, However, recent research indicates that the trade-off is subject to unknown contextual factors in addition to the level of automation. Furthermore, it has been suggested that the human operator’s SA may impact whether the trade-off between performance under routine and failure conditions is always tenable. The current study therefore aimed to i) provide evidence to support or refute the trade-off and ii) to identify possible extenuating factors. The results generally supported the existence of the routine-failure trade-off, though the strength of this finding was tempered somewhat by operators’ apparent selective disuse of higher level automation which limited the effective range of LOA tested. We interpreted that the SA collection method made the goal of SA maintenance explicit and in doing so encouraged operators to preferentially reallocate attention away from other system goals. Thus, the functional structure of the task seems to affect whether the routine-failure trade-off occurs in a given instance.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".