The Role of Working Memory in Maintaining Situation Awareness
Bibliographic record
Abstract
Basic and applied research suggests that working memory (WM) supports situation awareness (SA) in dynamic environments. However, the relationship between WM and SA has not been well articulated. The present paper explores the potential role of WM in SA-based tasks by a) using a well-established WM model to conceptually link the two concepts and b) empirically testing this link. A dual-task paradigm was used where participants tracked an object against a moving background. Periodically, participants were required to either predict where the tracked object would be or to search for it. In addition to the tracking task participants concurrently performed one of four load tasks that separately taxed each of the four WM components (i.e. verbal, visual, spatial and central executive control). As predicted by the multi-component WM model (Baddeley, 1986; Logie, 1995) performing the SA tasks (prediction and search) relied on different WM subsystems. It is concluded that prediction involves the verbal subsystem whereas target search involves the spatial subsystem. The results support the role of WM in maintaining SA in a dynamic environment.
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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 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".