The Role of Working Memory in Supporting Drivers’ Situation Awareness for Surrounding Traffic
Bibliographic record
Abstract
OBJECTIVE: To link working memory to driver situational awareness (SA) for surrounding traffic. BACKGROUND: Operating a motor vehicle is a complex activity that requires drivers to maintain a high level of SA. Working memory has been conceptually linked to SA; however, the roles of working memory subsystems in supporting driver SA is unclear. METHOD: Participants drove a simulated vehicle and monitored surrounding traffic while concurrently performing either visuospatial- or phonological-load tasks. Drivers' SA was indexed as the ability to recall the positions of the surrounding traffic relative to their own vehicle at the end of each trial. RESULTS: In Experiment I, a visuospatial task interfered with drivers' ability to recall the positions of traffic located in front of their vehicle. In contrast, a phonological task interfered with drivers' ability to recall the positions of traffic located behind their vehicle. Experiment 2 confirmed and extended the findings of Experiment I with the use of different visuospatial- and phonological-load tasks. CONCLUSION: Visuospatial and phonological codes play a role in supporting driver SA for traffic located in the forward view and the rear view, respectively. APPLICATION: Drivers' SA for surrounding vehicles is disrupted by concurrent performance on secondary tasks. The development and implementation of new in-cabin communication, navigation, and informational technologies needs to be done with the knowledge that components of drivers' working memory capacity may be exceeded, thereby compromising driving safety.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".