Effects of Conversation on Situation Awareness and Working Memory in Simulated Driving
Bibliographic record
Abstract
OBJECTIVE: In the present research, we investigated the hypothesis that working memory mediates conversation-induced impairment of situation awareness (SA) while driving. BACKGROUND: Although there is empirical evidence that conversation impairs driving performance, the cognitive mechanisms that mediate this relationship remain underspecified. Researchers have reported that a phonological working memory task decreased drivers' SA for vehicles located behind them whereas a visuospatial working memory task impaired SA for vehicles ahead. Conversation, therefore, might impair SA for vehicles behind the driver by preferentially taxing the phonological loop. METHOD: A 20-questions task was used as a proxy for natural conversation. In Experiment I, driving performance was measured across three within-subjects conversation conditions (i.e., no conversation, driver asks questions, driver answers questions) with the use of a driving simulator. In Experiment 2, participants drove in the same simulator while either conversing (20-questions task) or not Participants estimated the positions of other vehicles after the screens were blanked at the end of each trial. RESULTS: Speed monitoring and responses to visual probes were impaired by the 20-questions conversation task (Experiment 1). As predicted, conversation impaired SA for the location of other vehicles more for vehicles located behind the driver than for those in front (Experiment 2). CONCLUSION: Conversation impairs drivers' SA of vehicles behind them by taxing working memory's phonological loop and impairs SA generally by taxing working memory's central executive. APPLICATION: Provides a theoretical framework that links driver SA to working memory and a mechanism for understanding why conversation impairs driving performance.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".