Effects of Conversation on Situation Awareness and Working Memory in Simulated Driving
Why this work is in the frame
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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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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 | 0.001 | 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) | 0.000 | 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 it