Fluctuating Attentional Demand in a Simulated Driving Assessment: The Roles of Age and Driving Complexity
Bibliographic record
Abstract
OBJECTIVE: The purpose of the study was to explore age differences in attentional demand in response to driving situations of varying complexity within the context of a simulated assessment protocol. It was hypothesized that as road complexity increased, an indicator of attentional demand (i.e., latency to respond to a secondary task) would increase and, independent of the road complexity, older adults would exhibit greater attentional demand in comparison with younger and middle-aged drivers. METHODS: Drivers from 3 age categories (i.e., young, middle-aged, and older) completed an assessment protocol in a STISIM driving simulator (Systems Technology, Inc., Hawthorne, CA) during which participants responded to a series of strategically placed secondary tasks (i.e., peripheral detection tasks, PDTs). Situations where secondary tasks occurred were grouped according to whether they were straight-road, crossing-path, or lane-change events. Two global indices of driving safety as well as several cognitive measures external to the driving simulator were also collected. RESULTS: The results supported the hypothesis in that complex driving situations elicited greater attentional demand among drivers of all ages. Older adults showed greater attentional demand in comparison to young and middle-aged adults even after controlling for baseline response time. Older drivers also scored poorer on a global measure of driving safety. CONCLUSIONS: The findings are highly consistent with the literature on road complexity and attention that show that increased driving complexity is associated with poorer performance on tasks designed to concurrently assess attention, an effect that is more pronounced for older drivers. The results point to intrinsic and extrinsic factors that contribute to motor vehicle collisions (MVCs) among older drivers. The relevance of these findings is discussed in relation to interventions and future research aimed at improving road 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 | 0.005 |
| 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) | 0.001 | 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".