Visual Attention and Roadway Landmark Identification in At-Risk Older Drivers,
DOI: 10.17077/drivingassessment.1033
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Summary
This study investigates the relationship between off-road measures of visual attention and on-road landmark identification performance in at-risk older drivers. Motivated by the understanding that driving requires high visual processing capacity and that reductions in visual attention and processing speed are established risk factors for crashes in older adults, the research aimed to determine if standardized off-road tests could predict a driver’s ability to identify relevant stimuli, such as traffic signals and road signs, within a cluttered driving environment. The study involved 30 drivers aged 66–92 (mean age 74.6; 57% male) enrolled in a larger assessment of at-risk older drivers. Participants underwent a battery of visual and cognitive tests using the Visual Attention Analyzer 3000, which measured visual processing speed, divided attention, and selective attention. Additionally, participants completed an on-road driving task along a commercial segment of a four-lane divided state highway. During this drive, they were required to identify specific landmarks, including 10 restaurants and 7–9 safety-related signs, depending on the direction of travel. Performance was quantified as the percentage of correct identifications. The researchers calculated Spearman correlation coefficients to assess the relationship between the off-road attention scores and the on-road landmark identification performance. The results indicated that the cohort generally exhibited impaired performance on visual processing speed, divided attention, and selective attention tasks. On the road, drivers identified 76.35% of safety signs but only 40.3% of restaurants. Statistical analysis revealed a significant correlation (p=0.034) between the same/different foveal discrimination sub-test (a measure of selective attention) and performance on the restaurant identification task. However, no significant correlations were found between safety sign identification and any of the visual measures. Furthermore, visual processing speed and divided attention did not significantly correlate with either landmark category. The study also found that participant familiarity with the area and opportunities to identify signs while stopped at traffic signals did not significantly affect scores. The findings suggest that selective visual attention is a predictor of the ability to identify commercial landmarks like restaurants, whereas visual processing speed and divided attention are not. Surprisingly, the ability to identify safety signs did not correlate with any measured visual functions. The authors attribute these discrepancies to factors such as variable ambient traffic, which alters driver workload, and response bias, where some drivers indiscriminately called out every landmark encountered. The study concludes that future analyses should apply signal detection theory to address these response biases and better isolate true perceptual performance from behavioral tendencies.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-25 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
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- useful field of view
- external distraction
- peripheral attention
- visual
- cognitive capacity variation
- road complexity
Information type
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol
- Theoretical Contribution: theory or model