Attentional capture in driving displays
DOI: 10.1111/bjop.12197
archive: archived pipeline: cataloged
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Summary
This study investigates the conditions under which salient visual distractors capture attention in realistic driving scenarios, addressing a gap in the literature where previous research relied on sparse, artificial displays. The authors hypothesized that while drivers can often ignore distractors in known irrelevant locations, capture effects would increase when distractors are rare or when central attentional resources are diverted by a secondary task. The research comprised three experiments using simulated driving scenes (photographs taken from inside a car) where participants searched for a red target letter (T or L). Distractors were induced by color changes (red or green) on a GPS unit located in an irrelevant area of the dashboard where the target never appeared. Experiment 1 tested standard conditions with distractors present on 50% of trials. Experiment 2 manipulated distractor frequency, comparing groups where distractors appeared on only 10% versus 20% of trials. Experiment 3 examined the impact of divided attention by requiring a subset of participants to simultaneously monitor a stream of spoken digits for sequential repetitions while performing the visual search. In Experiment 1, no significant attentional capture was observed; reaction times were nearly identical whether the GPS distractor was present or absent, and there was no difference between relevant (red) and irrelevant (green) distractor colors. This suggests that when distractors are frequent, participants can effectively ignore them even in complex scenes. However, Experiment 2 revealed that distractor rarity significantly increased capture. The present-absent cost (slowing in reaction time when a distractor is present) was 61 ms for the 10% occurrence group, nearly five times larger than the 13 ms cost for the 20% occurrence group. This indicates that infrequent distractors are more likely to capture attention because the attentional goal to suppress them is weaker. Experiment 3 demonstrated that diverting central resources also amplified capture. Participants in the dual-task condition (monitoring the auditory stream) exhibited much larger capture effects than those in the single-task condition, confirming that reduced central resources hinder the ability to ignore salient but irrelevant stimuli. These findings identify two key risk factors for attentional capture in driving: the rarity of the distractor and the diversion of attention to secondary tasks. The results challenge the notion that salient stimuli are always automatically captured, showing that top-down control can suppress capture when distractors are predictable. Conversely, the study highlights that real-world distractions are most dangerous when they are unexpected or when the driver is cognitively loaded, providing specific implications for designing in-vehicle interfaces and understanding driver distraction risks.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | manual_pmc_pow_fetch | — | — | 47 | 2026-08-22 |
| extract | success | cached | — | — | 4 | 2026-08-23 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.8-27b-gittensor | summ-v5 | 2 | 2026-08-23 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-23; verification: pending re-verification.
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol
- Theoretical Contribution: theory or model