Summary: Lifeguard surveillance is a complex task that is crucial for swimmer safety, though few studies of applied visual search have investigated this domain. This current study compared lifeguard and non-lifeguard search skills using dynamic, naturalistic stimuli (video clips of confederate swimmers) that varied in set size and type of drowning. Lifeguards were more accurate and responded faster to drowning targets. Differences between drowning targets were also found: Passive drownings were responded to less often, but more quickly than active drownings, highlighting that passive drownings may be less salient but are highly informative once detected. Set size effects revealed a dip in reaction speeds at an intermediate set-size level, suggesting a possible change in visual search strategies as the array increases in size. Nonetheless, the ability of the test to discriminate between lifeguards and non-lifeguards offers future possibilities for training and assessing lifeguard surveillance skills.

Visual search is increasingly being explored in dynamic, real-world environments. This includes swimming pools, where lifeguards have shown superior drowning detection in simulated environments. Here we explored if lifeguard superiority is observed in real-life scenes of a busy swimming pool. Experiment 1 required participants to identify real-life distressed swimmers in clips of busy pool activity via a touchscreen interface. Experiment 2 sought to replicate the first study, with the inclusion of eye-movement measures.Experiment 3 varied the methodology, using an occlusion method where clips were frozen and blurred shortly after target onset. The results demonstrated an experience effect, with lifeguards detecting distressed swimmers more often and faster than non-lifeguards. No clear differences were found in the eye-movements between groups; thus, we cannot conclude that the lifeguards' faster responses are due to better scanning strategies. The different methodological approaches revealed the occlusion method to have the larger effect size, supporting the growing evidence that occlusion may be a better test for dynamic target detection than traditional response-time tests. This research demonstrates that the clear lifeguard experience effect generalises to real-life pool environments with a large number of swimmers and real incidents. It could be used to inform lifeguard training tools and assessments.

How does domain expertise influence dynamic visual search? Previous studies of visual search often use abstract search arrays that are devoid of applied context, with comparatively few studies exploring applied naturalistic and dynamic settings. The current research adds to this literature by examining lifeguard drowning-detection across two studies using naturalistic, dynamic search tasks. Behavioural responses and eye-movement data were recorded as participants watched staged video clips and attempted to identify if a swimmer was drowning. The results demonstrate lifeguard superiority in response times to drowning events, compared to non-lifeguards. No differences between lifeguard and non-lifeguard eye-movements were noted however. This suggests that the experiential benefit in response times results from other underlying processes, rather than any scanning benefits. This research highlights the complex nature of naturalistic and dynamic searches, while demonstrating the robust nature of simulated videos in producing experience effects in visual search.

Lifeguard drowning detection in swimming pools and beach settings is influenced by experience. The current experiment explores the cognitive skills that might underlie this experience effect. Lifeguard and non-lifeguard performance in a domain-free multiple object avoidance (MOA) task and a partially domain-free functional field of view (FFOV) task was compared to performance on an occlusion-based drowning detection task. Lifeguards performed better than non-lifeguards on the MOA task and the FFOV central task (identifying whether an isolated swimmer was drowning).However, only performance in the central FFOV task was associated with performance in the occlusion-based drowning detection task, and this was the only part of the two tasks that was not domain-free. These results suggest lifeguard drowning detection is mainly driven through the learned ability to process behaviours of drowning swimmers quicker than non-lifeguards. Therefore, it may be possible to train novices' ability to detect drowning swimmers through an exposure task.

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