An exploration into the contributing cognitive skills of lifeguard visual search

Abstract

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.

Intense classification training to increase the detection of drowning swimmers

“…Importantly, the improvement in performance between pre‐ and post‐test was marked, with drowning detection improving by 26% for the drowning training group. This level of performance is similar to that of lifeguard participants in previous research (Laxton et al, 2022). This improvement appears due to the repeated exposure and level of engagement in the drowning‐training intervention.…”

“…This was achieved by employing a novel intense classification task where participants had to repeatedly and rapidly classify whether a swimmer was experiencing drowning/distress, or simply playing in the water. The rationale behind this was that the evidence from the literature and previous studies suggest that processing of the drowning characteristics is perhaps the key factor that underlies lifeguard superiority in drowning detection tasks (Laxton et al, 2022). Interpretation of otherwise ambiguous swimming behaviours was improved by forcing participants to classify potential drowning events.…”

“…This proposed faster recognition of drowning behaviours was explored in a recent paper by Laxton et al (2022). This explored which cognitive skills potentially underlie drowning detection, comparing performance on a multiple object avoidance task (MOA; Mackenzie et al, 2021) and a functional field of view task (FFoV; Park & Reed, 2010) to performance on an occlusion‐based drowning detection task (developed by Laxton, Guest, et al, 2021).…”

“…This was reinforced by the finding (Laxton, Crundall, et al, 2021, Experiment 2) that lifeguards and lifesavers performed similarly, even though the latter group only receives training in how to identify drowning behaviours, rather than how to scan for them. This proposed faster recognition of drowning behaviours was explored in a recent paper by Laxton et al (2022). This explored which cognitive skills potentially underlie drowning detection, comparing performance on a multiple object avoidance task (MOA; Mackenzie et al, 2021) and a functional field of view task (FFoV; Park & Reed, 2010) to performance on an occlusion-based drowning detection task (developed by .…”

“…Given the evidence that drowning detection likely improves via increased exposure to the stimuli (Laxton et al, 2022), the next obvious step in this research is to determine whether this process can be harnessed for a training intervention. It may be possible to use a similar classification task as used in the central FFoV task from the Laxton et al (2022) study to improve lifeguard drowning detection.…”