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A model of metacognition for bushfire fighters

机译:丛林大火战士的元认知模型

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Large-scale bushfires are complex or macrocognitive decision environments (Klein et al.). They involve many people, such as incident management teams, firefighting crews, and resident communities. Those people can also be geographically dispersed. This means that they need to coordinate their bushfire response efforts and manage multiple, often competing, cognitive demands. To do this, bushfire responders need to use metacognitive skills to regulate their thinking, particularly under stressful high cognitive load conditions. We explored these types of issues with career and volunteer bushfire fighters (Frye and Wearing in J Cogn Technol 16(2): 33-44, 2011). We found that rule-based procedures can sometimes reduce errors, and increase safety, because they reduce cognitive load (e.g., 'you just do it'). However, fixation on other rules and procedures can increase errors, and erode safety, because people fail to adapt to the current situation (e.g., 'you really need to think about that'). In this paper we use a model of metacognition to describe how experts regulate their thinking and thus avoid errors associated with cognitive overload (such as tunnel vision and goal fixation) during large-scale bushfires. The implications for bushfire training and procedures are discussed.
机译:大规模的丛林大火是复杂的或具有宏观认知的决策环境(Klein等人)。他们涉及许多人,例如事件管理团队,消防人员和居民社区。这些人也可以在地理位置上分散。这意味着他们需要协调丛林大火的应对工作,并应对多种(通常是相互竞争的)认知需求。为此,丛林大火的响应者需要使用元认知技能来调节其思维,尤其是在压力很大的认知负荷条件下。我们与职业和志愿森林大火的战斗人员探讨了这类问题(Frye and Wearing in J Cogn Technol 16(2):33-44,2011)。我们发现基于规则的程序有时可以减少错误并提高安全性,因为它们减少了认知负担(例如,“您随便做”)。但是,由于人们无法适应当前情况(例如,“您确实需要考虑这一点”),对其他规则和程序进行固定会增加错误并削弱安全性。在本文中,我们使用元认知模型来描述专家如何调节他们的思维,从而避免在大规模林区大火期间与认知超负荷相关的错误(例如隧道视野和目标固定)。讨论了丛林大火训练和程序的含义。

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