Complex decision-making in threatening environments

威胁环境中的复杂决策

• 批准号: 2602944
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2602944
• 关键词: Complex; decision; making; threatening; environments

Many professions are asked to operate in environments that are inherently hostile and unpredictable (e.g., emergency services, military, and disaster support). Within these environments, they must make critical decisions based upon split-second assessments of environmental risks and upon predictions of the consequences of different courses of action. But the capacity to meet such cognitive demands may be seriously impeded by being in a threatened state (Lieberman et al., 2005), leading to mistakes, freezing, or impulsive action (McCall & Laycock, 2021). While training programs attempt to prepare individuals for working in threatening environments, research shows that trainees deviate from formal decision-making strategies during simulations of real-world complexity and threat (van den Heuvel, Alison, & Crego, 2012). A better understanding of how individuals negotiate uncertainty while under threat may therefore have important implications for improving screening and training individuals for optimal performance in these types of environments (McCall & Laycock, 2021). I propose to examine the effect of threat on complex decision-making in naturalistic environments and to do so both with civilian and expert (i.e., military) samples.To achieve this goal, I will integrate a classic paradigm from the complex decision-making literature within a virtual reality (VR) environment. This environment will allow me to manipulate threat (McCall, Hildebrandt, Bornemann, & Singer, 2015) naturalistically and to replicate the cognitive demands of an operational environment. This environment will also allow me to measure potentially critical variables such as visual attention, speed of movement, and subjective experience (McCall, Hildebrandt, Hartmann, Baczkowski, & Singer, 2016). I will plan and execute three phases of enquiry, spanning all research opportunities during the MA in social research methods and PhD. In Phase 1, I will develop and validate the VR platform to test adaptive decision-making (VRIGT). The design of VRIGT will be based on the established Iowa gambling task (IGT) (Bechara, Damasio, Damasio, & Anderson, 1994). Within VRIGT, the basic structure of the IGT will remain unchanged. However, the task will take place in a virtual building which the participant is attempting to exit. I will replace the IGT cards with doors, cash with time, and the bank with time left to escape. The aim is not to bank cash but to escape quickly. Validation will be achieved by correlating the performance of participants across three conditions (VR, desktop display and classic IGT application).Phase 2 will add threat to the VRIGT simulation and conduct a study that addresses the question, what is the effect of threat on adaptive decision-making performance? Participants will be asked to negotiate the VRIGT in both threatening (e.g., the potential for explosive devices in the world) and non-threatening conditions. As the VRIGT is designed around a single environment, variation between conditions can be isolated to manipulations of threats themselves.In Phase 3, will use the VRIGT to test individual differences in complex decision-making in a relevant real-world population, trainees from the UK Royal Marines. Here I will test the effects of specialist training and experience (operational exposure) on adaptive decision-making. Through this project, I hope to shed light on complexity as a meaningful factor in understanding cognitive performance in threatening and unpredictable environments. Achieving this will help address legacy issues of ecological validity in this field. Moreover, it may lead directly to improvements in real-world training and screening for professionals who work in high-risk environments. I believe this will ultimately help combat unsafe and unethical practice in hostile and unpredictable environments.

许多职业被要求在固有的敌对和不可预测的环境中工作（例如，紧急服务、军事和灾难支持）。在这些环境中，他们必须根据对环境风险的瞬间评估和对不同行动方案后果的预测做出关键决策。但是，处于受威胁的状态可能会严重阻碍满足这种认知需求的能力（Lieberman et al., 2005），从而导致错误、冻结或冲动行为（McCall & Laycock, 2021）。虽然培训计划试图让个人做好在危险环境中工作的准备，但研究表明，在模拟现实世界的复杂性和威胁时，受训者会偏离正式的决策策略（van den Heuvel, Alison, & Crego, 2012）。因此，更好地了解个体在受到威胁时如何应对不确定性，可能对改善筛查和培训个体在这些类型的环境中获得最佳表现具有重要意义（McCall & Laycock, 2021）。我建议在自然环境中研究威胁对复杂决策的影响，并以平民和专家（即军事）为样本进行研究。为了实现这一目标，我将在虚拟现实（VR）环境中整合复杂决策文献中的经典范例。这种环境将允许我自然地操纵威胁（McCall, Hildebrandt, Bornemann, & Singer, 2015），并复制操作环境的认知需求。这种环境还允许我测量潜在的关键变量，如视觉注意力、运动速度和主观体验（McCall, Hildebrandt, Hartmann, Baczkowski, & Singer, 2016）。我将计划并执行三个阶段的调查，涵盖社会研究方法硕士和博士期间的所有研究机会。在第一阶段，我将开发和验证VR平台，以测试自适应决策（vright）。vright的设计将基于既定的爱荷华赌博任务（IGT） （Bechara, Damasio, Damasio, & Anderson, 1994）。在vright中，IGT的基本结构将保持不变。然而，任务将发生在一个虚拟的建筑物，参与者试图退出。我会把IGT卡换成门，把现金换成时间，把银行换成逃跑的时间。他们的目的不是把钱存入银行，而是迅速逃离。验证将通过将参与者在三种条件下（VR，桌面显示和经典IGT应用）的表现相关联来实现。第二阶段将在vright模拟中添加威胁，并进行一项研究，以解决威胁对适应性决策绩效的影响。与会者将被要求在威胁（例如，世界上潜在的爆炸装置）和非威胁条件下谈判vright。由于vright是围绕单一环境设计的，因此可以将条件之间的变化与威胁本身的操作隔离开来。在第三阶段，将使用vright测试来自英国皇家海军陆战队的相关现实世界人群中复杂决策的个体差异。在这里，我将测试专业培训和经验（操作暴露）对适应性决策的影响。通过这个项目，我希望阐明复杂性作为理解在威胁和不可预测的环境中的认知表现的一个有意义的因素。实现这一目标将有助于解决该领域生态有效性的遗留问题。此外，它可能直接导致现实世界中对在高风险环境中工作的专业人员的培训和筛选的改善。我相信这最终将有助于在敌对和不可预测的环境中打击不安全和不道德的做法。