Chapter 6 118 easily. However, Chapter 4 has demonstrated that factors that enhance representativeness in real-life, may not enhance representativeness in VR. VR training elicited high levels of heart rate and perceived stress without the addition of a pain stimulus. When adding a pain stimulus to simulate the possibility of getting hit by an opponent in VR, it did not increase the trainees heart rates or the level of perceived stress further, oppositely to what has been shown during real-life training (Nieuwenhuys & Oudejans, 2011). When aiming to apply the representative learning design to VR, the theoretical considerations of representativeness have to be reconsidered. Like in real-life, trainees should be able to utilize key features and characteristics of the actual performance context. In VR, however, using too many features (e.g., pain stimulus) to create a representative performance environment may not have any additional benefit or possibly overstimulate trainees. Thus, the theoretical implications for representative learning design in VR entail a larger focus on factors like the cognitive load the virtual environment itself imposes on trainees on top of focusing predominantly on designing or adding characteristics that resemble the performance context as closely as possible. On the job, police officers perform a variety of skills and tasks under the influence of stress. To this end, and in line with representative learning design, real-life and VR training should provide challenging situations in which trainees can safely prepare for on-duty incidents. Cognitive load theory (CLT) describes how cognitive resources can be managed to increase learning (Van Merrienboer & Sweller, 2005; Mugford et al., 2013). CLT can therefore provide a theoretical framework for effective instructional design of training. In Chapter 3, we have shown that during VR training, police officers experience higher extraneous load compared to real-life training. Thus, particularly for VR training, it is important to reduce extraneous load (e.g., learning-irrelevant demands placed on the trainee by the newness of the VR technology) as much as possible, increase germane load, and manage intrinsic load for optimal learning (Mugford et al., 2013). In VR, extraneous load can be reduced by providing sufficient time for familiarization with the VR equipment and environment. During the familiarization period, police instructors should provide specific instructions that help the trainee to navigate the environment. This approach increases germane load and prepares trainees for the execution of the training scenarios. Police trainers should design VR based on a training design that adheres to incremental exposure to VR with sufficient break time in between to reduce extraneous load and the occurrence of cybersickness — an additional factor that is cognitively demanding (da Silva Marinho et al., 2022). To manage intrinsic load (i.e., the inherent difficulty of the task and the complexity of the information to be learned), VR as a training tool provides various possibilities for differentiation. In VR, the intrinsic cognitive load can be managed by adjusting the complexity of the training scenarios through the various options that VR provides: initial
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