Abstract
Virtual reality (VR) has become a popular tool to investigate pedestrian behavior. Many researchers, however, tend to simplify traffic scenarios to maximize experimental control at the expense of ecological validity. Multiple repetitions, facilitated by the brief durations of widespread crossing tasks, further add to predictability and likely reduce the need for cognitive processing. Considering the complexity inherent to naturalistic traffic, such simplification may result in biases that compromise the transferability and meaningfulness of empirical results. In the present work, we outline how human information processing might be affected by differences between common experimental designs and naturalistic traffic. Aiming at an optimal balance between experimental control and realistic demands, we discuss measures to counteract predictability, monotony, and repetitiveness. In line with this framework, we conducted a simulator study to investigate the influence of variations in the behavior of surrounding traffic. Although the observed effects seem negligible, we encourage the evaluation of further parameters that may affect results based on scenario design, rather than discussing methodological limitations only in terms of simulator fidelity.
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This research was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, project number 317326196).
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Schneider, S., Li, G. (2020). Virtual Scenarios for Pedestrian Research: A Matter of Complexity?. In: Chen, J.Y.C., Fragomeni, G. (eds) Virtual, Augmented and Mixed Reality. Design and Interaction. HCII 2020. Lecture Notes in Computer Science(), vol 12190. Springer, Cham. https://doi.org/10.1007/978-3-030-49695-1_12
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