IEEE Trans Vis Comput Graph. 2019 Oct;25(10):2886-2895. doi: 10.1109/TVCG.2018.2865945. Epub 2018 Aug 17.
This paper examines how people jointly coordinate their decisions and actions with risky vs. safe human and agent road-crossing partners (Fig. 1 ). The task for participants was to physically cross a steady stream of traffic in a large-screen virtual environment without getting hit by a car. The computer-generated (CG) agent was programmed to be either safe (taking only large gaps) or risky (also taking small gaps). The human partners were classified as safe (taking more large gaps) or risky (also taking some small gaps) based on their average gap size selection. We found that participants in all four conditions preferred to cross with their partner. As a consequence, the riskiness of the partner (both human and agent) influenced the riskiness of participants' gap choices. We also found that participants tightly synchronized their movement with both human and agent partners. The largest differences in performance between those paired with agent vs. human partners occurred on trials when participants crossed different gaps than their partners. This study demonstrates the potential for studying how people interact with CG agents when performing whole-body joint actions using large-screen immersive virtual environments.
本文研究了人们如何与风险或安全的人类和代理道路穿越伙伴共同协调他们的决策和行动（图 1 ）。参与者的任务是在大屏幕虚拟环境中穿越稳定的车流，而不会被汽车撞到。计算机生成的（CG）代理被编程为安全（只选择大间隙）或冒险（也选择小间隙）。基于其平均间隙大小选择，人类伙伴被分类为安全（选择更多大间隙）或冒险（也选择一些小间隙）。我们发现，所有四个条件下的参与者都更喜欢与他们的伙伴一起穿越。因此，伙伴的风险程度（人类和代理）影响了参与者的间隙选择的风险程度。我们还发现，参与者与人类和代理伙伴紧密同步他们的动作。与代理伙伴相比，参与者在与人类伙伴穿越不同间隙的试验中表现出最大的差异。这项研究展示了使用大屏幕沉浸式虚拟环境研究人们在进行全身联合动作时如何与 CG 代理进行交互的潜力。
