We aim to ask and answer an essential question "how quickly do we react after observing a displayed visual target?" To this end, we present psychophysical studies that characterize the remarkable disconnect between human saccadic behaviors and spatial visual acuity. Building on the results of our studies, we develop a perceptual model to predict temporal gaze behavior, particularly saccadic latency, as a function of the statistics of a displayed image. Specifically, we implement a neurologically-inspired probabilistic model that mimics the accumulation of confidence that leads to a perceptual decision. We validate our model with a series of objective measurements and user studies using an eye-tracked VR display. The results demonstrate that our model prediction is in statistical alignment with real-world human behavior. Further, we establish that many sub-threshold image modifications commonly introduced in graphics pipelines may significantly alter human reaction timing, even if the differences are visually undetectable. Finally, we show that our model can serve as a metric to predict and alter reaction latency of users in interactive computer graphics applications, thus may improve gaze-contingent rendering, design of virtual experiences, and player performance in e-sports. We illustrate this with two examples: estimating competition fairness in a video game with two different team colors, and tuning display viewing distance to minimize player reaction time.
翻译:我们的目标是询问和回答一个基本问题,“在观察显示的视觉目标之后,我们是如何迅速反应的?”为此,我们展示了心理物理研究,这些研究以人类学程度行为与空间视觉能力之间的显著脱节为特征。根据研究结果,我们开发了一种概念模型,以预测时间观行为,特别是学程度延缓,作为显示图像统计数据的函数。具体地说,我们实施了一种神经学激励的概率模型,模仿信心积累,从而导致一种感知性决定。我们用一系列客观测量和用户研究来验证我们的模型,并使用视觉跟踪VR显示。结果显示我们的模型预测与现实世界人类行为的统计一致性。此外,我们确定在图形管道中常见的许多次临界图像修改可能会显著改变人类反应时间,即使差异在视觉上不易察觉。最后,我们展示了我们的模型可以用作一种指标,用以预测和改变互动计算机图形应用中用户反应的延迟度,从而用视觉跟踪显示一系列客观测量和用户的研究。结果显示我们的模型与现实世界人类行为的统计一致。我们可以用两种视觉动作来改进了游戏的演练,设计了两种演练的演练。我们用来去的演练。