Center for Vision Research, York University, Toronto, Canada.
PLoS One. 2023 Jan 30;18(1):e0267983. doi: 10.1371/journal.pone.0267983. eCollection 2023.
To interact successfully with moving objects in our environment we need to be able to predict their behavior. Predicting the position of a moving object requires an estimate of its velocity. When flow parsing during self-motion is incomplete-that is, when some of the retinal motion created by self-motion is incorrectly attributed to object motion-object velocity estimates become biased. Further, the process of flow parsing should add noise and lead to object velocity judgements being more variable during self-motion. Biases and lowered precision in velocity estimation should then translate to biases and lowered precision in motion extrapolation. We investigate this relationship between self-motion, velocity estimation and motion extrapolation with two tasks performed in a realistic virtual reality (VR) environment: first, participants are shown a ball moving laterally which disappears after a certain time. They then indicate by button press when they think the ball would have hit a target rectangle positioned in the environment. While the ball is visible, participants sometimes experience simultaneous visual lateral self-motion in either the same or in the opposite direction of the ball. The second task is a two-interval forced choice task in which participants judge which of two motions is faster: in one interval they see the same ball they observed in the first task while in the other they see a ball cloud whose speed is controlled by a PEST staircase. While observing the single ball, they are again moved visually either in the same or opposite direction as the ball or they remain static. We expect participants to overestimate the speed of a ball that moves opposite to their simulated self-motion (speed estimation task), which should then lead them to underestimate the time it takes the ball to reach the target rectangle (prediction task). Seeing the ball during visually simulated self-motion should increase variability in both tasks. We expect to find performance in both tasks to be correlated, both in accuracy and precision.
为了成功地与环境中的移动物体交互，我们需要能够预测它们的行为。预测移动物体的位置需要估计其速度。当自我运动期间的流解析不完整时-也就是说，当一些由自我运动产生的视网膜运动被错误地归因于物体运动时-物体速度估计会产生偏差。此外，流解析的过程应该会增加噪声，并导致在自我运动期间物体速度判断更加多变。因此，在速度估计中的偏差和精度降低应该转化为在运动外推中的偏差和精度降低。我们通过在现实虚拟现实 (VR) 环境中执行的两个任务来研究自我运动、速度估计和运动外推之间的这种关系：首先，向参与者展示一个横向移动的球，该球在一定时间后消失。然后，他们通过按钮按下指示他们认为球会击中环境中定位的目标矩形的时间。当球可见时，参与者有时会在球相同或相反的方向上同时经历视觉横向自我运动。第二个任务是一个两间隔强制选择任务，参与者判断两个运动中哪一个更快：在一个间隔中，他们看到与第一个任务中观察到的相同的球，而在另一个间隔中，他们看到一个球云，其速度由 PEST 阶梯控制。在观察单个球时，他们再次在与球相同或相反的方向上视觉上移动，或者保持静止。我们期望参与者高估与模拟自我运动相反方向移动的球的速度（速度估计任务），这应该导致他们低估球到达目标矩形所需的时间（预测任务）。在视觉模拟自我运动期间看到球应该会增加两个任务中的变异性。我们期望在两个任务中的表现都相关，无论是准确性还是精度。
