Ahuja Aarit, Sheinberg David L
Neuroscience Department, Brown University, Providence, RI, USA.
Carney Institute for Brain Science, Brown University, Providence, RI, USA.
J Vis. 2019 Jun 3;19(6):13. doi: 10.1167/19.6.13.
We regularly interact with moving objects in our environment. Yet, little is known about how we extrapolate the future movements of visually perceived objects. One possibility is that movements are experienced by a mental visual simulation, allowing one to internally picture an object's upcoming motion trajectory, even as the object itself remains stationary. Here we examined this possibility by asking human participants to make judgments about the future position of a falling ball on an obstacle-filled display. We found that properties of the ball's trajectory were highly predictive of subjects' reaction times and accuracy on the task. We also found that the eye movements subjects made while attempting to ascertain where the ball might fall had significant spatiotemporal overlap with those made while actually perceiving the ball fall. These findings suggest that subjects simulated the ball's trajectory to inform their responses. Finally, we trained a convolutional neural network to see whether this problem could be solved by simple image analysis as opposed to the more intricate simulation strategy we propose. We found that while the network was able to solve our task, the model's output did not effectively or consistently predict human behavior. This implies that subjects employed a different strategy for solving our task, and bolsters the conclusion that they were engaging in visual simulation. The current study thus provides support for visual simulation of motion as a means of understanding complex visual scenes and paves the way for future investigations of this phenomenon at a neural level.
我们在周围环境中经常与移动物体进行交互。然而,对于我们如何推断视觉感知物体的未来运动,却知之甚少。一种可能性是,运动是通过心理视觉模拟来体验的,这使人们能够在脑海中描绘出物体即将到来的运动轨迹,即使物体本身保持静止。在这里,我们通过要求人类参与者对充满障碍物的显示屏上下落球的未来位置进行判断,来检验这种可能性。我们发现,球的轨迹特性对受试者在任务中的反应时间和准确性具有高度预测性。我们还发现,受试者在试图确定球可能落在哪里时所做的眼动,与实际观察球下落时所做的眼动在时空上有显著重叠。这些发现表明,受试者模拟了球的轨迹以指导他们的反应。最后,我们训练了一个卷积神经网络,看看这个问题是否可以通过简单的图像分析来解决,而不是我们提出的更复杂的模拟策略。我们发现,虽然该网络能够解决我们的任务,但模型的输出并不能有效或一致地预测人类行为。这意味着受试者采用了不同的策略来解决我们的任务,并支持了他们正在进行视觉模拟的结论。因此,当前的研究为将运动视觉模拟作为理解复杂视觉场景的一种方式提供了支持,并为未来在神经层面研究这一现象铺平了道路。