Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, Frankfurt am Main, Germany.
Infant Behav Dev. 2010 Dec;33(4):635-53. doi: 10.1016/j.infbeh.2010.07.018. Epub 2010 Sep 22.
The perception of the unity of objects, their permanence when out of sight, and the ability to perceive continuous object trajectories even during occlusion belong to the first and most important capacities that infants have to acquire. Despite much research a unified model of the development of these abilities is still missing. Here we make an attempt to provide such a unified model. We present a recurrent artificial neural network that learns to predict the motion of stimuli occluding each other and that develops representations of occluded object parts. It represents completely occluded, moving objects for several time steps and successfully predicts their reappearance after occlusion. This framework allows us to account for a broad range of experimental data. Specifically, the model explains how the perception of object unity develops, the role of the width of the occluders, and it also accounts for differences between data for moving and stationary stimuli. We demonstrate that these abilities can be acquired by learning to predict the sensory input. The model makes specific predictions and provides a unifying framework that has the potential to be extended to other visual event categories.
物体统一性的感知、物体在视线之外时的持久性，以及即使在遮挡的情况下仍能感知连续物体轨迹的能力，这些都是婴儿必须首先掌握的最重要的能力。尽管进行了大量的研究，但仍然缺乏这些能力发展的统一模型。在这里，我们尝试提供这样一个统一的模型。我们提出了一个递归人工神经网络，它可以学习预测相互遮挡的刺激的运动，并且可以发展出遮挡物体部分的表示。它可以代表完全遮挡、移动的物体，并能成功预测它们在遮挡后的再次出现。这个框架使我们能够解释广泛的实验数据。具体来说，该模型解释了物体统一性的感知是如何发展的，遮挡物的宽度的作用，还解释了移动和静止刺激的数据之间的差异。我们证明了这些能力可以通过学习预测感官输入来获得。该模型做出了具体的预测，并提供了一个统一的框架，有可能扩展到其他视觉事件类别。
