Han Zhixian, Sereno Anne B
Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States.
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States.
Front Comput Neurosci. 2024 Jul 2;18:1397819. doi: 10.3389/fncom.2024.1397819. eCollection 2024.
Many studies have shown that the human visual system has two major functionally distinct cortical visual pathways: a ventral pathway, thought to be important for object recognition, and a dorsal pathway, thought to be important for spatial cognition. According to our and others previous studies, artificial neural networks with two segregated pathways can determine objects' identities and locations more accurately and efficiently than one-pathway artificial neural networks. In addition, we showed that these two segregated artificial cortical visual pathways can each process identity and spatial information of visual objects independently and differently. However, when using such networks to process multiple objects' identities and locations, a binding problem arises because the networks may not associate each object's identity with its location correctly. In a previous study, we constrained the binding problem by training the artificial identity pathway to retain relative location information of objects. This design uses a location map to constrain the binding problem. One limitation of that study was that we only considered two attributes of our objects (identity and location) and only one possible map (location) for binding. However, typically the brain needs to process and bind many attributes of an object, and any of these attributes could be used to constrain the binding problem. In our current study, using visual objects with multiple attributes (identity, luminance, orientation, and location) that need to be recognized, we tried to find the best map (among an identity map, a luminance map, an orientation map, or a location map) to constrain the binding problem. We found that in our experimental simulations, when visual attributes are independent of each other, a location map is always a better choice than the other kinds of maps examined for constraining the binding problem. Our findings agree with previous neurophysiological findings that show that the organization or map in many visual cortical areas is primarily retinotopic or spatial.
许多研究表明，人类视觉系统有两条主要的功能上截然不同的皮质视觉通路：一条腹侧通路，被认为对物体识别很重要；另一条背侧通路，被认为对空间认知很重要。根据我们和其他人之前的研究，具有两条分离通路的人工神经网络比单通路人工神经网络能更准确、高效地确定物体的身份和位置。此外，我们还表明，这两条分离的人工皮质视觉通路各自能够独立且不同地处理视觉对象的身份和空间信息。然而，当使用这样的网络来处理多个物体的身份和位置时，就会出现一个绑定问题，因为网络可能无法正确地将每个物体的身份与其位置关联起来。在之前的一项研究中，我们通过训练人工身份通路来保留物体的相对位置信息，从而限制了绑定问题。这种设计使用位置图来限制绑定问题。该研究的一个局限性在于，我们只考虑了物体的两个属性（身份和位置）以及仅一种可能的用于绑定的图（位置）。然而，通常大脑需要处理和绑定物体的许多属性，并且这些属性中的任何一个都可以用于限制绑定问题。在我们当前的研究中，我们使用具有多个需要识别的属性（身份、亮度、方向和位置）的视觉对象，试图找到最佳的图（在身份图、亮度图、方向图或位置图之中）来限制绑定问题。我们发现在我们的实验模拟中，当视觉属性相互独立时，对于限制绑定问题而言，位置图总是比所研究的其他种类的图更好的选择。我们的发现与之前的神经生理学发现一致，这些发现表明许多视觉皮质区域中的组织或图主要是视网膜拓扑的或空间的。
