The two visual streams hypothesis is a robust example of neural functional specialization that has inspired countless studies over the past four decades. According to one prominent version of the theory, the fundamental goal of the dorsal visual pathway is the transformation of retinal information for visually-guided motor behavior. To that end, the dorsal stream processes input using absolute (or veridical) metrics only when the movement is initiated, necessitating very little, or no, memory. Conversely, because the ventral visual pathway does not involve motor behavior (its output does not influence the real world), the ventral stream processes input using relative (or illusory) metrics and can accumulate or integrate sensory evidence over long time constants, which provides a substantial capacity for memory. In this study, we tested these relations between functional specialization, processing metrics, and memory by training identical recurrent neural networks to perform either a viewpoint-invariant object classification task or an orientation/size determination task. The former task relies on relative metrics, benefits from accumulating sensory evidence, and is usually attributed to the ventral stream. The latter task relies on absolute metrics, can be computed accurately in the moment, and is usually attributed to the dorsal stream. To quantify the amount of memory required for each task, we chose two types of neural network models. Using a long-short-term memory (LSTM) recurrent network, we found that viewpoint-invariant object categorization (object task) required a longer memory than orientation/size determination (orientation task). Additionally, to dissect this memory effect, we considered factors that contributed to longer memory in object tasks. First, we used two different sets of objects, one with self-occlusion of features and one without. Second, we defined object classes either strictly by visual feature similarity or (more liberally) by semantic label. The models required greater memory when features were self-occluded and when object classes were defined by visual feature similarity, showing that self-occlusion and visual similarity among object task samples are contributing to having a long memory. The same set of tasks modeled using modified leaky-integrator echo state recurrent networks (LiESN), however, did not replicate the results, except under some conditions. This may be because LiESNs cannot perform fine-grained memory adjustments due to their network-wide memory coefficient and fixed recurrent weights. In sum, the LSTM simulations suggest that longer memory is advantageous for performing viewpoint-invariant object classification (a putative ventral stream function) because it allows for interpolation of features across viewpoints. The results further suggest that orientation/size determination (a putative dorsal stream function) does not benefit from longer memory. These findings are consistent with the two visual streams theory of functional specialization.

两种视觉流假说是神经功能专业化的有力例子，在过去的四十年里激发了无数的研究。根据该理论的一个重要版本，背侧视觉通路的基本目标是将视网膜信息转换为视觉引导的运动行为。为此，背侧流仅在运动启动时才使用绝对（或真实）度量来处理输入，因此需要很少的内存或不需要内存。相反，由于腹侧视觉通路不涉及运动行为（其输出不影响现实世界），因此腹侧流使用相对（或虚幻）度量来处理输入，并且可以在长时间常数内积累或整合感觉证据，这提供了强大的记忆能力。在本研究中，我们通过训练相同的循环神经网络来执行视点不变的对象分类任务或方向/大小确定任务，测试了功能专业化、处理指标和记忆之间的关系。前一项任务依赖于相对指标，受益于积累感官证据，并且通常归因于腹侧流。后一个任务依赖于绝对度量，可以立即准确计算，并且通常归因于背侧流。为了量化每个任务所需的内存量，我们选择了两种类型的神经网络模型。使用长短期记忆（LSTM）循环网络，我们发现视点不变的对象分类（对象任务）比方向/大小确定（方向任务）需要更长的记忆。此外，为了剖析这种记忆效应，我们考虑了导致对象任务中记忆时间更长的因素。首先，我们使用了两组不同的对象，一组具有自遮挡特征，一组没有。其次，我们严格地通过视觉特征相似性或（更自由地）通过语义标签来定义对象类。当特征自遮挡以及通过视觉特征相似性定义对象类别时，模型需要更大的记忆，这表明对象任务样本之间的自遮挡和视觉相似性有助于拥有长记忆。然而，使用改进的泄漏积分器回声状态循环网络（LiESN）建模的同一组任务并没有复制结果，除非在某些条件下。这可能是因为 LiESN 由于其网络范围的内存系数和固定的循环权重而无法执行细粒度的内存调整。总之，LSTM 模拟表明，较长的内存有利于执行视点不变的对象分类（假定的腹侧流函数），因为它允许跨视点对特征进行插值。结果进一步表明，方向/大小确定（假定的背侧流功能）不会从较长的记忆中受益。