Burk Diana C, Sheinberg David L
Department of Neuroscience, Brown University, Providence, RI 02912, United States.
Carney Institute for Brain Science, Brown University, Providence, RI 02912, United States.
Cereb Cortex Commun. 2022 Aug 18;3(3):tgac034. doi: 10.1093/texcom/tgac034. eCollection 2022.
Our brains continuously acquire sensory information and make judgments even when visual information is limited. In some circumstances, an ambiguous object can be recognized from how it moves, such as an animal hopping or a plane flying overhead. Yet it remains unclear how movement is processed by brain areas involved in visual object recognition. Here we investigate whether inferior temporal (IT) cortex, an area known for its relevance in visual form processing, has access to motion information during recognition. We developed a matching task that required monkeys to recognize moving shapes with variable levels of shape degradation. Neural recordings in area IT showed that, surprisingly, some IT neurons responded stronger to degraded shapes than clear ones. Furthermore, neurons exhibited motion sensitivity at different times during the presentation of the blurry target. Population decoding analyses showed that motion patterns could be decoded from IT neuron pseudo-populations. Contrary to previous findings, these results suggest that neurons in IT can integrate visual motion and shape information, particularly when shape information is degraded, in a way that has been previously overlooked. Our results highlight the importance of using challenging multifeature recognition tasks to understand the role of area IT in naturalistic visual object recognition.
即使视觉信息有限，我们的大脑也会持续获取感官信息并做出判断。在某些情况下，可以从物体的运动方式识别出模糊的物体，比如跳跃的动物或头顶飞过的飞机。然而，参与视觉物体识别的脑区是如何处理运动信息的，目前仍不清楚。在这里，我们研究了颞下回（IT）皮质（一个在视觉形状处理方面具有相关性的区域）在识别过程中是否能够获取运动信息。我们设计了一项匹配任务，要求猴子识别形状退化程度不同的移动形状。IT区的神经记录显示，令人惊讶的是，一些IT神经元对退化形状的反应比对清晰形状的反应更强。此外，在模糊目标呈现期间的不同时间，神经元表现出运动敏感性。群体解码分析表明，可以从IT神经元伪群体中解码出运动模式。与之前的研究结果相反，这些结果表明，IT区的神经元能够以一种之前被忽视的方式整合视觉运动和形状信息，特别是当形状信息退化时。我们的研究结果凸显了使用具有挑战性的多特征识别任务来理解IT区在自然视觉物体识别中的作用的重要性。
