Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, Maryland 20892
Department of Psychology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213.
J Neurosci. 2023 Jan 25;43(4):621-634. doi: 10.1523/JNEUROSCI.0371-22.2022. Epub 2022 Dec 6.
Humans can label and categorize objects in a visual scene with high accuracy and speed, a capacity well characterized with studies using static images. However, motion is another cue that could be used by the visual system to classify objects. To determine how motion-defined object category information is processed by the brain in the absence of luminance-defined form information, we created a novel stimulus set of "object kinematograms" to isolate motion-defined signals from other sources of visual information. Object kinematograms were generated by extracting motion information from videos of 6 object categories and applying the motion to limited-lifetime random dot patterns. Using functional magnetic resonance imaging (fMRI) ( = 15, 40% women), we investigated whether category information from the object kinematograms could be decoded within the occipitotemporal and parietal cortex and evaluated whether the information overlapped with category responses to static images from the original videos. We decoded object category for both stimulus formats in all higher-order regions of interest (ROIs). More posterior occipitotemporal and ventral regions showed higher accuracy in the static condition, while more anterior occipitotemporal and dorsal regions showed higher accuracy in the dynamic condition. Further, decoding across the two stimulus formats was possible in all regions. These results demonstrate that motion cues can elicit widespread and robust category responses on par with those elicited by static luminance cues, even in ventral regions of visual cortex that have traditionally been associated with primarily image-defined form processing. Much research on visual object recognition has focused on recognizing objects in static images. However, motion is a rich source of information that humans might also use to categorize objects. Here, we present the first study to compare neural representations of several animate and inanimate objects when category information is presented in two formats: static cues or isolated dynamic motion cues. Our study shows that, while higher-order brain regions differentially process object categories depending on format, they also contain robust, abstract category representations that generalize across format. These results expand our previous understanding of motion-derived animate and inanimate object category processing and provide useful tools for future research on object category processing driven by multiple sources of visual information.
人类可以高精度、高速度地对视觉场景中的物体进行标记和分类，这一能力在使用静态图像的研究中得到了很好的描述。然而，运动也是视觉系统可以用来对物体进行分类的另一个线索。为了确定在没有亮度定义的形状信息的情况下，大脑是如何处理运动定义的物体类别信息的，我们创建了一个新的“物体运动图”刺激集，以从其他视觉信息源中分离出运动定义的信号。物体运动图是通过从 6 个物体类别的视频中提取运动信息，并将运动应用于有限寿命的随机点模式来生成的。使用功能磁共振成像（fMRI）（n = 15，40%为女性），我们研究了物体运动图的类别信息是否可以在枕颞和顶叶皮层内解码，并评估了该信息是否与来自原始视频的静态图像的类别反应重叠。我们在所有高级别感兴趣区域（ROI）中对两种刺激格式的物体类别进行了解码。在静态条件下，更靠后的枕颞和腹侧区域的准确率更高，而在动态条件下，更靠前的枕颞和背侧区域的准确率更高。此外，在两种刺激格式之间进行解码在所有区域都是可能的。这些结果表明，运动线索可以引发广泛而强烈的类别反应，与静态亮度线索引发的反应相当，即使在传统上与主要基于图像定义的形状处理相关的视觉皮层的腹侧区域也是如此。许多关于视觉物体识别的研究都集中在识别静态图像中的物体上。然而，运动是人类也可以用来对物体进行分类的丰富信息源。在这里，我们提出了第一项比较当类别信息以两种格式呈现时，几种有生命和无生命物体的神经表示的研究：静态线索或孤立的动态运动线索。我们的研究表明，虽然高级大脑区域根据格式不同而对物体类别进行不同的处理，但它们也包含跨格式通用的强大、抽象的类别表示。这些结果扩展了我们之前对运动衍生的有生命和无生命物体类别处理的理解，并为未来基于多种视觉信息源的物体类别处理研究提供了有用的工具。
