Laboratory of Brain and Cognition, NIMH/NIH, Bethesda, MD, USA.
Laboratory of Neuropsychology, NIMH/NIH, Bethesda, MD, USA.
J Vis. 2021 Apr 1;21(4):3. doi: 10.1167/jov.21.4.3.
The current experiment investigated the extent to which perceptual categorization of animacy (i.e., the ability to discriminate animate and inanimate objects) is facilitated by image-based features that distinguish the two object categories. We show that, with nominal training, naïve macaques could classify a trial-unique set of 1000 novel images with high accuracy. To test whether image-based features that naturally differ between animate and inanimate objects, such as curvilinear and rectilinear information, contribute to the monkeys' accuracy, we created synthetic images using an algorithm that distorted the global shape of the original animate/inanimate images while maintaining their intermediate features (Portilla & Simoncelli, 2000). Performance on the synthesized images was significantly above chance and was predicted by the amount of curvilinear information in the images. Our results demonstrate that, without training, macaques can use an intermediate image feature, curvilinearity, to facilitate their categorization of animate and inanimate objects.
当前的实验研究了知觉分类的程度，即区分有生命和无生命物体的能力是否受到区分这两个物体类别的基于图像的特征的促进。我们表明，在名义训练下，天真的猕猴可以以很高的准确率对独特的 1000 个新图像进行分类。为了测试基于图像的特征是否自然地在有生命和无生命的物体之间存在差异，例如曲线和直线信息，是否有助于猴子的准确性，我们使用一种算法创建了合成图像，该算法在保持原始有生命/无生命图像的中间特征的同时扭曲了全局形状（Portilla & Simoncelli，2000）。在合成图像上的表现明显高于随机水平，并且可以通过图像中的曲线信息量来预测。我们的结果表明，在没有训练的情况下，猕猴可以使用中间图像特征，即曲线度，来促进它们对有生命和无生命物体的分类。
