Greene Michelle R, Fei-Fei Li
Department of Computer Science, Stanford University, Stanford, CA.
J Vis. 2014 Jan 16;14(1):14. doi: 10.1167/14.1.14.
Human observers categorize visual stimuli with remarkable efficiency--a result that has led to the suggestion that object and scene categorization may be automatic processes. We tested this hypothesis by presenting observers with a modified Stroop paradigm in which object or scene words were presented over images of objects or scenes. Terms were either congruent or incongruent with the images. Observers classified the words as being object or scene terms while ignoring images. Classifying a word on an incongruent image came at a cost for both objects and scenes. Furthermore, automatic processing was observed for entry-level scene categories, but not superordinate-level categories, suggesting that not all rapid categorizations are automatic. Taken together, we have demonstrated that entry-level visual categorization is an automatic and obligatory process.
人类观察者能够极其高效地对视觉刺激进行分类——这一结果表明，物体和场景分类可能是自动过程。我们通过向观察者呈现一种改良的斯特鲁普范式来检验这一假设，在该范式中，物体或场景的文字呈现在物体或场景的图像之上。文字与图像要么一致，要么不一致。观察者在忽略图像的情况下，将文字分类为物体或场景类别。对不一致图像上的文字进行分类，对物体和场景来说都需要付出代价。此外，我们观察到入门级场景类别存在自动加工，但上级类别则不然，这表明并非所有快速分类都是自动的。综上所述，我们证明了入门级视觉分类是一个自动且必然的过程。
