Department of Psychology, Johann Wolfgang Goethe-Universität, Theodor-W.-Adorno-Platz 6, 60323, Frankfurt Am Main, Germany.
Sci Rep. 2023 Apr 11;13(1):5912. doi: 10.1038/s41598-023-32385-y.
It usually only takes a single glance to categorize our environment into different scene categories (e.g. a kitchen or a highway). Object information has been suggested to play a crucial role in this process, and some proposals even claim that the recognition of a single object can be sufficient to categorize the scene around it. Here, we tested this claim in four behavioural experiments by having participants categorize real-world scene photographs that were reduced to a single, cut-out object. We show that single objects can indeed be sufficient for correct scene categorization and that scene category information can be extracted within 50 ms of object presentation. Furthermore, we identified object frequency and specificity for the target scene category as the most important object properties for human scene categorization. Interestingly, despite the statistical definition of specificity and frequency, human ratings of these properties were better predictors of scene categorization behaviour than more objective statistics derived from databases of labelled real-world images. Taken together, our findings support a central role of object information during human scene categorization, showing that single objects can be indicative of a scene category if they are assumed to frequently and exclusively occur in a certain environment.
通常只需要一眼就能将我们的环境分为不同的场景类别（例如厨房或高速公路）。有人提出，物体信息在这个过程中起着至关重要的作用，甚至有一些观点声称，仅识别单个物体就足以对其周围的场景进行分类。在这里，我们通过让参与者对仅包含一个裁剪物体的现实世界场景照片进行分类，在四个行为实验中测试了这一说法。结果表明，单个物体确实足以进行正确的场景分类，并且可以在物体呈现后的 50 毫秒内提取场景类别信息。此外，我们确定了目标场景类别的物体频率和特异性是人类场景分类的最重要的物体属性。有趣的是，尽管特异性和频率是根据统计学定义的，但与从标记的现实世界图像数据库中得出的更客观的统计数据相比，人类对这些属性的评分更能预测场景分类行为。总的来说，我们的研究结果支持在人类场景分类中物体信息起核心作用的观点，表明如果单个物体被认为经常且专门出现在某个环境中，那么它们就可以作为场景类别的指示物。
