Visual crowding, the impairment of object recognition in peripheral vision due to flanking objects, has generally been studied using simple stimuli on blank backgrounds. While crowding is widely assumed to occur in natural scenes, it has not been shown rigorously yet. Given that scene contexts can facilitate object recognition, crowding effects may be dampened in real-world scenes. Therefore, this study investigated crowding using objects in computer-generated real-world scenes. In two experiments, target objects were presented with four flanker objects placed uniformly around the target. Previous research indicates that crowding occurs when the distance between the target and flanker is approximately less than half the retinal eccentricity of the target. In each image, the spacing between the target and flanker objects was varied considerably above or below the standard (0.5) threshold to either suppress or facilitate the crowding effect. Experiment 1 cued the target location and then briefly flashed the scene image before participants could move their eyes. Participants then selected the target object’s category from a 15-alternative forced choice response set (including all objects shown in the scene). Experiment 2 used eye tracking to ensure participants were centrally fixating at the beginning of each trial and showed the image for the duration of the participant’s fixation. Both experiments found object recognition accuracy decreased with smaller spacing between targets and flanker objects. Thus, this study rigorously shows crowding of objects in semantically consistent real-world scenes.

视觉拥挤，即由于侧翼物体而导致的周边视觉物体识别受损，通常是使用空白背景上的简单刺激来研究的。虽然人们普遍认为拥挤发生在自然场景中，但尚未得到严格证明。鉴于场景上下文可以促进对象识别，现实世界场景中的拥挤效应可能会减弱。因此，本研究使用计算机生成的现实世界场景中的对象来调查拥挤情况。在两个实验中，目标对象呈现出均匀放置在目标周围的四个侧翼对象。先前的研究表明，当目标和侧翼之间的距离大约小于目标视网膜偏心率的一半时，就会发生拥挤。在每幅图像中，目标和侧翼物体之间的间距在标准 (0.5) 阈值之上或之下有很大变化，以抑制或促进拥挤效应。实验 1 提示目标位置，然后在参与者移动眼睛之前短暂闪烁场景图像。然后，参与者从 15 个备选强制选择响应集中选择目标对象的类别（包括场景中显示的所有对象）。实验 2 使用眼动追踪来确保参与者在每次试验开始时集中注视，并在参与者注视期间显示图像。两项实验都发现，随着目标与侧翼物体之间的间距变小，物体识别准确度也会降低。因此，这项研究严格地展示了语义一致的现实世界场景中对象的拥挤情况。