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一项物体计数任务揭示了对于典型结构化场景的复杂性估计不足。

An object numbering task reveals an underestimation of complexity for typically structured scenes.

作者信息

Carter Alex A, Kaiser Daniel

机构信息

Department of Psychology, University of York, York, UK.

Department of Mathematics and Computer Science, Physics, Geography, Justus-Liebig-Universität Gießen, Arndtstraße 2, 35392, Gießen, Germany.

出版信息

Psychon Bull Rev. 2025 Apr;32(2):760-769. doi: 10.3758/s13423-024-02577-2. Epub 2024 Sep 17.

Abstract

Our visual environments are composed of an abundance of individual objects. The efficiency with which we can parse such rich environments is remarkable. Previous work suggests that this efficiency is partly explained by grouping mechanisms, which allow the visual system to process the objects that surround us as meaningful groups rather than individual entities. Here, we show that the grouping of objects in typically and meaningfully structured environments directly relates to a reduction of perceived complexity. In an object numerosity discrimination task, we showed participants pairs of schematic scene miniatures, in which objects were structured in typical or atypical ways and asked them to judge which scene consisted of more individual objects. Critically, participants underestimated the number of objects in typically structured compared with atypically structured scenes, suggesting that grouping based on typical object configurations reduces the perceived numerical complexity of a scene. In two control experiments, we show that this overestimation also occurs when the objects are presented on textured backgrounds, and that it is specific to upright scenes, indicating that it is not related to basic visual feature differences between typically and atypically structured scenes. Together, our results suggest that our visual surroundings appear less complex to the visual system than the number of objects in them makes us believe.

摘要

我们的视觉环境由大量的单个物体组成。我们解析如此丰富环境的效率非常高。先前的研究表明,这种效率部分是由分组机制来解释的,分组机制使视觉系统能够将我们周围的物体作为有意义的组而不是单个实体来处理。在这里,我们表明,在典型且有意义的结构化环境中物体的分组直接关系到感知复杂性的降低。在一项物体数量辨别任务中,我们向参与者展示成对的示意性场景缩影,其中物体以典型或非典型的方式进行结构化,并要求他们判断哪个场景包含更多的单个物体。关键的是,与非典型结构化场景相比,参与者低估了典型结构化场景中的物体数量,这表明基于典型物体配置的分组降低了场景中感知到的数字复杂性。在两个对照实验中,我们表明当物体呈现于有纹理的背景上时也会出现这种高估现象,并且这种现象特定于直立场景,这表明它与典型和非典型结构化场景之间的基本视觉特征差异无关。总之,我们的结果表明,对于视觉系统而言,我们的视觉环境看起来比其中物体的数量让我们所认为的要没那么复杂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0290/12000177/26b2470f552d/13423_2024_2577_Fig1_HTML.jpg

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