Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000 Grenoble, France.
Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, 38000 Grenoble, France; Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France.
Vision Res. 2020 May;170:60-72. doi: 10.1016/j.visres.2020.02.008. Epub 2020 Apr 4.
Studies on scene perception have shown that the rapid extraction of low spatial frequencies (LSF) allows a coarse parsing of the scene, prior to the analysis of high spatial frequencies (HSF) containing details. Many studies suggest that scene gist recognition can be achieved with only the low resolution of peripheral vision. Our study investigated the advantage of peripheral vision on central vision during a scene categorization task (indoor vs. outdoor). In Experiment 1, we used large scene photographs from which we built one central disk and four circular rings of different eccentricities. The central disk either contained or not an object semantically related to the scene category. Results showed better categorization performances for the peripheral rings, despite the presence of an object in central vision that was semantically related to the scene category that significantly improved categorization performances. In Experiment 2, the central disk and rings were assembled from Central to Peripheral vision (CtP sequence) or from Peripheral to Central vision (PtC sequence). Results revealed better performances for PtC than CtP sequences, except when no central object was present under rapid categorization constraints. As Experiment 3 suggested that the PtC advantage was not explained by a reduction of the visibility of the object in the central disk by the surrounding peripheral rings (CtP sequence), results are interpreted in the context of a predominant coarse-to-fine processing during scene categorization, with greater efficiency and utility of coarse peripheral vision relative to fine central vision during rapid scene categorization.
场景感知研究表明,快速提取低空间频率(LSF)可以在分析包含细节的高空间频率(HSF)之前,对场景进行粗略解析。许多研究表明,仅通过周边视觉的低分辨率就可以实现场景概要识别。我们的研究调查了在场景分类任务(室内与室外)中,周边视觉相对于中央视觉的优势。在实验 1 中,我们使用了大尺寸的场景照片,从中构建了一个中央圆盘和四个不同偏心度的圆形环。中央圆盘要么包含与场景类别语义相关的物体,要么不包含。结果表明,尽管中央视觉中存在与场景类别语义相关的物体,但周边环的分类表现更好,这显著提高了分类表现。在实验 2 中,中央圆盘和环从中央到周边视觉(CtP 序列)或从周边到中央视觉(PtC 序列)组装。结果显示,PtC 序列的表现优于 CtP 序列,除非在快速分类约束下中央没有物体。由于实验 3 表明 PtC 优势不能用中央盘周围的周边环降低中央物体的可见度(CtP 序列)来解释,因此结果是在场景分类过程中占主导地位的粗到细处理的背景下进行解释的,在快速场景分类中,相对于精细的中央视觉,粗的周边视觉具有更高的效率和实用性。