Molecular Mind Lab, IMT School for Advanced Studies Lucca, Lucca, 55100 Italy.
Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Twin Cities, Minneapolis, MN, 55455.
eNeuro. 2018 Jun 26;5(3). doi: 10.1523/ENEURO.0075-18.2018. eCollection 2018 May-Jun.
One of the major challenges in visual neuroscience is represented by foreground-background segmentation. Data from nonhuman primates show that segmentation leads to two distinct, but associated processes: the enhancement of neural activity during figure processing (i.e., foreground enhancement) and the suppression of background-related activity (i.e., background suppression). To study foreground-background segmentation in ecological conditions, we introduce a novel method based on parametric modulation of low-level image properties followed by application of simple computational image-processing models. By correlating the outcome of this procedure with human fMRI activity, measured during passive viewing of 334 natural images, we produced easily interpretable "correlation images" from visual populations. Results show evidence of foreground enhancement in all tested regions, from V1 to lateral occipital complex (LOC), while background suppression occurs in V4 and LOC only. Correlation images derived from V4 and LOC revealed a preserved spatial resolution of foreground textures, indicating a richer representation of the salient part of natural images, rather than a simplistic model of object shape. Our results indicate that scene segmentation occurs during natural viewing, even when individuals are not required to perform any particular task.
视觉神经科学的主要挑战之一是前景-背景分割。来自非人类灵长类动物的数据表明，分割会导致两个不同但相关的过程：在图形处理过程中增强神经活动（即前景增强）和抑制与背景相关的活动（即背景抑制）。为了在生态条件下研究前景-背景分割，我们引入了一种基于低水平图像属性参数调制的新方法，然后应用简单的计算图像处理模型。通过将该过程的结果与人类 fMRI 活动相关联，这些活动是在被动观看 334 张自然图像时测量的，我们从视觉群体中产生了易于解释的“相关图像”。结果表明，在所有测试的区域（从 V1 到外侧枕叶复合体（LOC））都有前景增强的证据，而背景抑制仅发生在 V4 和 LOC。来自 V4 和 LOC 的相关图像显示出前景纹理的空间分辨率得到了保留，这表明对自然图像的显著部分有更丰富的表示，而不是对物体形状的简单模型。我们的结果表明，即使个体不需要执行任何特定任务，在自然观看时也会发生场景分割。
