Li Bao, Tong Li, Zhang Chi, Chen Panpan, Cao Long, Gao Hui, Yu ZiYa, Wang LinYuan, Yan Bin
Henan Key Laboratory of Imaging and Intelligent Processing, Information Engineering University, Zhengzhou, 450000, China.
Sci Data. 2025 Jul 1;12(1):1111. doi: 10.1038/s41597-025-05414-w.
In everyday environments, partially occluded objects are more common than fully visible ones. Despite their visual incompleteness, the human brain can reconstruct these objects to form coherent perceptual representations, a phenomenon referred to as amodal completion. However, current computer vision systems still struggle to accurately infer the hidden portions of occluded objects. While the neural mechanisms underlying amodal completion have been partially explored, existing findings often lack consistency, likely due to limited sample sizes and varied stimulus materials. To address these gaps, we introduce a novel fMRI dataset,the Occluded Image Interpretation Dataset (OIID), which captures human perception during image interpretation under different levels of occlusion. This dataset includes fMRI responses and behavioral data from 65 participants. The OIID enables researchers to identify the brain regions involved in processing occluded images and examines individual differences in functional responses. Our work contributes to a deeper understanding of how the human brain interprets incomplete visual information and offers valuable insights for advancing both theoretical research and related practical applications in amodal completion fields.
在日常环境中，部分遮挡的物体比完全可见的物体更为常见。尽管其视觉信息不完整，但人类大脑能够重构这些物体，形成连贯的感知表征，这一现象被称为非模态完成。然而，当前的计算机视觉系统仍难以准确推断被遮挡物体的隐藏部分。虽然非模态完成背后的神经机制已得到部分探索，但现有研究结果往往缺乏一致性，这可能是由于样本量有限和刺激材料多样所致。为填补这些空白，我们引入了一个新颖的功能磁共振成像（fMRI）数据集，即遮挡图像解释数据集（OIID），该数据集记录了不同遮挡程度下图像解释过程中的人类感知。这个数据集包含了65名参与者的fMRI反应和行为数据。OIID使研究人员能够识别参与处理遮挡图像的脑区，并研究功能反应中的个体差异。我们的工作有助于更深入地理解人类大脑如何解释不完整的视觉信息，并为推进非模态完成领域的理论研究和相关实际应用提供有价值的见解。
