An fMRI Dataset on Occluded Image Interpretation for Human Amodal Completion Research - Bao Li; Li Tong; Chi Zhang; Panpan Chen; Long Cao; Hui Gao; ZiYa Yu; LinYuan Wang; Bin Yan (Scientific Data)
Abstract 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.