"Detecting and identifying targets in cluttered scenes is critical for successful interactions in the complex and dynamic environments we inhabit. Despite the ease and speed with which we recognize objects, visual recognition entails the computationally challenging task of binding relevant features together for the perception of coherent meaningful objects. Most work on visual integration focuses on spatial binding processes. However, detecting dynamic objects in cluttered scenes entails storage of image fragments in memory and integration across time. Here we propose to investigate the mechanisms that mediate integration across space and time. We will exploit the contour slit-viewing paradigm: that is, an object moving behind a narrow slit is still perceived as a whole. In task 1, we will characterize quantitatively the ability for slit-viewing perception and generate a computational model describing the principles that guide spatiotemporal integration. In task 2, we will test for similarities and differences in the neural substrates underlying spatial and spatiotemporal integration using conventional GLM and advanced multivariate analyses. In task 3, we will test for cortical regions that mediate perceptual deformations under slit-viewing. Task 4 will use precise retinotopic mapping to investigate the memory store processes engaged in spatiotemporal integration. Finally, in task 5 we will employ simultaneous EEG-fMRI recordings to examine the dynamic information processing of spatiotemporal integration and the interactions within the cortical circuit involved. The proposed work programme will devise a new paradigm and take advantage of recent advances in multimodal recordings (EEG-fMRI) and advanced computational analysis methods to explore the neural mechanisms of spatiotemporal integration for the first time. This interdisciplinary work will offer new insights into the neural machinery that underlies our ability to recognize dynamic objects in cluttered scenes."