Spatiotemporal Form Integration: Unified Surface Perception In a World Fragmented In Space and Time

Objects in the world are often occluded or in motion and visible fragments of objects are revealed at different times and locations in space. To form coherent representations of the surfaces of these objects, the visual system must integrate locally detected form information across space and time--a process I refer to as spatiotemporal form integration (SFI). Despite its importance, relatively little is known about SFI processes compared to the mechanisms supporting spatial integration. Specifically, though previous research proposes that SFI relies on mechanisms that maintain persistent form representations, the durations over which these representations can persist and be integrated with subsequent form information is unknown. Additionally, though the spatial constraints of positional updating that support translating object percepts have been previously described, the spatial and temporal parameters supporting rigidly rotating SFI surface representations remain uncharacterized. Importantly, virtually nothing is known about the neural correlates of SFI processes.;The experiments in this dissertation were designed to address fundamental questions in the literature concerning how the visual system integrates visual surfaces despite the pervasive real-world problems of motion and occlusion. I first identify spatial and temporal constraints within which SFI can support representations of stationary and rigidly rotating objects. Using the parameters that support robust SFI percepts, I identified regions of visual cortex that support SFI using fMRI. Specifically, areas V3, V3AB, V4, and LOC support static surface representations completed by SFI. In addition, demonstrate that hMT+ and KO support SFI representations of rigidly rotating surfaces. In addition, I applied high-density EEG to determine the neural time course of SFI. The results suggest that surfaces completed by SFI take slightly longer to integrate compared to those defined by real contours. Finally, I demonstrate that top-down mechanisms related to prior knowledge play an important role in SFI processes. These findings lend insight into limits under which the visual system is capable of forming surface representations from the integration of transient, piecemeal form information across space and time and elucidate neural correlates of SFI.