Boundary extension is constrained by naturalistic image properties

Abstract

AbstractBoundary extension (BE) is a classical memory illusion in which observers remember more of a scene than was presented. According to predictive accounts, BE reflects the integration of visual input and expectations of what is beyond a scene’s boundaries. Alternatively, according to normalization accounts, BE reflects one end of a normalization process towards a scene’s typically-experienced viewing distance, such that BE and boundary contraction (BC) are equally common. Here, we show that BE and BC depend on depth-of-field (DOF), as determined by the aperture settings on a camera. Photographs with naturalistic DOF led to the strongest BE across a large stimulus set, while BC was primarily observed for unnaturalistic DOF. The relationship between DOF and BE was confirmed in three controlled experiments that isolated DOF from co-varying factors. In line with predictive accounts, we propose that BE is strongest for scene images that resemble day-to-day visual experience.Statement of RelevanceIn daily life, we experience a rich and continuous visual world in spite of the capacity limits of the visual system. Boundary extension (BE) is a memory illusion that sheds light on how observers compensate for such limits – that is, by filling-in the visual input with anticipatory representations of upcoming views, based on memory. However, not all images equally lead to BE. In this set of studies, we show that BE is strongest for images showing naturalistic depth-of-field, resembling human visual experience. Based on these findings, we propose that BE reflects a mechanism with adaptive value that is conditional to a scene being perceived as naturalistic. More generally, the strong reliance of a cognitive effect, such as BE, on naturalistic image properties indicates that it is imperative to use image sets that are ecologically-representative when studying the cognitive, computational, and neural mechanisms of natural vision.