Boundary 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.

Boundary extension is a classic memory illusion in which observers remember more of a scene than was presented. According to predictive-processing accounts, boundary extension reflects the integration of visual input and expectations of what is beyond a scene’s boundaries. According to normalization accounts, boundary extension rather reflects one end of a normalization process toward a scene’s typically experienced viewing distance, such that close-up views give boundary extension but distant views give boundary contraction. Here, across four experiments ( N = 125 adults), we found that boundary extension strongly depends on depth of field, as determined by the aperture settings on a camera. Photographs with naturalistic depth of field led to larger boundary extension than photographs with unnaturalistic depth of field, even when distant views were shown. We propose that boundary extension reflects a predictive mechanism with adaptive value that is strongest for naturalistic views of scenes. The current findings indicate that depth of field is an important variable to consider in the study of scene perception and memory.