Predictive Processing of Scene Layout Depends on Naturalistic Depth of Field

Abstract

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.