Object Vision in a Structured World. Issue 8 (August 2019)

• Record Type: Journal Article
• Authors: Kaiser, Daniel; Quek, Genevieve L.; Cichy, Radoslaw M.; Peelen, Marius V.
• Is Part Of: Trends in cognitive sciences. Volume 23:Issue 8(2019)
• Page Start: 672
• Page End: 685
• Publication Date: 2019-08
• Subjects: object perception -- real-world structure -- positional regularities -- high-level vision; Cognitive science -- Periodicals; Cognitive neuroscience -- Periodicals; 153.05
• DOI: 10.1016/j.tics.2019.04.013
• Languages: English
• ISSNs: 1364-6613

Abstract

Abstract : In natural vision, objects appear at typical locations, both with respect to visual space (e.g., an airplane in the upper part of a scene) and other objects (e.g., a lamp above a table). Recent studies have shown that object vision is strongly adapted to such positional regularities. In this review we synthesize these developments, highlighting that adaptations to positional regularities facilitate object detection and recognition, and sharpen the representations of objects in visual cortex. These effects are pervasive across various types of high-level content. We posit that adaptations to real-world structure collectively support optimal usage of limited cortical processing resources. Taking positional regularities into account will thus be essential for understanding efficient object vision in the real world. Highlights: The characteristic spatial distribution of individual objects impacts profoundly on object processing in the human brain. Neural representations are sharper and perceptual sensitivity is increased when objects appear at retinal locations corresponding to their typical location in the world (e.g., an airplane in the upper visual field). The characteristic positioning of objects relative to one another also systematically influences object processing. When multiobject displays are arranged in their typical relative positions (e.g., a lamp above a table), the visual system represents them as groups, allowing objects to be more easily detected, recognized, and memorized. Neural adaptations to natural scene structure enable the visual brain to optimally represent the large number of objects contained in real-world environments, thereby simplifying the neural code for scene analysis.