2019/03/04 by Been Kim, Emily Reif, Kim, Been +7
Computer Science · Neuroscience · #Aesthetic Perception and Analysis #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Visual Attention and Saliency Detection #Visual perception and processing mechanisms
paper · pdf · doi:10.48550/arxiv.1903.01069
openalex publication_date 2019/03/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Gestalt laws of perceptual organization, which describe how visual elements in an image are grouped and interpreted, have traditionally been thought of as innate despite their ecological validity. We use deep-learning methods to investigate whether natural scene statistics might be sufficient to derive the Gestalt laws. We examine the law of closure, which asserts that human visual perception tends to "close the gap" by assembling elements that can jointly be interpreted as a complete figure or object. We demonstrate that a state-of-the-art convolutional neural network, trained to classify natural images, exhibits closure on synthetic displays of edge fragments, as assessed by similarity of internal representations. This finding provides support for the hypothesis that the human perceptual system is even more elegant than the Gestaltists imagined: a single law---adaptation to the statistical structure of the environment---might suffice as fundamental.