title: The perception of naturalness correlates with low-level visual features of environmental scenes.
abstract: Previous research has shown that interacting with natural environments vs. more urban or built environments can have salubrious psychological effects, such as improvements in attention and memory. Even viewing pictures of nature vs. pictures of built environments can produce similar effects. A major question is: What is it about natural environments that produces these benefits? Problematically, there are many differing qualities between natural and urban environments, making it difficult to narrow down the dimensions of nature that may lead to these benefits. In this study, we set out to uncover visual features that related to individuals' perceptions of naturalness in images. We quantified naturalness in two ways: first, implicitly using a multidimensional scaling analysis and second, explicitly with direct naturalness ratings. Features that seemed most related to perceptions of naturalness were related to the density of contrast changes in the scene, the density of straight lines in the scene, the average color saturation in the scene and the average hue diversity in the scene. We then trained a machine-learning algorithm to predict whether a scene was perceived as being natural or not based on these low-level visual features and we could do so with 81% accuracy. As such we were able to reliably predict subjective perceptions of naturalness with objective low-level visual features. Our results can be used in future studies to determine if these features, which are related to naturalness, may also lead to the benefits attained from interacting with nature.
authors.affiliation: Department of Psychology, The University of Chicago, Chicago, IL, United States of America.
authors.forename: Marc G
authors.initials: MG
authors.lastname: Berman
authors.affiliation: Department of Psychology, New Mexico State University, Las Cruces, NM, United States of America.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Hout
authors.affiliation: Department of Psychology, The University of Chicago, Chicago, IL, United States of America.
authors.forename: Omid
authors.initials: O
authors.lastname: Kardan
authors.affiliation: Department of Natural Resources and Environment, The University of Michigan, Ann Arbor, MI, United States of America.
authors.forename: MaryCarol R
authors.initials: MR
authors.lastname: Hunter
authors.affiliation: Department of Psychology, The University of South Carolina, Columbia, SC, United States of America.
authors.forename: Grigori
authors.initials: G
authors.lastname: Yourganov
authors.affiliation: Department of Psychology, The University of South Carolina, Columbia, SC, United States of America.
authors.forename: John M
authors.initials: JM
authors.lastname: Henderson
authors.affiliation: Department of Psychology, The University of South Carolina, Columbia, SC, United States of America.
authors.forename: Taylor
authors.initials: T
authors.lastname: Hanayik
authors.affiliation: Department of Psychology, The University of South Carolina, Columbia, SC, United States of America.
authors.forename: Hossein
authors.initials: H
authors.lastname: Karimi
authors.affiliation: Department of Psychology, The University of Michigan, Ann Arbor, MI, United States of America.
authors.forename: John
authors.initials: J
authors.lastname: Jonides
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002947:Cities; D003118:Color Perception; D004777:Environment; D005260:Female; D006801:Humans; D008297:Male; D065855:Spatial Processing; D014796:Visual Perception; D055815:Young Adult