Is the preference of natural versus man-made scenes driven by bottom–up processing of the visual features of nature?

Abstract

Previous research has shown that viewing images of nature scenes can have a beneficial effect on memory, attention, and mood. In this study, we aimed to determine whether the preference of natural versus man-made scenes is driven by bottom–up processing of the low-level visual features of nature. We used participants’ ratings of perceived naturalness as well as esthetic preference for 307 images with varied natural and urban content. We then quantified 10 low-level image features for each image (a combination of spatial and color properties). These features were used to predict esthetic preference in the images, as well as to decompose perceived naturalness to its predictable (modeled by the low-level visual features) and non-modeled aspects. Interactions of these separate aspects of naturalness with the time it took to make a preference judgment showed that naturalness based on low-level features related more to preference when the judgment was faster (bottom–up). On the other hand, perceived naturalness that was not modeled by low-level features was related more to preference when the judgment was slower. A quadratic discriminant classification analysis showed how relevant each aspect of naturalness (modeled and non-modeled) was to predicting preference ratings, as well as the image features on their own. Finally, we compared the effect of color-related and structure-related modeled naturalness, and the remaining unmodeled naturalness in predicting esthetic preference. In summary, bottom–up (color and spatial) properties of natural images captured by our features and the non-modeled naturalness are important to esthetic judgments of natural and man-made scenes, with each predicting unique variance.

Cognitive and affective responses to natural scenes: Effects of low level visual properties on preference, cognitive load and eye-movements

“…They even tested their algorithm "in the wild" by incorporating it into a smart phone camera app that allowed participants to analyse any location and environment by simply using their smart phones' cameras. Given the wide replication of the positive effects of natural environments (McMahan & Estes, 2015), and researchers' ability to algorithmically model restoration responses (Kardan et al, 2015;Valtchanov & Hancock, 2015), it is logical that the underlying mechanism should be biologically based and mostly consistent across individuals around the world.…”

“…The third goal of this experiment was to investigate the proposed notion that the restorative effects of nature may be partially driven by low level visual properties of scenes (Kardan et al, 2015;Valtchanov & Hancock, 2015) that prompt a soft fascination or initial affective response, potentially through activation of the ventral visual pathway. More specifically, the goal of the current study was to examine how visual spatial frequencies, which are the building blocks of human visual perception (Olshausen & Field, 1996;Simoncelli & Olshausen, 2001), may influence restoration.…”

Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

“…The Kardan et al (2015a) research focused on low-level features (as originally defined by Berman et al, 2014), where, in an experimental setting, 52 research participants (26 female, mean age = 21.1) provided aesthetic preference ratings, using a 7-point rating scale for 307 images (naturalness ratings were similarly obtained from Berman et al, 2014). Participant aesthetic preference ratings were then modeled using low-level visual features as predictors.…”

Quantifying Urban Surroundings Using Deep Learning Techniques: A New Proposal

“…The presence of naturalness in the scene is associated with the restoration of attention [46,47], stress recovery [48], satisfaction, and well-being [49]. Naturalness has been studied and shown to impart long-term psychophysical health benefits.…”