“I Feel like I Am in That Place and I Would like to See More”: Aesthetic and Embodiment Components of Tourist Destination Image

Photographs of places are cognitive sources that provide the observer with a first, essential impression of a potential tourist destination, before the observer visits that place. Recent evidence suggests that aesthetic qualities of a tourist destination may affect tourists’ experience and satisfaction, contributing to their loyalty toward a destination and intention to return. Drawing upon the literature on sensorimotor processes of aesthetic experience of arts, here, we investigated whether embodiment and aesthetic qualities of landscape photos might play a role in people’s aesthetic preference and willingness to visit a tourist destination. One-hundred twenty-one participants (Mage = 22.17, SD = 6.25) completed an online survey, which asked to evaluate a series of landscapes according to subjective ratings of presence, exploration, and completion, that is the intention to explore beyond the represented place (embodiment dimensions), as well as of symmetry. Furthermore, participants rated how much they liked each destination (Liking) and how much they would like to visit that place (Tourist judgment). Convolutional neural networks (CNN) of image features (Symmetry, Variance and Self-similarity) were also analyzed to rule out the effects of these features on the 2 types of judgment. Results showed that embodiment components predicted both Liking and Tourist judgements. In contrast, neither subjective Symmetry nor CNN measures predicted any of the 2 Liking and Tourist judgements. Overall, our findings support a novel theoretical framework of tourist aesthetic judgment, whereby sensorimotor mechanisms might play a role in tourist destination choice

“I feel like I am in that place and I would like to see more”: Aesthetic and embodiment components of tourist destination image.

Photographs of places are cognitive sources that provide the observer with a first, essential impression of a potential tourist destination, before the observer visits that place. Recent evidence suggests that aesthetic qualities of a tourist destination may affect tourists’ experience and satisfaction, contributing to their loyalty toward a destination and intention to return. Drawing upon the literature on sensorimotor processes of aesthetic experience of arts, here, we investigated whether embodiment and aesthetic qualities of landscape photos might play a role in people’s aesthetic preference and willingness to visit a tourist destination. One-hundred twenty-one participants (Mage = 22.17, SD = 6.25) completed an online survey, which asked to evaluate a series of landscapes according to subjective ratings of presence, exploration, and completion, that is the intention to explore beyond the represented place (embodiment dimensions), as well as of symmetry. Furthermore, participants rated how much they liked each destination (Liking) and how much they would like to visit that place (Tourist judgment). Convolutional neural networks (CNN) of image features (Symmetry, Variance and Self-similarity) were also analyzed to rule out the effects of these features on the 2 types of judgment. Results showed that embodiment components predicted both Liking and Tourist judgements. In contrast, neither subjective Symmetry nor CNN measures predicted any of the 2 Liking and Tourist judgements. Overall, our findings support a novel theoretical framework of tourist aesthetic judgment, whereby sensorimotor mechanisms might play a role in tourist destination choice

Using Convolutional Neural Network Filters to Measure Left-Right Mirror Symmetry in Images

We propose a method for measuring symmetry in images by using filter responses from Convolutional Neural Networks (CNNs). The aim of the method is to model human perception of left/right symmetry as closely as possible. Using the Convolutional Neural Network (CNN) approach has two main advantages: First, CNN filter responses closely match the responses of neurons in the human visual system; they take information on color, edges and texture into account simultaneously. Second, we can measure higher-order symmetry, which relies not only on color, edges and texture, but also on the shapes and objects that are depicted in images. We validated our algorithm on a dataset of 300 music album covers, which were rated according to their symmetry by 20 human observers, and compared results with those from a previously proposed method. With our method, human perception of symmetry can be predicted with high accuracy. Moreover, we demonstrate that the inclusion of features from higher CNN layers, which encode more abstract image content, increases the performance further. In conclusion, we introduce a model of left/right symmetry that closely models human perception of symmetry in CD album covers