Global Image Properties Predict Ratings of Affective Pictures

Abstract

Affective pictures are widely used in studies of human emotions. The objects or scenes shown in affective pictures play a pivotal role in eliciting particular emotions. However, affective processing can also be mediated by low-level perceptual features, such as local brightness contrast, color or the spatial frequency profile. In the present study, we asked whether image properties that reflect global image structure and image composition affect the rating of affective pictures. We focused on 13 global image properties that were previously associated with the esthetic evaluation of visual stimuli, and determined their predictive power for the ratings of five affective picture datasets (IAPS, GAPED, NAPS, DIRTI, and OASIS). First, we used an SVM-RBF classifier to predict high and low ratings for valence and arousal, respectively, and achieved a classification accuracy of 58-76% in this binary decision task. Second, a multiple linear regression analysis revealed that the individual image properties account for between 6 and 20% of the variance in the subjective ratings for valence and arousal. The predictive power of the image properties varies for the different datasets and type of ratings. Ratings tend to share similar sets of predictors if they correlate positively with each other. In conclusion, we obtained evidence from non-linear and linear analyses that affective pictures evoke emotions not only by what they show, but they also differ by how they show it. Whether the human visual system actually uses these perceptive cues for emotional processing remains to be investigated.

Image Valence Modulates the Processing of Low-Resolution Affective Natural Scenes

“…Each image contained more than one informative cue or animal to promote more distributed image-viewing gaze allocation. The selected images were further controlled to be comparable on both low level and global image properties (Lakens et al., 2013; Redies et al., 2020) across the valence categories. Overall, images of different valence ratings had no significant difference in image brightness, F (4, 19) = 0.68, p = .62; root mean square contrast, F (4, 19) = 3.13, p = .05; hue-saturation-value colour space, F (4, 19) <0.75, p > .57; first-order edge-orientation entropy, F (4, 19) = 1.61, p = .22; and second-order edge-orientation entropy, F (4, 19) = 1.35, p = .30; symmetry, F (4, 19) = 1.71, p = .20; and self-similarity, F (4, 19) = 1.79, p = .18.…”

“…However, these distortion effects were expression-dependent with less deterioration impact on relatively positive happy and surprise expressions in comparison with negative expressions such as angry, sad, fear, and disgust (Du & Martinez, 2011; Guo et al., 2019), suggesting that the emotional valence could influence our perceptual judgement and gaze behaviour in viewing of (at least) degraded facial expression images. Furthermore, recent computational studies have reported that images with negative and neutral valence could be classified with ∼70% accuracy by using Fourier amplitude spectrum (Rhodes et al., 2019), and different combinations of global image properties (e.g., colour values, symmetry, edge density, self-similarity, Fourier slope and sigma, and first-order and second-order edge-orientation entropies) could even predict valence and arousal ratings of affective scenes at above chance level (Redies et al., 2020), suggesting that different categories of affective scenes may have systematic differences in image statistics. Given that image distortion would disturb image statistics (Sheikh et al., 2005) and natural images of different local and global properties subsequently show different susceptibilities to the same distortion process (Röhrbein et al., 2015), it is plausible that our perception of degraded affective scenes would vary according to their emotional valence and arousal.…”

Perfect social media image posts: symmetry and contrast influence consumer response

“…Preference for symmetry is inherent and strong (Creusen et al, 2010;Tinio and Leder, 2009), and holds for both actual objects and abstract stimuli (Shepherd and Bar, 2011). Symmetry in images should positively affect viewers' preference, pleasure, or liking (Bode et al, 2017;Reber et al, 2004a;Redies et al, 2020). In marketing, mirror symmetry in brand logos has been shown to positively influence consumer liking and interest (Henderson and Cote, 1998;van der Lans et al, 2009).…”

“…Other literature suggests that processing fluency might first influence beauty judgments (Labroo and Pocheptsova, 2016;Reber et al, 2004a;Schwarz, 2018), which, in turn, may impact liking of the fluently processed stimulus. This assertion can be linked to the research in experimental aesthetics, neuroaesthetics, marketing and psychology that investigated the influence of structural properties on aesthetic response to images (e.g., Berlyne, 1974;Orth and Malkewitz, 2012;Redies et al, 2020). According to theoretical work in experimental aesthetics, any image conveys both semantic (i.e., meaning) information as well as aesthetic information that arises through its structural properties.…”

“…Unfortunately, how social media marketing images shape consumer responses remains insufficiently studied. However, researchers have conceptually posited that certain structural properties of images (e.g., simplicity, symmetry, or image contrast) directly influence aesthetic responses (i.e., aesthetic appreciation and judgments that an image is beautiful) and consumer liking of that image (Berlyne, 1974;Bloch, 1995;Hayn-Leichsenring et al, 2017;Marin et al, 2016;Redies et al, 2020;Zarzosa and Huhmann, 2019), whereas others suggest that such effects can be explained via processing fluency (Brinol et al, 2006;Reber et al, 2004a;Reber et al, 2004b;Schwarz, 2018), but treat judgments of beauty and liking as equivalent concepts Labroo and Pocheptsova, 2016). However, neuroimaging studies suggest that judgments of beauty and liking are processed in different regions of the brain (see Marin et al, 2016 for review), which suggests that they are separate but related constructs.…”