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.
Highlights
Affective pictures have become increasingly popular in psychological, neuroscientific and clinical research on emotions over the last two decades (Horvat, 2017)
We investigate to what extent the five datasets differ in their image properties and whether particular patterns of image properties can predict the ratings of specific emotions
To find out whether the set of image properties contains any information that contributes to the prediction of the affective ratings, we carried out a classification experiment using a support vector machine (SVM) with a radial basis function (RBF) kernel
Summary
Affective pictures have become increasingly popular in psychological, neuroscientific and clinical research on emotions over the last two decades (Horvat, 2017). Researchers have studied the role of affective pictures in cognitive and physiological processes such as fluency, autonomic arousal, pupil size and facial expression (Bernat et al, 2006; Bradley et al, 2008; Albrecht and Carbon, 2014; Lang et al, 1993; Lench et al, 2011; Snowden et al, 2016) Their effect on neurophysiological processes has been investigated using event-related potentials (Junghöfer et al, 2001; Olofsson et al, 2008; Weinberg and Hajcak, 2010) and fMRI (Satpute et al, 2015). Because swapping amplitude spectra between picture categories did not affect the ratings, the authors concluded that the amplitude differences were not used by the human visual system to discriminate between the affective picture categories
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