The Complex Affective Scene Set (COMPASS): Solving the Social Content Problem in Affective Visual Stimulus Sets

The way we look at an image or a webpage can reveal personality traits

Personality is a central concept and a cross-domain explanatory factor in psychology to characterize and differentiate individuals. Surprisingly, among the many studies on oculomotor behavior, only a few have investigated how personality influences the exploration of a visual stimulus. Due to the limited number of existing studies, it is still uncertain if markers of personality in eye movements are always observable in eye movements across various exploration contexts. Here, introducing a novel concept of gaze-based signatures of personality, we used visual exploration metrics to detect personality signatures across various exploration contexts (visual search and free-viewing on images and webpages) in 91 participants. Personality data were collected as in the reference paper that validated the French version of the Big Five Inventory. Linear regression analyses demonstrated that while Extraversion and Openness to Experience did not correlate with any particular exploration metric, the other three traits-Conscientiousness, Agreeableness, and Neuroticism-correlated robustly with all exploration metrics in different visual exploration contexts. Our study provides evidence for the capture of the gaze-based signature of personality from very brief eye movement recordings.

Effects of Voluntary Attention on Social and Non-Social Emotion Perception

Existing studies have focused on the effect of emotion on attention, and the role of attention on emotion has largely been underestimated. To further determine the mechanisms underlying the role of attention on emotion, the present study explored the effects of voluntary attention on both social and non-social aspects of emotional perception. Participants were 25 college students who completed the Rapid Serial Visual Prime (RSVP) paradigm. In this study, the selection rates of participants' emotional intensity, pleasure and distinctness perception of the pictures were measured. The results showed as following: (a) The cued condition selection rate was higher than the non-cued condition in the evaluation of non-social emotional intensity perception and pleasure perception, (b) In the evaluation of social emotional intensity and pleasure perception, there was no significant difference in the selection rate between the cued and non-cued condition, (c) The cued condition selection rate was higher than the non-cued condition in the perception of non-social positive emotional intensity and social negative emotional distinctness. The novel findings of this study revealed that the effect of voluntary attention on emotional perception is influenced not only by emotional valence but also by emotional sociality.

Language Is a Unique Context for Emotion Perception

Access to words used to label emotion concepts (e.g., "disgust") facilitates perceptions of facial muscle movements as instances of specific emotions (see Lindquist & Gendron, 2013). However, it remains unclear whether the effect of language on emotion perception is unique or whether it is driven by language's tendency to evoke situational context. In two studies, we used a priming and perceptual matching task to test the hypothesis that the effect of language on emotion perception is unique to that of situational context. We found that participants were more accurate to perceptually match facial portrayals of emotion after being primed with emotion labels as compared to situational context or control stimuli. These findings add to growing evidence that language serves as context for emotion perception and demonstrates for the first time that the effect of language on emotion perception is not merely a consequence of evoked situational context.

EEG-Based Estimation on the Reduction of Negative Emotions for Illustrated Surgical Images

Electroencephalogram (EEG) biosignals are widely used to measure human emotional reactions. The recent progress of deep learning-based classification models has improved the accuracy of emotion recognition in EEG signals. We apply a deep learning-based emotion recognition model from EEG biosignals to prove that illustrated surgical images reduce the negative emotional reactions that the photographic surgical images generate. The strong negative emotional reactions caused by surgical images, which show the internal structure of the human body (including blood, flesh, muscle, fatty tissue, and bone) act as an obstacle in explaining the images to patients or communicating with the images with non-professional people. We claim that the negative emotional reactions generated by illustrated surgical images are less severe than those caused by raw surgical images. To demonstrate the difference in emotional reaction, we produce several illustrated surgical images from photographs and measure the emotional reactions they engender using EEG biosignals; a deep learning-based emotion recognition model is applied to extract emotional reactions. Through this experiment, we show that the negative emotional reactions associated with photographic surgical images are much higher than those caused by illustrated versions of identical images. We further execute a self-assessed user survey to prove that the emotions recognized from EEG signals effectively represent user-annotated emotions.