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Our first impression of others is highly influenced by their facial appearance. However, the perception and evaluation of faces is not only guided by internal features such as facial expressions, but also highly dependent on contextual information such as secondhand information (verbal descriptions) about the target person. To investigate the time course of contextual influences on cortical face processing, event-related brain potentials were investigated in response to neutral faces, which were preceded by brief verbal descriptions containing cues of affective valence (negative, neutral, positive) and self-reference (self-related vs. other-related). ERP analysis demonstrated that early and late stages of face processing are enhanced by negative and positive as well as self-relevant descriptions, although faces per se did not differ perceptually. Affective ratings of the faces confirmed these findings. Altogether, these results demonstrate for the first time both on an electrocortical and behavioral level how contextual information modifies early visual perception in a top-down manner.

Perception and evaluation of facial expressions are known to be heavily modulated by emotional features of contextual information. Such contextual effects, however, might also be driven by non-emotional aspects of contextual information, an interaction of emotional and non-emotional factors, and by the observers’ inherent traits. Therefore, we sought to assess whether contextual information about self-reference in addition to information about valence influences the evaluation and neural processing of neutral faces. Furthermore, we investigated whether social anxiety moderates these effects. In the present functional magnetic resonance imaging (fMRI) study, participants viewed neutral facial expressions preceded by a contextual sentence conveying either positive or negative evaluations about the participant or about somebody else. Contextual influences were reflected in rating and fMRI measures, with strong effects of self-reference on brain activity in the medial prefrontal cortex and right fusiform gyrus. Additionally, social anxiety strongly affected the response to faces conveying negative, self-related evaluations as revealed by the participants’ rating patterns and brain activity in cortical midline structures and regions of interest in the left and right middle frontal gyrus. These results suggest that face perception and processing are highly individual processes influenced by emotional and non-emotional aspects of contextual information and further modulated by individual personality traits.

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The influence of positive or negative expectations on clinical outcomes such as pain relief or motor performance in patients and healthy participants has been extensively investigated for years. Such research promises potential benefit for patient treatment by deliberately using expectations as means to stimulate endogenous regulation processes. Especially regarding recent interest and controversies revolving around cognitive enhancement, the question remains whether mere expectancies might also yield enhancing or impairing effects in the cognitive domain, i.e., can we improve or impair cognitive performance simply by creating a strong expectancy in participants about their performance? Moreover, previous literature suggests that especially subjective perception is highly susceptible to expectancy effects, whereas objective measures can be affected in certain domains, but not in others. Does such a dissociation of objective measures and subjective perception also apply to cognitive placebo and nocebo effects? In this study, we sought to investigate whether placebo and nocebo effects can be evoked in cognitive tasks, and whether these effects influence objective and subjective measures alike. To this end, we instructed participants about alleged effects of different tone frequencies (high, intermediate, low) on brain activity and cognitive functions. We paired each tone with specific success rates in a Flanker task paradigm as a preliminary conditioning procedure, adapted from research on placebo hypoalgesia. In a subsequent test phase, we measured reaction times and success rates in different expectancy conditions (placebo, nocebo, and control) and then asked participants how the different tone frequencies affected their performance. Interestingly, we found no effects of expectation on objective measures, but a strong effect on subjective perception, i.e., although actual performance was not affected by expectancy, participants strongly believed that the placebo tone frequency improved their performance.

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Figure 1. Aesthetically pleasing images from our derived scores. The complex matter of aesthetics depends on many factors, e.g., visual appearance, composition, content, or style, and makes it almost impossible to directly compare all images if they are similarly beautiful. AbstractRating how aesthetically pleasing an image appears is a highly complex matter and depends on a large number of different visual factors. Previous work has tackled the aesthetic rating problem by ranking on a 1-dimensional rating scale, e.g., incorporating handcrafted attributes. In this paper, we propose a rather general approach to automatically map aesthetic pleasingness with all its complexity into an "aesthetic space" to allow for a highly fine-grained resolution. In detail, making use of deep learning, our method directly learns an encoding of a given image into this highdimensional feature space resembling visual aesthetics.Additionally to the mentioned visual factors, differences in personal judgments have a large impact on the likeableness of a photograph. Nowadays, online platforms allow users to "like" or favor certain content with a single click. To incorporate a huge diversity of people, we make use of such multi-user agreements and assemble a large data set of 380K images (AROD) with associated meta information and derive a score to rate how visually pleasing a given photo is. We validate our derived model of aesthetics in a user study. Further, without any extra data labeling or handcrafted features, we achieve state-of-the art accuracy on the AVA benchmark data set. Finally, as our approach is able to predict the aesthetic quality of any arbitrary image or video, we demonstrate our results on applications for resorting photo collections, capturing the best shot on mobile devices and aesthetic key-frame extraction from videos.