Evidence on the coherence between emotion and facial expression in adults from laboratory experiments is reviewed. High coherence has been found in several studies between amusement and smiling; low to moderate coherence between other positive emotions and smiling. The available evidence for surprise and disgust suggests that these emotions are accompanied by their “traditional” facial expressions, and even components of these expressions, only in a minority of cases. Evidence concerning sadness, anger, and fear is very limited. For sadness, one study (...) suggests that high emotion–expression coherence may exist in specific situations, whereas for anger and fear, the evidence points to low coherence. Insufficient emotion intensity and inhibition of facial expressions seem unable to account for the observed dissociations between emotion and facial expression. (shrink)

Most experimental studies of facial expression processing have used static stimuli (photographs), yet facial expressions in daily life are generally dynamic. In its original photographic format, the Karolinska Directed Emotional Faces (KDEF) has been frequently utilized. In the current study, we validate a dynamic version of this database, the KDEF-dyn. To this end, we applied animation between neutral and emotional expressions (happy, sad, angry, fearful, disgusted, and surprised; 1,033-ms unfolding) to 40 KDEF models, with morphing software. Ninety-six human observers categorized (...) the expressions of the resulting 240 video-clip stimuli, and automated face analysis assessed the evidence for 6 expressions and 20 facial action units (AUs) at 31 intensities. Low-level image properties (luminance, signal-to-noise ratio, etc.) and other purely perceptual factors (e.g., size, unfolding speed) were controlled. Human recognition performance (accuracy, efficiency, and confusions) patterns were consistent with prior research using static and other dynamic expressions. Automated assessment of expressions and AUs was sensitive to intensity manipulations. Significant correlations emerged between human observers’ categorization and automated classification. The KDEF-dyn database aims to provide a balance between experimental control and ecological validity for research on emotional facial expression processing. The stimuli and the validation data are available to the scientific community. (shrink)

Research on facial emotion expression has mostly focused on emotion recognition, assuming that a small number of discrete emotions is elicited and expressed via prototypical facial muscle configurations as captured in still photographs. These are expected to be recognized by observers, presumably via template matching. In contrast, appraisal theories of emotion propose a more dynamic approach, suggesting that specific elements of facial expressions are directly produced by the result of certain appraisals and predicting the facial patterns to be expected for (...) certain appraisal configurations. This approach has recently been extended to emotion perception, claiming that observers first infer individual appraisals and only then make categorical emotion judgments based on the estimated appraisal patterns, using semantic inference rules. Here we report two related studies to empirically investigate the facial action unit configurations that are used by actors to convey specific emotions in short affect bursts and to examine to what extent observers can infer a person’s emotions from the predicted facial expression configurations. The results show that 1) professional actors use many of the predicted facial action unit patterns to enact systematically specified appraisal outcomes in a realistic scenario setting, and 2) naïve observers infer the respective emotions based on highly similar facial movement configurations with a degree of accuracy comparable to earlier research findings. Based on estimates of underlying appraisal criteria for the different emotions we conclude that the patterns of facial action units identified in this research correspond largely to prior predictions and encourage further research on appraisal-driven expression and inference. (shrink)

Experience and expression are orthogonal emotion dimensions: we do not always show what we feel, nor do we always feel what we show. However, the experience and expression dimensions of emotion are rarely considered simultaneously. We propose a model outlining the intersection of goals for emotion experience and expression. We suggest that these goals may be aligned or misaligned. Our model posits these states can be separated into goals to experience and express, experience but not express, express but not experience, (...) or neither experience nor express positive and negative emotion. We contend that considering intersections between experience and expression goals will advance understanding of emotion regulation choice and success. (shrink)