A story superiority effect for disgust, fear, embarrassment, and pride

Identifying Emotional Expressions: Children's Reasoning About Pretend Emotions of Sadness and Anger

This study aims to further understand children’s capacity to identify and reason about pretend emotions by analyzing which sources of information they take into account when interpreting emotions simulated in pretend play contexts. A total of 79 children aged 3 to 8 participated in the final sample of the study. They were divided into the young group (ages 3 to 5) and the older group (6 to 8). The children were administered a facial emotion recognition task, a pretend emotions task, and a non-verbal cognitive ability test. In the pretend emotions task, the children were asked whether the protagonist of silent videos, who was displaying pretend emotions (pretend anger and pretend sadness), was displaying a real or a pretend emotion, and to justify their answer. The results show significant differences in the children’s capacity to identify and justify pretend emotions according to age and type of emotion. The data suggest that young children recognize pretend sadness, but have more difficulty detecting pretend anger. In addition, children seem to find facial information more useful for the detection of pretend sadness than pretend anger, and they more often interpret the emotional expression of the characters in terms of pretend play. The present research presents new data about the recognition of negative emotional expressions of sadness and anger and the type of information children take into account to justify their interpretation of pretend emotions, which consists not only in emotional expression but also contextual information.

Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements

It is commonly assumed that a person's emotional state can be readily inferred from his or her facial movements, typically called emotional expressions or facial expressions. This assumption influences legal judgments, policy decisions, national security protocols, and educational practices; guides the diagnosis and treatment of psychiatric illness, as well as the development of commercial applications; and pervades everyday social interactions as well as research in other scientific fields such as artificial intelligence, neuroscience, and computer vision. In this article, we survey examples of this widespread assumption, which we refer to as the common view, and we then examine the scientific evidence that tests this view, focusing on the six most popular emotion categories used by consumers of emotion research: anger, disgust, fear, happiness, sadness, and surprise. The available scientific evidence suggests that people do sometimes smile when happy, frown when sad, scowl when angry, and so on, as proposed by the common view, more than what would be expected by chance. Yet how people communicate anger, disgust, fear, happiness, sadness, and surprise varies substantially across cultures, situations, and even across people within a single situation. Furthermore, similar configurations of facial movements variably express instances of more than one emotion category. In fact, a given configuration of facial movements, such as a scowl, often communicates something other than an emotional state. Scientists agree that facial movements convey a range of information and are important for social communication, emotional or otherwise. But our review suggests an urgent need for research that examines how people actually move their faces to express emotions and other social information in the variety of contexts that make up everyday life, as well as careful study of the mechanisms by which people perceive instances of emotion in one another. We make specific research recommendations that will yield a more valid picture of how people move their faces to express emotions and how they infer emotional meaning from facial movements in situations of everyday life. This research is crucial to provide consumers of emotion research with the translational information they require.

Tracking the recognition of static and dynamic facial expressions of emotion across the life span

The effective transmission and decoding of dynamic facial expressions of emotion is omnipresent and critical for adapted social interactions in everyday life. Thus, common intuition would suggest an advantage for dynamic facial expression recognition (FER) over the static snapshots routinely used in most experiments. However, although many studies reported an advantage in the recognition of dynamic over static expressions in clinical populations, results obtained from healthy participants are contrasted. To clarify this issue, we conducted a large cross-sectional study to investigate FER across the life span in order to determine if age is a critical factor to account for such discrepancies. More than 400 observers (age range 5-96) performed recognition tasks of the six basic expressions in static, dynamic, and shuffled (temporally randomized frames) conditions, normalized for the amount of energy sampled over time. We applied a Bayesian hierarchical step-linear model to capture the nonlinear relationship between age and FER for the different viewing conditions. Although replicating the typical accuracy profiles of FER, we determined the age at which peak efficiency was reached for each expression and found greater accuracy for most dynamic expressions across the life span. This advantage in the elderly population was driven by a significant decrease in performance for static images, which was twice as large as for the young adults. Our data posit the use of dynamic stimuli as being critical in the assessment of FER in the elderly population, inviting caution when drawing conclusions from the sole use of static face images to this aim.