Jumping for Joy: The Importance of the Body and of Dynamics in the Expression and Recognition of Positive Emotions

Age-of-actor effects in body expression recognition of children

Investigations of developmental trajectories for emotion recognition suggest that both face- and body expression recognition increases rapidly in early childhood and reaches adult levels of performance near the age of ten. So far, little is known about whether children's ability to recognise body expressions is influenced by the age of the person they are observing. This question is investigated here by presenting 119 children and 42 young adults with videos of children, young adults and older adults expressing emotions with their whole body. The results revealed an own-age advantage for children, reflected in adult-level accuracy for videos of children for most expressions but reduced accuracy for videos of older adults. Children's recognition of older adults' expressions was not correlated with children's estimated amount of contact with older adults. Support for potential influences of social biases on performance measures was minimal. The own-age advantage was explained in terms of children's reduced familiarity with body expressions of older adults due to aging related changes in the kinematics characteristics of movements and potentially due to stronger embodiment of other children's bodily movements.

Perceiving dynamic emotions expressed simultaneously in the face and body minimizes perceptual differences between young and older adults

OBJECTIVES

It is commonly argued that older adults show difficulties in standardized tasks of emotional expression perception, yet most previous works relied on classic sets of static, decontextualized, and stereotypical facial expressions. In real-life, facial expressions are dynamic and embedded in a rich context, two key factors that may aid emotion perception. Specifically, body language provides important affective cues that may disambiguate facial movements.

METHOD

We compared emotion perception of dynamic faces, bodies, and their combination, in a sample of older (age 60-83, n=126) and young (age 18-30, n=124) adults. We used the Geneva Multimodal Emotion Portrayals (GEMEP) set, which includes a full view of expressers' faces and bodies, displaying a diverse range of positive and negative emotions, portrayed dynamically and holistically in a non-stereotypical, unconstrained manner. Critically, we digitally manipulated the dynamic cue such that perceivers viewed isolated faces (without bodies), isolated bodies (without faces), or faces with bodies.

RESULTS

Older adults showed better perception of positive and negative dynamic facial expressions, while young adults showed better perception of positive isolated dynamic bodily expressions. Importantly, emotion perception of faces with bodies was comparable across ages.

DISCUSSION

Dynamic emotion perception in young and older adults may be more similar than previously assumed, especially when the task is more realistic and ecological. Our results emphasize the importance of contextualized and ecological tasks in emotion perception across ages.

The Qingdao Preschooler Facial Expression Set: Acquisition and Validation of Chinese Children's Facial Emotion Stimuli

Traditional research on emotion-face processing has primarily focused on the expression of basic emotions using adult emotional face stimuli. Stimulus sets featuring child faces or emotions other than basic emotions are rare. The current study describes the acquisition and evaluation of the Qingdao Preschooler Facial Expression (QPFE) set, a facial stimulus set with images featuring 54 Chinese preschoolers' emotion expressions. The set includes 712 standardized color photographs of six basic emotions (joy, fear, anger, sadness, surprise, and disgust), five discrete positive emotions (interest, contentment, relief, pride, and amusement), and a neutral expression. The validity of the pictures was examined based on 43 adult raters' online evaluation, including agreement between designated emotions and raters' labels, as well as intensity and representativeness scores. Overall, these data should contribute to the developmental and cross-cultural research on children's emotion expressions and provide insights for future research on positive emotions.

fNIRS Evidence for Recognizably Different Positive Emotions

The behavioral differentiation of positive emotions has recently been studied in terms of their discrete adaptive functions or appraising profiles. Some preliminary neurophysiological evidences have been found with electroencephalography or autonomic nervous system measurements such as heart rate, skin conductance, etc. However, the brain's hemodynamic responses to different positive emotions remain largely unknown. In the present study, the functional near-infrared spectroscopy (fNIRS) technique was employed. With this tool, we for the first time reported recognizable discrete positive emotions using fNIRS signals. Thirteen participants watched 30 emotional video clips to elicit 10 typical kinds of positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love), and their frontal neural activities were simultaneously recorded with a 24-channel fNIRS system. The multidimensional scaling analysis of participants' subjective ratings on these 10 positive emotions revealed three distinct clusters, which could be interpreted as "playfulness" for amusement, joy, interest, "encouragement" for awe, gratitude, hope, inspiration, pride, and "harmony" for love, serenity. Hemodynamic responses to these three positive emotion clusters showed distinct patterns, and HbO-based individual-level binary classifications between them achieved an averaged accuracy of 73.79 ± 11.49% (77.56 ± 7.39% for encouragement vs. harmony, 73.29 ± 11.87% for playfulness vs. harmony, 70.51 ± 13.96% for encouragement vs. harmony). Benefited from fNIRS's high portability, low running cost and the relative robustness against motion and electrical artifacts, our findings provided support for implementing a more fine-grained emotion recognition system with subdivided positive emotion categories.