Dataset Information

Emotion identification across adulthood using the Dynamic FACES database of emotional expressions in younger, middle aged, and older adults.

ABSTRACT: Facial stimuli are widely used in behavioural and brain science research to investigate emotional facial processing. However, some studies have demonstrated that dynamic expressions elicit stronger emotional responses compared to static images. To address the need for more ecologically valid and powerful facial emotional stimuli, we created Dynamic FACES, a database of morphed videos (n?=?1026) from younger, middle-aged, and older adults displaying naturalistic emotional facial expressions (neutrality, sadness, disgust, fear, anger, happiness). To assess adult age differences in emotion identification of dynamic stimuli and to provide normative ratings for this modified set of stimuli, healthy adults (n?=?1822, age range 18-86 years) categorised for each video the emotional expression displayed, rated the expression distinctiveness, estimated the age of the face model, and rated the naturalness of the expression. We found few age differences in emotion identification when using dynamic stimuli. Only for angry faces did older adults show lower levels of identification accuracy than younger adults. Further, older adults outperformed middle-aged adults' in identification of sadness. The use of dynamic facial emotional stimuli has previously been limited, but Dynamic FACES provides a large database of high-resolution naturalistic, dynamic expressions across adulthood. Information on using Dynamic FACES for research purposes can be found at http://faces.mpib-berlin.mpg.de .

SUBMITTER: Holland CAC

PROVIDER: S-EPMC6599511 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Emotion identification across adulthood using the Dynamic FACES database of emotional expressions in younger, middle aged, and older adults.

Holland Catherine A C CAC Ebner Natalie C NC Lin Tian T Samanez-Larkin Gregory R GR

Cognition & emotion 20180329 2

Facial stimuli are widely used in behavioural and brain science research to investigate emotional facial processing. However, some studies have demonstrated that dynamic expressions elicit stronger emotional responses compared to static images. To address the need for more ecologically valid and powerful facial emotional stimuli, we created Dynamic FACES, a database of morphed videos (n = 1026) from younger, middle-aged, and older adults displaying naturalistic emotional facial expressions (neut ...[more]

PMID: 29595363

Similar Datasets

Project description: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.

Dataset Information

Emotion identification across adulthood using the Dynamic FACES database of emotional expressions in younger, middle aged, and older adults.

Publications

Emotion identification across adulthood using the Dynamic FACES database of emotional expressions in younger, middle aged, and older adults.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets