Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

"Grumpy" or "furious"? arousal of emotion labels influences judgments of facial expressions.

ABSTRACT: Whether language information influences recognition of emotion from facial expressions remains the subject of debate. The current studies investigate how variations in emotion labels that are paired with expressions influences participants' judgments of the emotion displayed. Static (Study 1) and dynamic (Study 2) facial expressions depicting eight emotion categories were paired with emotion labels that systematically varied in arousal (low and high). Participants rated the arousal, valence, and dominance of expressions paired with labels. Isolated faces and isolated labels were also rated. As predicted, the label presented influenced participants' judgments of the expressions. Across both studies, higher arousal labels were associated with: 1) higher ratings of arousal for sad, angry, and scared expressions, and 2) higher ratings of dominance for angry, proud, and disgust expressions. These results indicate that emotion labels influence judgments of facial expressions.

SUBMITTER: Barker MS

PROVIDER: S-EPMC7329125 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

"Grumpy" or "furious"? arousal of emotion labels influences judgments of facial expressions.

Barker Megan S MS Bidstrup Emma M EM Robinson Gail A GA Nelson Nicole L NL

PloS one 20200701 7

Whether language information influences recognition of emotion from facial expressions remains the subject of debate. The current studies investigate how variations in emotion labels that are paired with expressions influences participants' judgments of the emotion displayed. Static (Study 1) and dynamic (Study 2) facial expressions depicting eight emotion categories were paired with emotion labels that systematically varied in arousal (low and high). Participants rated the arousal, valence, and ...[more]

PMID: 32609780

Similar Datasets

Compound facial expressions of emotion.

Project description:Understanding the different categories of facial expressions of emotion regularly used by us is essential to gain insights into human cognition and affect as well as for the design of computational models and perceptual interfaces. Past research on facial expressions of emotion has focused on the study of six basic categories--happiness, surprise, anger, sadness, fear, and disgust. However, many more facial expressions of emotion exist and are used regularly by humans. This paper describes an important group of expressions, which we call compound emotion categories. Compound emotions are those that can be constructed by combining basic component categories to create new ones. For instance, happily surprised and angrily surprised are two distinct compound emotion categories. The present work defines 21 distinct emotion categories. Sample images of their facial expressions were collected from 230 human subjects. A Facial Action Coding System analysis shows the production of these 21 categories is different but consistent with the subordinate categories they represent (e.g., a happily surprised expression combines muscle movements observed in happiness and surprised). We show that these differences are sufficient to distinguish between the 21 defined categories. We then use a computational model of face perception to demonstrate that most of these categories are also visually discriminable from one another.

| S-EPMC3992629 | biostudies-literature

Visual perception of facial expressions of emotion.

Project description:Facial expressions of emotion are produced by contracting and relaxing the facial muscles in our face. I hypothesize that the human visual system solves the inverse problem of production, that is, to interpret emotion, the visual system attempts to identify the underlying muscle activations. I show converging computational, behavioral and imaging evidence in favor of this hypothesis. I detail the computations performed by the human visual system to achieve the decoding of these facial actions and identify a brain region where these computations likely take place. The resulting computational model explains how humans readily classify emotions into categories as well as continuous variables. This model also predicts the existence of a large number of previously unknown facial expressions, including compound emotions, affect attributes and mental states that are regularly used by people. I provide evidence in favor of this prediction.

| S-EPMC5657455 | biostudies-literature

Facial expressions of emotion are not culturally universal.

Project description:Since Darwin's seminal works, the universality of facial expressions of emotion has remained one of the longest standing debates in the biological and social sciences. Briefly stated, the universality hypothesis claims that all humans communicate six basic internal emotional states (happy, surprise, fear, disgust, anger, and sad) using the same facial movements by virtue of their biological and evolutionary origins [Susskind JM, et al. (2008) Nat Neurosci 11:843-850]. Here, we refute this assumed universality. Using a unique computer graphics platform that combines generative grammars [Chomsky N (1965) MIT Press, Cambridge, MA] with visual perception, we accessed the mind's eye of 30 Western and Eastern culture individuals and reconstructed their mental representations of the six basic facial expressions of emotion. Cross-cultural comparisons of the mental representations challenge universality on two separate counts. First, whereas Westerners represent each of the six basic emotions with a distinct set of facial movements common to the group, Easterners do not. Second, Easterners represent emotional intensity with distinctive dynamic eye activity. By refuting the long-standing universality hypothesis, our data highlight the powerful influence of culture on shaping basic behaviors once considered biologically hardwired. Consequently, our data open a unique nature-nurture debate across broad fields from evolutionary psychology and social neuroscience to social networking via digital avatars.

| S-EPMC3358835 | biostudies-other

Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research.

Project description:As early as infancy, caregivers' facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal-core Research Domain Criteria constructs-as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother-daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation.

| S-EPMC7319037 | biostudies-literature

Training Emotion Recognition Accuracy: Results for Multimodal Expressions and Facial Micro Expressions.

Project description:Nonverbal emotion recognition accuracy (ERA) is a central feature of successful communication and interaction, and is of importance for many professions. We developed and evaluated two ERA training programs-one focusing on dynamic multimodal expressions (audio, video, audio-video) and one focusing on facial micro expressions. Sixty-seven subjects were randomized to one of two experimental groups (multimodal, micro expression) or an active control group (emotional working memory task). Participants trained once weekly with a brief computerized training program for three consecutive weeks. Pre-post outcome measures consisted of a multimodal ERA task, a micro expression recognition task, and a task about patients' emotional cues. Post measurement took place approximately a week after the last training session. Non-parametric mixed analyses of variance using the Aligned Rank Transform were used to evaluate the effectiveness of the training programs. Results showed that multimodal training was significantly more effective in improving multimodal ERA compared to micro expression training or the control training; and the micro expression training was significantly more effective in improving micro expression ERA compared to the other two training conditions. Both pre-post effects can be interpreted as large. No group differences were found for the outcome measure about recognizing patients' emotion cues. There were no transfer effects of the training programs, meaning that participants only improved significantly for the specific facet of ERA that they had trained on. Further, low baseline ERA was associated with larger ERA improvements. Results are discussed with regard to methodological and conceptual aspects, and practical implications and future directions are explored.

| S-EPMC8406528 | biostudies-literature

The NimStim set of facial expressions: judgments from untrained research participants.

Project description:A set of face stimuli called the NimStim Set of Facial Expressions is described. The goal in creating this set was to provide facial expressions that untrained individuals, characteristic of research participants, would recognize. This set is large in number, multiracial, and available to the scientific community online. The results of psychometric evaluations of these stimuli are presented. The results lend empirical support for the validity and reliability of this set of facial expressions as determined by accurate identification of expressions and high intra-participant agreement across two testing sessions, respectively.

| S-EPMC3474329 | biostudies-literature

Facial feedback affects valence judgments of dynamic and static emotional expressions.

Project description:The ability to judge others' emotions is required for the establishment and maintenance of smooth interactions in a community. Several lines of evidence suggest that the attribution of meaning to a face is influenced by the facial actions produced by an observer during the observation of a face. However, empirical studies testing causal relationships between observers' facial actions and emotion judgments have reported mixed findings. This issue was investigated by measuring emotion judgments in terms of valence and arousal dimensions while comparing dynamic vs. static presentations of facial expressions. We presented pictures and videos of facial expressions of anger and happiness. Participants (N = 36) were asked to differentiate between the gender of faces by activating the corrugator supercilii muscle (brow lowering) and zygomaticus major muscle (cheek raising). They were also asked to evaluate the internal states of the stimuli using the affect grid while maintaining the facial action until they finished responding. The cheek raising condition increased the attributed valence scores compared with the brow-lowering condition. This effect of facial actions was observed for static as well as for dynamic facial expressions. These data suggest that facial feedback mechanisms contribute to the judgment of the valence of emotional facial expressions.

| S-EPMC4362049 | biostudies-literature

Cognitive penetrability and emotion recognition in human facial expressions.

Project description:Do our background beliefs, desires, and mental images influence our perceptual experience of the emotions of others? In this paper, we will address the possibility of cognitive penetration (CP) of perceptual experience in the domain of social cognition. In particular, we focus on emotion recognition based on the visual experience of facial expressions. After introducing the current debate on CP, we review examples of perceptual adaptation for facial expressions of emotion. This evidence supports the idea that facial expressions are perceptually processed as wholes. That is, the perceptual system integrates lower-level facial features, such as eyebrow orientation, mouth angle etc., into facial compounds. We then present additional experimental evidence showing that in some cases, emotion recognition on the basis of facial expression is sensitive to and modified by the background knowledge of the subject. We argue that such sensitivity is best explained as a difference in the visual experience of the facial expression, not just as a modification of the judgment based on this experience. The difference in experience is characterized as the result of the interference of background knowledge with the perceptual integration process for faces. Thus, according to the best explanation, we have to accept CP in some cases of emotion recognition. Finally, we discuss a recently proposed mechanism for CP in the face-based recognition of emotion.

| S-EPMC4473593 | biostudies-other

Identifying and detecting facial expressions of emotion in peripheral vision.

Project description:Facial expressions of emotion are signals of high biological value. Whilst recognition of facial expressions has been much studied in central vision, the ability to perceive these signals in peripheral vision has only seen limited research to date, despite the potential adaptive advantages of such perception. In the present experiment, we investigate facial expression recognition and detection performance for each of the basic emotions (plus neutral) at up to 30 degrees of eccentricity. We demonstrate, as expected, a decrease in recognition and detection performance with increasing eccentricity, with happiness and surprised being the best recognized expressions in peripheral vision. In detection however, while happiness and surprised are still well detected, fear is also a well detected expression. We show that fear is a better detected than recognized expression. Our results demonstrate that task constraints shape the perception of expression in peripheral vision and provide novel evidence that detection and recognition rely on partially separate underlying mechanisms, with the latter more dependent on the higher spatial frequency content of the face stimulus.

| S-EPMC5976168 | biostudies-literature

Facial expressions elicit multiplexed perceptions of emotion categories and dimensions.

Project description:Human facial expressions are complex, multi-component signals that can communicate rich information about emotions,1-5 including specific categories, such as "anger," and broader dimensions, such as "negative valence, high arousal."6-8 An enduring question is how this complex signaling is achieved. Communication theory predicts that multi-component signals could transmit each type of emotion information-i.e., specific categories and broader dimensions-via the same or different facial signal components, with implications for elucidating the system and ontology of facial expression communication.9 We addressed this question using a communication-systems-based method that agnostically generates facial expressions and uses the receiver's perceptions to model the specific facial signal components that represent emotion category and dimensional information to them.10-12 First, we derived the facial expressions that elicit the perception of emotion categories (i.e., the six classic emotions13 plus 19 complex emotions3) and dimensions (i.e., valence and arousal) separately, in 60 individual participants. Comparison of these facial signals showed that they share subsets of components, suggesting that specific latent signals jointly represent-i.e., multiplex-categorical and dimensional information. Further examination revealed these specific latent signals and the joint information they represent. Our results-based on white Western participants, same-ethnicity face stimuli, and commonly used English emotion terms-show that facial expressions can jointly represent specific emotion categories and broad dimensions to perceivers via multiplexed facial signal components. Our results provide insights into the ontology and system of facial expression communication and a new information-theoretic framework that can characterize its complexities.

| S-EPMC8751635 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data