Dynamic human and avatar facial expressions elicit differential brain responses.
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ABSTRACT:
SUBMITTER: Kegel LC
PROVIDER: S-EPMC7235958 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and objectiveAlthough avatars are now widely used in advertisement, entertainment, and business today, no study has investigated whether brain lesions in neurological patients interfere with brain activation in response to dynamic avatar facial expressions. The aim of our event-related fMRI study was to compare brain activation differences in people with epilepsy and controls during the processing of fearful and neutral dynamic expressions displayed by human or avatar faces.MethodsUsing functional magnetic resonance imaging (fMRI), we examined brain responses to dynamic facial expressions of trained actors and their avatar look-alikes in 16 people with temporal lobe epilepsy (TLE) and 26 controls. The actors' fearful and neutral expressions were recorded on video and conveyed onto their avatar look-alikes by face tracking.ResultsOur fMRI results show that people with TLE exhibited reduced response differences between fearful and neutral expressions displayed by humans in the right amygdala and the left superior temporal sulcus (STS). Further, TLE was associated with reduced response differences between human and avatar fearful expressions in the dorsal pathway of the face perception network (STS and inferior frontal gyrus) as well as in the medial prefrontal cortex.ConclusionsTaken together, these findings suggest that brain responses to dynamic facial expressions are altered in people with TLE compared to neurologically healthy individuals-regardless of whether the face is human or computer-generated. In TLE, areas sensitive to dynamic facial features and associated with processes relating to the self and others are particularly affected when processing dynamic human and avatar expressions. Our findings highlight that the impact of TLE on facial emotion processing must be extended to artificial faces and should be considered when applying dynamic avatars in the context of neurological conditions.
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Project description:Daily life demands that we differentiate between a multitude of emotional facial expressions (EFEs). The mirror neuron system (MNS) is becoming increasingly implicated as a neural network involved with understanding emotional body expressions. However, the specificity of the MNS's involvement in emotion recognition has remained largely unexplored. This study investigated whether six basic dynamic EFEs (anger, disgust, fear, happiness, sadness, and surprise) would be differentiated through event-related desynchronisation (ERD) of sensorimotor alpha and beta oscillatory activity, which indexes sensorimotor MNS activity. We found that beta ERD differentiated happy, fearful, and sad dynamic EFEs at the central region of interest, but not at occipital regions. Happy EFEs elicited significantly greater central beta ERD relative to fearful and sad EFEs within 800 - 2,000 ms after EFE onset. These differences were source-localised to the primary somatosensory cortex, which suggests they are likely to reflect differential sensorimotor simulation rather than differential attentional engagement. Furthermore, individuals with higher trait anxiety showed less beta ERD differentiation between happy and sad faces. Similarly, individuals with higher trait autism showed less beta ERD differentiation between happy and fearful faces. These findings suggest that the differential simulation of specific affective states is attenuated in individuals with higher trait anxiety and autism. In summary, the MNS appears to support the skills needed for emotion processing in daily life, which may be influenced by certain individual differences. This provides novel evidence for the notion that simulation-based emotional skills may underlie the emotional difficulties that accompany affective disorders, such as anxiety.
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Project description:Dynamic facial expressions of emotions constitute natural and powerful means of social communication in daily life. A number of previous neuroimaging studies have explored the neural mechanisms underlying the processing of dynamic facial expressions, and indicated the activation of certain social brain regions (e.g., the amygdala) during such tasks. However, the activated brain regions were inconsistent across studies, and their laterality was rarely evaluated. To investigate these issues, we measured brain activity using functional magnetic resonance imaging in a relatively large sample (n = 51) during the observation of dynamic facial expressions of anger and happiness and their corresponding dynamic mosaic images. The observation of dynamic facial expressions, compared with dynamic mosaics, elicited stronger activity in the bilateral posterior cortices, including the inferior occipital gyri, fusiform gyri, and superior temporal sulci. The dynamic facial expressions also activated bilateral limbic regions, including the amygdalae and ventromedial prefrontal cortices, more strongly versus mosaics. In the same manner, activation was found in the right inferior frontal gyrus (IFG) and left cerebellum. Laterality analyses comparing original and flipped images revealed right hemispheric dominance in the superior temporal sulcus and IFG and left hemispheric dominance in the cerebellum. These results indicated that the neural mechanisms underlying processing of dynamic facial expressions include widespread social brain regions associated with perceptual, emotional, and motor functions, and include a clearly lateralized (right cortical and left cerebellar) network like that involved in language processing.
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Project description:Facial expression is an integral aspect of non-verbal communication of affective information. Earlier psychological studies have reported that the presentation of prerecorded photographs or videos of emotional facial expressions automatically elicits divergent responses, such as emotions and facial mimicry. However, such highly controlled experimental procedures may lack the vividness of real-life social interactions. This study incorporated a live image relay system that delivered models' real-time performance of positive (smiling) and negative (frowning) dynamic facial expressions or their prerecorded videos to participants. We measured subjective ratings of valence and arousal and facial electromyography (EMG) activity in the zygomaticus major and corrugator supercilii muscles. Subjective ratings showed that the live facial expressions were rated to elicit higher valence and more arousing than the corresponding videos for positive emotion conditions. Facial EMG data showed that compared with the video, live facial expressions more effectively elicited facial muscular activity congruent with the models' positive facial expressions. The findings indicate that emotional facial expressions in live social interactions are more evocative of emotional reactions and facial mimicry than earlier experimental data have suggested.
| S-EPMC7544832 | biostudies-literature
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.
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Project description:Observing others' emotions triggers physiological arousal in infants as well as in adults, reflected in dilated pupil sizes. This study is the first to examine parents' and infants' pupil responses to dynamic negative emotional facial expressions. Moreover, the links between pupil responses and negative emotional dispositions were explored among infants and parents. Infants' and one of their parent's pupil responses to negative versus neutral faces were measured via eye tracking in 222 infants (5- to 7-month-olds, n = 77, 11- to 13-month-olds, n = 78, and 17- to 19-month-olds, n = 67) and 229 parents. One parent contributed to the pupil data, whereas both parents were invited to fill in questionnaires on their own and their infant's negative emotional dispositions. Infants did not differentially respond to negative expressions, while parents showed stronger pupil responses to negative versus neutral expressions. There was a positive association between infants' and their parent's mean pupil responses and significant links between mothers' and fathers' stress levels and their infants' pupil responses. We conclude that a direct association between pupil responses in parents and offspring is observable already in infancy in typical development. Stress in parents is related to their infants' pupillary arousal to negative emotions.
| S-EPMC9291579 | biostudies-literature
Project description:Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition.
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Project description:With a shift in interest toward dynamic expressions, numerous corpora of dynamic facial stimuli have been developed over the past two decades. The present research aimed to test existing sets of dynamic facial expressions (published between 2000 and 2015) in a cross-corpus validation effort. For this, 14 dynamic databases were selected that featured facial expressions of the basic six emotions (anger, disgust, fear, happiness, sadness, surprise) in posed or spontaneous form. In Study 1, a subset of stimuli from each database (N = 162) were presented to human observers and machine analysis, yielding considerable variance in emotion recognition performance across the databases. Classification accuracy further varied with perceived intensity and naturalness of the displays, with posed expressions being judged more accurately and as intense, but less natural compared to spontaneous ones. Study 2 aimed for a full validation of the 14 databases by subjecting the entire stimulus set (N = 3812) to machine analysis. A FACS-based Action Unit (AU) analysis revealed that facial AU configurations were more prototypical in posed than spontaneous expressions. The prototypicality of an expression in turn predicted emotion classification accuracy, with higher performance observed for more prototypical facial behavior. Furthermore, technical features of each database (i.e., duration, face box size, head rotation, and motion) had a significant impact on recognition accuracy. Together, the findings suggest that existing databases vary in their ability to signal specific emotions, thereby facing a trade-off between realism and ecological validity on the one end, and expression uniformity and comparability on the other.
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Project description:Humanlike androids can function as social agents in social situations and in experimental research. While some androids can imitate facial emotion expressions, it is unclear whether their expressions tap the same processing mechanisms utilized in human expression processing, for example configural processing. In this study, the effects of global inversion and asynchrony between facial features as configuration manipulations were compared in android and human dynamic emotion expressions. Seventy-five participants rated (1) angry and happy emotion recognition and (2) arousal and valence ratings of upright or inverted, synchronous or asynchronous, android or human agent dynamic emotion expressions. Asynchrony in dynamic expressions significantly decreased all ratings (except valence in angry expressions) in all human expressions, but did not affect android expressions. Inversion did not affect any measures regardless of agent type. These results suggest that dynamic facial expressions are processed in a synchrony-based configural manner for humans, but not for androids.
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Project description:Previous studies demonstrated the positive effects of smiling on interpersonal outcomes. The present research examined if enhancing one's smile in a virtual environment could lead to a more positive communication experience. In the current study, participants' facial expressions were tracked and mapped on a digital avatar during a real-time dyadic conversation. The avatar's smile was rendered such that it was either a slightly enhanced version or a veridical version of the participant's actual smile. Linguistic analyses using the Linguistic Inquiry Word Count (LIWC) revealed that participants who communicated with each other via avatars that exhibited enhanced smiles used more positive words to describe their interaction experience compared to those who communicated via avatars that displayed smiling behavior reflecting the participants' actual smiles. In addition, self-report measures showed that participants in the 'enhanced smile' condition felt more positive affect after the conversation and experienced stronger social presence compared to the 'normal smile' condition. These results are particularly striking when considering the fact that most participants (>90%) were unable to detect the smiling manipulation. This is the first study to demonstrate the positive effects of transforming unacquainted individuals' actual smiling behavior during a real-time avatar-networked conversation.
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