Processing Of Dynamic Facial Expressions Research Articles

The role of visual spatial frequencies in newborns' processing of dynamic facial expressions of emotion.

Evidence on newborns' discrimination of emotional facial expressions is scarce, and the question of what is the nature of the visual information that newborns rely on to perform such discrimination remains open. Here, we manipulated the spatial frequency (SF) content of the stimuli by selectively removing low spatial frequency (LSF) and high spatial frequency bands using newborn-appropriate cutoffs to investigate what information newborns use when preferring and discriminating between dynamic displays showing happy and fearful expressions unfolding over time. Using a preferential looking paradigm, in Study 1 (N = 63, 59% females, 92% White), we showed that newborns looked longer to happy over fearful expressions in unfiltered (broad spatial frequency) and high-pass (high spatial frequency > 0.6 cycles per degree [cpd]) faces but not in low-pass (LSF < 0.5 cpd) faces. In Study 2 (N = 22, 59% females, 91% White), newborns tested in a visual habituation paradigm showed successful discrimination of the two LSF emotions. Results show that newborns can discriminate between dynamic images of happy and fearful facial expressions containing either extreme low SF (< 0.5 cpd) information or higher SF (> 0.6 cpd) bandwidth. Their preference for the happy expression was present for intact and high-pass filtered faces but not for low-pass faces. This SF effect is tentatively driven by an enhancement of attentional response to the LSF fearful face, whereas the response to the happy face is unaffected by the SF manipulation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Face-Selective Patches in Marmosets Are Involved in Dynamic and Static Facial Expression Processing.

The correct identification of facial expressions is critical for understanding the intention of others during social communication in the daily life of all primates. Here we used ultra-high-field fMRI at 9.4 T to investigate the neural network activated by facial expressions in awake New World common marmosets from both male and female sex, and to determine the effect of facial motions on this network. We further explored how the face-patch network is involved in the processing of facial expressions. Our results show that dynamic and static facial expressions activate face patches in temporal and frontal areas (O, PV, PD, MD, AD, and PL) as well as in the amygdala, with stronger responses for negative faces, also associated with an increase of the respiration rates of the monkey. Processing of dynamic facial expressions involves an extended network recruiting additional regions not known to be part of the face-processing network, suggesting that face motions may facilitate the recognition of facial expressions. We report for the first time in New World marmosets that the perception and identification of changeable facial expressions, vital for social communication, recruit face-selective brain patches also involved in face detection processing and are associated with an increase of arousal.SIGNIFICANCE STATEMENT Recent research in humans and nonhuman primates has highlighted the importance to correctly recognize and process facial expressions to understand others' emotions in social interactions. The current study focuses on the fMRI responses of emotional facial expressions in the common marmoset (Callithrix jacchus), a New World primate species sharing several similarities of social behavior with humans. Our results reveal that temporal and frontal face patches are involved in both basic face detection and facial expression processing. The specific recruitment of these patches for negative faces associated with an increase of the arousal level show that marmosets process facial expressions of their congener, vital for social communication.

Cross-species representations of dynamic facial expression

Primates’ facial expressions represent an important channel of emotional communication. Human and monkey expressions of the same emotion can be quite different. How efficiently can we learn to recognize facial expressions of another primate species, and can we understand human facial movements, even if they are linked to faces of another species? METHODS: To clarify these questions, exploiting state-of-the-art technology in computer animation, we have developed highly realistic models of dynamically moving macaque and human heads. These faces were animated using human and monkey motion capture data. Using a hierarchical generative Bayesian model (combining GP-LVMs and GPDMs), we interpolated continuously in space-time between the facial movements representing emotional expressions in humans and monkeys. In addition, this technique allows to modify continuously the expressiveness of such facial movements. Exploiting these stimuli, we categorized facial movements from a two-dimensional morphing space, including two expressions from humans and monkeys in terms of expression and species. Movements were presented on the monkey and the human face model. RESULTS: Representation of facial movement was largely independent of the basic form of the face (human or monkey). For natural expressions we found no own-species advantage in the categorization, but instead higher expressiveness of the monkey expressions. This effect is diminished if stimuli are balanced in terms of expressiveness by equating their optic flow content. This supports a joint perceptual representation for dynamic expressions from different species with a separate encoding of the basic shape of the face and its motion. Also, it suggests a critical role of optic flow in the processing of dynamic facial expressions.

Widespread and lateralized social brain activity for processing dynamic facial expressions.

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.

Impaired neural processing of dynamic faces in left-onset Parkinson's disease

Parkinson's disease (PD) affects patients beyond the motor domain. According to previous evidence, one mechanism that may be impaired in the disease is face processing. However, few studies have investigated this process at the neural level in PD. Moreover, research using dynamic facial displays rather than static pictures is scarce, but highly warranted due to the higher ecological validity of dynamic stimuli. In the present study we aimed to investigate how PD patients process emotional and non-emotional dynamic face stimuli at the neural level using event-related potentials. Since the literature has revealed a predominantly right-lateralized network for dynamic face processing, we divided the group into patients with left (LPD) and right (RPD) motor symptom onset (right versus left cerebral hemisphere predominantly affected, respectively). Participants watched short video clips of happy, angry, and neutral expressions and engaged in a shallow gender decision task in order to avoid confounds of task difficulty in the data. In line with our expectations, the LPD group showed significant face processing deficits compared to controls. While there were no group differences in early, sensory-driven processing (fronto-central N1 and posterior P1), the vertex positive potential, which is considered the fronto-central counterpart of the face-specific posterior N170 component, had a reduced amplitude and delayed latency in the LPD group. This may indicate disturbances of structural face processing in LPD. Furthermore, the effect was independent of the emotional content of the videos. In contrast, static facial identity recognition performance in LPD was not significantly different from controls, and comprehensive testing of cognitive functions did not reveal any deficits in this group. We therefore conclude that PD, and more specifically the predominant right-hemispheric affection in left-onset PD, is associated with impaired processing of dynamic facial expressions, which could be one of the mechanisms behind the often reported problems of PD patients in their social lives.

Does schizotypal personality enhance the processing of dynamic facial expressions?

Does schizotypal personality enhance the processing of dynamic facial expressions?

P1-67. N170 response to change in facial expressions is correlated with autistic traits: An event-related potential study

Face-specific N170 for facial expressions is smaller in autism spectrum disorder (ASD). The aim of this study was to investigate the processing of dynamic facial expressions in ASD. Facial expressions preceded by neutral face elicited a larger N170 (Miyoshi et al., 2004). This study quantified autistic traits in 17 healthy individuals, using the Autism-Spectrum Quotient (AQ, Baron-Cohen et al., 2001). N170 amplitude for facial expressional change was correlated with autistic traits ( r = 0.49, P < 0.05). Moreover, we divided participants into high and low AQ groups. Participants with high AQ had more smaller N170 for anger expression than participants with low AQ ( P = 0.08). However There was no difference of N170 for happy expression between two groups. Our findings suggest the individuals with the autistic traits have an abnormally encoding for negative dynamic facial expression.

Atypical recognition of dynamic changes in facial expressions in autism spectrum disorders

Previous studies have suggested that the processing of dynamic facial expressions is impaired in autism spectrum disorders (ASD). However, the specific component that is impaired in the processing of dynamic facial expressions has not been identified. We investigated the recognition of dynamic changes in facial expressions among individuals with ASD and age- and sex-matched typically developing controls. Morphing animations of facial expressions of six emotions were presented at four different changing speeds, and participants rated the naturalness of the expression changes. The correspondence between reduced speeds and decreased naturalness ratings was weaker in the ASD than in the control group. These results suggest that the atypical visual analysis of dynamic changes in facial expressions underlies the impairment in real-life social interaction among individuals with ASD.

Functional Mapping of Dynamic Happy and Fearful Facial Expression Processing in Adolescents

This paper assessed the neural systems involved in processing of dynamic facial expressions in adolescents. The processing of facial expressions changes as a function of age, and it is thus important to understand how healthy adolescent subjects process dynamic facial expressions prior to analyzing disease-related changes. We hypothesized that viewing of dynamic facial expressions with opposing valences (happy vs. fearful) induces differential activations and deactivations in the brain. 27 healthy adolescents (9 female, 18 male, mean age = 14.5 years; age range 11.6-17.3 years) were examined by using the ASSQ and K-SADS-PL and scanned with 1.5-T fMRI during viewing of dynamic facial expressions and mosaic control images. The stimuli activated the same areas as previously seen in dynamic facial expression in adults. Our results indicated that opposing-valence dynamic facial expressions had differential effects on many cortical structures but not on subcortical limbic structures. The mirror neuron system is activated more during viewing of fearful compared to happy expressions in bilateral inferior frontal gyrus (IFG) and superior temporal sulcus (STS) left dominantly. We also detected more deactivation in the ventral anterior cingulate gyrus (ACG), showing more automated attentional processing of fearful expressions during passive viewing. Females were found to deactivate the right frontal pole more than male adolescents during happy facial expressions, while there were no differences in fear processing between genders. No clear gender or age effects were detected. In conclusion fear induces stronger responses in attention and mirror neurons probably related to fear contagion.

Amygdala activity in response to forward versus backward dynamic facial expressions

Observations of dynamic facial expressions of emotion activate several brain regions, but the psychological functions of these regions remain unknown. To investigate this issue, we presented dynamic facial expressions of fear and happiness forwards or backwards, thus altering the emotional meaning of the facial expression while maintaining comparable visual properties. Thirteen subjects passively viewed the stimuli while being scanned using fMRI. After image acquisition, the subject's emotions while perceiving the stimuli were investigated using valence and intensity scales. The left amygdala showed higher activity in response to forward compared with backward presentations, for both fearful and happy expressions. Amygdala activity showed a positive relationship with the intensity of the emotion experienced. These results suggest that the amygdala is not involved in the visual but is involved in the emotional processing of dynamic facial expressions, including specifically the elicitation of subjective emotions.

Enhanced perceptual, emotional, and motor processing in response to dynamic facial expressions of emotion1

Abstract: Dynamic facial expressions of emotion constitute natural and powerful media compared with static ones. However, little is known about the processing of dynamic facial expressions of emotion. In this paper, we describe the results of our recent neuroimaging and psychological studies on this issue. A neuroimaging study was conducted to investigate brain activity while viewing dynamic facial expressions. The results revealed that the broad region of visual cortices, the amygdala, and the right inferior frontal gyrus were more activated in response to dynamic facial expressions than control stimuli, such as static facial expressions and dynamic mosaics. In corresponding with the characteristics of these brain activities, the results of three psychological studies indicated that the dynamic presentation: (a) intensified the perceptual image of the facial expression (perceptual enhancement); (b) enhanced the emotional feeling; and (c) elicited spontaneous and rapid facial mimicry. These results revealed that the dynamic property facilitates the perceptual, emotional, and motor processing of facial expressions of emotion.

Enhanced neural activity in response to dynamic facial expressions of emotion: an fMRI study

Dynamic facial expressions of emotion constitute natural and powerful media of communication between individuals. However, little is known about the neural substrate underlying the processing of dynamic facial expressions of emotion. We depicted the brain areas by using fMRI with 22 right-handed healthy subjects. The facial expressions are dynamically morphed from neutral to fearful or happy expressions. Two types of control stimuli were presented: (i) static facial expressions, which provided sustained fearful or happy expressions, and (ii) dynamic mosaic images, which provided dynamic information with no facial features. Subjects passively viewed these stimuli. The left amygdala was highly activated in response to dynamic facial expressions relative to both control stimuli in the case of fearful expressions, but not in the case of happy expressions. The broad region of the occipital and temporal cortices, especially in the right hemisphere, which included the activation foci of the inferior occipital gyri, middle temporal gyri, and fusiform gyri, showed higher activation during viewing of the dynamic facial expressions than it did during the viewing of either control stimulus, common to both expressions. In the same manner, the right ventral premotor cortex was also activated. These results identify the neural substrate for enhanced emotional, perceptual/cognitive, and motor processing of dynamic facial expressions of emotion.

Enhanced neural activity in response to dynamic facial expressions of emotion: an fMRI study

Dynamic facial expressions of emotion constitute natural and powerful media of communication between individuals. However, little is known about the neural substrate underlying the processing of dynamic facial expressions of emotion. We depicted the brain areas by using fMRI with 22 right-handed healthy subjects. The facial expressions are dynamically morphed from neutral to fearful or happy expressions. Two types of control stimuli were presented: (i) static facial expressions, which provided sustained fearful or happy expressions, and (ii) dynamic mosaic images, which provided dynamic information with no facial features. Subjects passively viewed these stimuli. The left amygdala was highly activated in response to dynamic facial expressions relative to both control stimuli in the case of fearful expressions, but not in the case of happy expressions. The broad region of the occipital and temporal cortices, especially in the right hemisphere, which included the activation foci of the inferior occipital gyri, middle temporal gyri, and fusiform gyri, showed higher activation during viewing of the dynamic facial expressions than it did during the viewing of either control stimulus, common to both expressions. In the same manner, the right ventral premotor cortex was also activated. These results identify the neural substrate for enhanced emotional, perceptual/cognitive, and motor processing of dynamic facial expressions of emotion.