Holistic processing is a fundamental element of face-recognition studies. Some behavioral studies have investigated the impact of rigid facial motion on holistic face processing, yet it is still unclear how rigid motion affects the time course of holistic face processing for different face races. The current study investigated this issue, using the composite face effect (CFE) as a direct measure of holistic processing. Participants were asked to match the identity of the top half of a static composite face with the study face during the test stage, where the study face was either static or rigidly-moving. ERP results showed that rigidly-moving study faces elicited a larger CFE relative to static study faces in the N170 component when recognizing own-race faces. The amplitude of P1, N170 and P2 components indicated that rigid motion facilitated holistic face processing, with differences observed between the hemispheres over time. Specifically, the CFE was only observed after exposure to rigidly-moving faces in the P1 and P2 components of the right hemisphere. Additionally, a greater CFE was observed following exposure to rigidly-moving faces compared to static faces, particularly in the N170 component of the left hemisphere. This study suggests that holistic processing is a fundamental aspect of face perception that applies to both static and moving faces, not just static ones. Furthermore, rigid facial motion improves holistic processing of own-race faces during the structural encoding stage. These findings provide evidence of distinct neural mechanisms underlying the holistic processing of static and moving faces.

Video recordings accurately capture facial expression movements; however, they are difficult for face perception researchers to standardise and manipulate. For this reason, dynamic morphs of photographs are often used, despite their lack of naturalistic facial motion. This study aimed to investigate how humans perceive emotions from faces using real videos and two different approaches to artificially generating dynamic expressions - dynamic morphs, and AI-synthesised deepfakes. Our participants perceived dynamic morphed expressions as less intense when compared with videos (all emotions) and deepfakes (fearful, happy, sad). Videos and deepfakes were perceived similarly. Additionally, they perceived morphed happiness and sadness, but not morphed anger or fear, as less genuine than other formats. Our findings support previous research indicating that social responses to morphed emotions are not representative of those to video recordings. The findings also suggest that deepfakes may offer a more suitable standardized stimulus type compared to morphs. Additionally, qualitative data were collected from participants and analysed using ChatGPT, a large language model. ChatGPT successfully identified themes in the data consistent with those identified by an independent human researcher. According to this analysis, our participants perceived dynamic morphs as less natural compared with videos and deepfakes. That participants perceived deepfakes and videos similarly suggests that deepfakes effectively replicate natural facial movements, making them a promising alternative for face perception research. The study contributes to the growing body of research exploring the usefulness of generative artificial intelligence for advancing the study of human perception.

BACKGROUND: Emotion processing deficits are known to accompany depressive symptoms and are often seen in stroke patients. Little is known about the influence of post-stroke depressive (PSD) symptoms and specific brain lesions on altered emotion processing abilities and how these phenomena develop over time. This potential relationship may impact post-stroke rehabilitation of neurological and psychosocial function. To address this scientific gap, we investigated the relationship between PSD symptoms and emotion processing abilities in a longitudinal study design from the first days post-stroke into the early chronic phase.
METHODS: Twenty-six ischemic stroke patients performed an emotion processing task on videos with emotional faces (\'happy,\' \'sad,\' \'anger,\' \'fear,\' and \'neutral\') at different intensity levels (20%, 40%, 60%, 80%, 100%). Recognition accuracies and response times were measured, as well as scores of depressive symptoms (Montgomery-Åsberg Depression Rating Scale). Twenty-eight healthy participants matched in age and sex were included as a control group. Whole-brain support-vector regression lesion-symptom mapping (SVR-LSM) analyses were performed to investigate whether specific lesion locations were associated with the recognition accuracy of specific emotion categories.
RESULTS: Stroke patients performed worse in overall recognition accuracy compared to controls, specifically in the recognition of happy, sad, and fearful faces. Notably, more depressed stroke patients showed an increased processing towards specific negative emotions, as they responded significantly faster to angry faces and recognized sad faces of low intensities significantly more accurately. These effects obtained for the first days after stroke partly persisted to follow-up assessment several months later. SVR-LSM analyses revealed that inferior and middle frontal regions (IFG/MFG) and insula and putamen were associated with emotion-recognition deficits in stroke. Specifically, recognizing happy facial expressions was influenced by lesions affecting the anterior insula, putamen, IFG, MFG, orbitofrontal cortex, and rolandic operculum. Lesions in the posterior insula, rolandic operculum, and MFG were also related to reduced recognition accuracy of fearful facial expressions, whereas recognition deficits of sad faces were associated with frontal pole, IFG, and MFG damage.
CONCLUSIONS: PSD symptoms facilitate processing negative emotional stimuli, specifically angry and sad facial expressions. The recognition accuracy of different emotional categories was linked to brain lesions in emotion-related processing circuits, including insula, basal ganglia, IFG, and MFG. In summary, our study provides support for psychosocial and neural factors underlying emotional processing after stroke, contributing to the pathophysiology of PSD.

UNASSIGNED: Maladaptive functioning of the amygdala has been associated with impaired emotion regulation in affective disorders. Recent advances in real-time fMRI neurofeedback have successfully demonstrated the modulation of amygdala activity in healthy and psychiatric populations. In contrast to an abstract feedback representation applied in standard neurofeedback designs, we proposed a novel neurofeedback paradigm using naturalistic stimuli like human emotional faces as the feedback display where change in the facial expression intensity (from neutral to happy or from fearful to neutral) was coupled with the participant\'s ongoing bilateral amygdala activity.
UNASSIGNED: The feasibility of this experimental approach was tested on 64 healthy participants who completed a single training session with four neurofeedback runs. Participants were assigned to one of the four experimental groups (n = 16 per group), i.e., happy-up, happy-down, fear-up, fear-down. Depending on the group assignment, they were either instructed to \"try to make the face happier\" by upregulating (happy-up) or downregulating (happy-down) the amygdala or to \"try to make the face less fearful\" by upregulating (fear-up) or downregulating (fear-down) the amygdala feedback signal.
UNASSIGNED: Linear mixed effect analyses revealed significant amygdala activity changes in the fear condition, specifically in the fear-down group with significant amygdala downregulation in the last two neurofeedback runs as compared to the first run. The happy-up and happy-down groups did not show significant amygdala activity changes over four runs. We did not observe significant improvement in the questionnaire scores and subsequent behavior. Furthermore, task-dependent effective connectivity changes between the amygdala, fusiform face area (FFA), and the medial orbitofrontal cortex (mOFC) were examined using dynamic causal modeling. The effective connectivity between FFA and the amygdala was significantly increased in the happy-up group (facilitatory effect) and decreased in the fear-down group. Notably, the amygdala was downregulated through an inhibitory mechanism mediated by mOFC during the first training run.
UNASSIGNED: In this feasibility study, we intended to address key neurofeedback processes like naturalistic facial stimuli, participant engagement in the task, bidirectional regulation, task congruence, and their influence on learning success. It demonstrated that such a versatile emotional face feedback paradigm can be tailored to target biased emotion processing in affective disorders.

Face perception provides an excellent example of how the brain processes nuanced visual differences and transforms them into behaviourally useful representations of identities and emotional expressions. While a body of literature has looked into the spatial and temporal neural processing of facial expressions, few studies have used a dimensionally varying set of stimuli containing subtle perceptual changes. In the current study, we used 48 short videos varying dimensionally in their intensity and category (happy, angry, surprised) of expression. We measured both fMRI and EEG responses to these video clips and compared the neural response patterns to the predictions of models based on image features and models derived from behavioural ratings of the stimuli. In fMRI, the inferior frontal gyrus face area (IFG-FA) carried information related only to the intensity of the expression, independent of image-based models. The superior temporal sulcus (STS), inferior temporal (IT) and lateral occipital (LO) areas contained information about both expression category and intensity. In the EEG, the coding of expression category and low-level image features were most pronounced at around 400 ms. The expression intensity model did not, however, correlate significantly at any EEG timepoint. Our results show a specific role for IFG-FA in the coding of expressions and suggest that it contains image and category invariant representations of expression intensity.

Faces carry key personal information about individuals, including cues to their identity, social traits, and emotional state. Much research to date has employed static images of faces taken under tightly controlled conditions yet faces in the real world are dynamic and experienced under ambient conditions. A common approach to studying key dimensions of facial variation is the use of facial caricatures. However, such techniques have again typically relied on static images, and the few examples of dynamic caricatures have relied on animating graphical head models. Here, we present a principal component analysis (PCA)-based active appearance model for capturing patterns of spatiotemporal variation in videos of natural dynamic facial behaviours. We demonstrate how this technique can be applied to generate dynamic anti-caricatures of biological motion patterns in facial behaviours. This technique could be extended to caricaturing other facial dimensions, or to more general analyses of spatiotemporal variations in dynamic faces.

Individuals with high social anxiety (HSA) show abnormal processing of emotional faces, which may increase their social anxiety. A growing number of event-related potential (ERP) studies have explored the neural mechanisms underlying the static-emotional face processing of HSA individuals. In view of the ecological validity of dynamic faces, this study will further explore the time course of dynamic-emotional face processing in individuals with HSA. To this end, 30 high and 30 low social anxiety (LSA) participants were asked to perform an identification task of dynamic-emotional faces while their brain responses were recorded using an ERP technique. The behavioral results showed the recognition accuracy of dynamic faces was higher than static faces when these faces were happy. For the P100 component, HSA participants showed higher P100 mean amplitudes of dynamic than static faces in the left hemisphere when they viewed happy, but not angry faces. In addition, increased N170 mean amplitudes of dynamic-happy faces were showed. Furthermore, the LPP mean amplitudes of dynamic faces were smaller than those of static faces. In sum, this study could provide a better understanding of the time course of dynamic-emotional face processing in HSA individuals.

It was recently proposed that both experience-driven and object-based perceptual grouping contribute to holistic face processing. We investigated whether motion-as a common fate perceptual grouping cue-could enhance the holistic processing of misaligned faces. We manipulated alignment and motion (dynamic and static) of study and test faces in a modified complete composite task in which the congruency effect was regarded as an indicator of holistic processing. Participants made same-different judgments about the top halves of two sequentially presented composite faces. We observed that when the study faces were dynamic, regardless of whether the test faces were dynamic or static, misaligned-misaligned face pairs were processed holistically. When the study faces were static, misaligned-misaligned face pairs showed no holistic processing, and neither did inverted faces. These results indicate that motion can promote the holistic processing of misaligned faces. Our findings provide important insights into different types of holistic face processing, and we discuss these types as well as their relationships with each other in depth.

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.

It has been well documented that static face processing is holistic. Faces contain variant (e.g., motion, viewpoint) and invariant (race, sex) features. However, little research has focused on whether holistic face representations are tolerant of within-person variations. The present study thus investigated whether holistic face representations of faces are tolerant of within-person motion and viewpoint variations by manipulating study-test consistency using a complete composite paradigm. Participants were shown two faces sequentially and were asked to judge whether the faces\' top halves were identical or different. The first face was a static face or a dynamic face rotated in depth at 30°, 60°, and 90°. The second face was either a different front-view static face (Experiment 1a, study-test inconsistent) or identical to the first face (Experiment 1b, study-test consistent). In Experiment 2, study-test consistency was manipulated within subjects, and inverted faces were included. Our results show that study-test consistency significantly enhanced the holistic processing of upright and inverted faces; this study-test consistency effect and holistic processing were not modulated by motion and viewpoint changes via depth rotation. Interestingly, we found holistic processing for moving study-test consistent inverted faces, but not for static inverted faces. What these results tell us about the nature of holistic face representation is discussed in depth with respect to earlier and current theories on face processing.