Emotional Facial Expressions Research Articles

The Effect of Synchrony of Happiness on Facial Expression of Negative Emotion When Lying

The Effect of Synchrony of Happiness on Facial Expression of Negative Emotion When Lying

The relationship between charitable giving and emotional facial expressions: Results from affective computing

This study investigated the relationship between emotional states (valence, arousal, and six basic emotions) and donation size in pet charities, and it compared the effectiveness of affective computing and emotion self-report methods in assessing attractiveness. Using FaceReader software and self-report, we measured the emotional states of participants (N = 45) during the donation task. The results showed that sadness, happiness, and anger were significantly related to donation size. Sadness and anger increased donations, whereas happiness decreased them. Arousal was not significantly correlated with the willingness to donate. These results are supported by both methods, whereas the self-reported data regarding the association of surprise, fear, and disgust with donation size are inconclusive. Thus, unpleasant emotions increase donation size, and combining affective computing with self-reported data improves the prediction of the effectiveness of a charity appeal. This study contributes to the understanding of the relationship between emotions and charitable behavior toward pet charities and evaluates the effectiveness of marketing mix elements using affective computing. The limitations include the laboratory setting for this experiment and the lack of measurement of prolonged and repeated exposure to unpleasant charity appeals.

The power of personal control: Task choice attenuates the effect of implicit sadness on sympathetically mediated cardiac response.

Implicitly processed pictures of facial expressions of emotions have been found to systematically influence sympathetically mediated cardiovascular reactivity during task performance. According to the Implicit-Affect-Primes-Effort model, this happens because different affect primes activate the concepts of performance ease versus performance difficulty. Grounded in a recent action shielding model, our laboratory experiment (N = 129 university students) tested whether engaging in action by personal choice can immunize against those implicit affective influences on effort. Participants worked on an objectively difficult cognitive task, which was either externally assigned or ostensibly personally chosen. As predicted, participants in the assigned task condition showed weaker cardiac pre-ejection period reactivity during task performance, reflecting disengagement, when they were primed with sadness than when they were exposed to anger primes. Most relevant, this affect prime effect disappeared when participants could ostensibly choose their task themselves. These findings replicate previous research on implicit affect's impact on sympathetically mediated cardiac response and extend the literature on action shielding by personal choice effects to implicit affective influences on action execution.

Psychopathy and gaze cueing

Background and objectivesPsychopathic traits – and especially callous affective features – have been linked to altered processing of others’ emotional expressions, and to reduced attention to the eyes. Despite the importance of gaze cueing (i.e., the tendency to orient attention toward where someone else is looking) for social functioning, few studies have investigated relationships between psychopathic traits and gaze cueing, and whether facial emotional expression influence these relationships, obtaining mixed results. To address this gap, the present study aimed to evaluate associations between psychopathic traits and gaze cueing for emotional and neutral expressions. Methods65 non-clinical male participants (Mage = 27.3 years) completed two self-report measures of psychopathy and performed laboratory tasks to assess gaze-cueing for emotional vs. neutral faces and an arrow-cueing task as a comparison. ResultsLinear mixed models showed no significant associations of emotional (versus neutral) expressions, or psychopathy trait dimensions, with either gaze cueing or arrow cueing. LimitationsReliance on a convenience sample of non-clinical men, assessed with self-reports measures of psychopathy, and using static emotional stimuli limit the generalizability of our findings. ConclusionsFindings suggest that psychopathic traits are not associated with individual differences in following others’ gaze to direct attention, and that there was no advantage for affective relative to neutral expressions.

Advances and Challenges in Human Emotion Recognition Systems: A Comprehensive Review

It's really difficult to tell what a person is feeling simply by glancing at their face or their behaviour. A basic human quality, the ability to decipher nonverbal clues from body language and facial expressions is essential for social and everyday communication. People use voice, gestures, and emotions to communicate with one another. Thus, there is a high need in various industries for systems that can identify the same. In terms of artificial intelligence, if a computer can recognize and interpret human emotions, it will be much easier for it to engage with people. A number of methods have been proposed in the past for evaluating human emotion. The traditional techniques essentially use visual and auditory cues to simulate human emotional reactions, including speech, body language, and facial expressions. Characterizing emotional states by physiological reactions has garnered more attention in recent times. Rapid advances in technology should make it possible for complex and perceptive HCI (human-computer interaction) systems to consider emotional states of humans during interactions, promoting empathy between humans and machines. Intelligent Human-Computer Interaction (HCI) applications, including virtual reality, video games, and educational systems, need the ability to recognize emotions. In the medical domain, feelings that people identify with each other could be a sign of specific functional issues, such as severe depression. The primary goal of this review is to examine alternative methods for identifying emotions using five distinct approaches, rank and explain the best methods along with their benefits, and provide commentary. In an effort to improve human-computer interactions, this article intends to be a resource for all academics and students researching in the field of emotion detection.

The Emerging Role of Artificial Intelligence Tools for Outcome Measurement in Facial Reanimation Surgery: A Review.

Importance: Surgeons treating facial paralysis with reanimation surgery measure the outcomes of surgery and adjust treatment to each patient's needs. Our objective is to review the current subjective facial paralysis assessment tools and the emerging computer-based objective analysis, which may involve artificial intelligence. Observations: In recent years, many new automated approaches to outcome measurement in facial reanimation surgery have been developed. Most of these tools utilize artificial intelligence to analyze emotional expression and symmetry of facial landmarks. Other tools have provided automated approaches to existing clinician-guided scales. Conclusions: Newly developed computer-based tools using artificial intelligence have been developed to both improve existing clinician-graded scales and provide new approaches to facial symmetry and emotional expressivity analysis.

Real-time emotion generation in human-robot dialogue using large language models.

Affective behaviors enable social robots to not only establish better connections with humans but also serve as a tool for the robots to express their internal states. It has been well established that emotions are important to signal understanding in Human-Robot Interaction (HRI). This work aims to harness the power of Large Language Models (LLM) and proposes an approach to control the affective behavior of robots. By interpreting emotion appraisal as an Emotion Recognition in Conversation (ERC) tasks, we used GPT-3.5 to predict the emotion of a robot's turn in real-time, using the dialogue history of the ongoing conversation. The robot signaled the predicted emotion using facial expressions. The model was evaluated in a within-subjects user study (N = 47) where the model-driven emotion generation was compared against conditions where the robot did not display any emotions and where it displayed incongruent emotions. The participants interacted with the robot by playing a card sorting game that was specifically designed to evoke emotions. The results indicated that the emotions were reliably generated by the LLM and the participants were able to perceive the robot's emotions. It was found that the robot expressing congruent model-driven facial emotion expressions were perceived to be significantly more human-like, emotionally appropriate, and elicit a more positive impression. Participants also scored significantly better in the card sorting game when the robot displayed congruent facial expressions. From a technical perspective, the study shows that LLMs can be used to control the affective behavior of robots reliably in real-time. Additionally, our results could be used in devising novel human-robot interactions, making robots more effective in roles where emotional interaction is important, such as therapy, companionship, or customer service.

Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study

Background: New advances in the field of machine learning make it possible to track facial emotional expression with high resolution, including micro-expressions. These advances have promising applications for psychotherapy research, since manual coding (e.g., the Facial Action Coding System), is time-consuming. Purpose: We tested whether this technology can reliably identify in-session emotional expression in a naturalistic treatment setting, and how these measures relate to the outcome of psychotherapy. Method: We applied a machine learning emotion classifier to video material from 389 psychotherapy sessions of 23 patients with borderline personality pathology. We validated the findings with human ratings according to the Clients Emotional Arousal Scale (CEAS) and explored associations with treatment outcomes. Results: Overall, machine learning ratings showed significant agreement with human ratings. Machine learning emotion classifiers, particularly the display of positive emotions (smiling and happiness), showed medium effect size on median-split treatment outcome (d = 0.3) as well as continuous improvement (r = 0.49, p < 0.05). Patients who dropped out form psychotherapy, showed significantly more neutral expressions, and generally less social smiling, particularly at the beginning of psychotherapeutic sessions. Conclusions: Machine learning classifiers are a highly promising resource for research in psychotherapy. The results highlight differential associations of displayed positive and negative feelings with treatment outcomes. Machine learning emotion recognition may be used for the early identification of drop-out risks and clinically relevant interactions in psychotherapy.

Influence of stimulus manipulation on conscious awareness of emotional facial expressions in the match-to-sample paradigm

The conscious perception of emotional facial expressions plays an indispensable role in social interaction. However, previous psychological studies have reported inconsistent findings regarding whether conscious awareness is greater for emotional expressions than for neutral expressions. Furthermore, whether this phenomenon is attributable to emotional or visual factors remains unknown. To investigate these issues, we conducted five psychological experiments to test the conscious perception of emotional and neutral facial expressions using the match-to-sample paradigm. Facial stimuli were momentarily presented in the peripheral visual fields while participants read simultaneously presented letters in the central visual fields. The participants selected a perceived face from nine samples. The results of all experiments demonstrated that emotional expressions were more accurately identified than neutral expressions. Furthermore, Experiment 4 showed that angry expressions were identified more accurately than anti-angry expressions, which expressed neutral emotions with comparable physical changes to angry expressions. Experiment 5, testing the interaction between emotional expression and face direction, showed that angry expressions looking toward participants were more accurately identified than those looking away from participants, even though they were physically identical. These results suggest that the conscious awareness of emotional facial expressions is enhanced by their emotional significance.

To see or not to see: the parallel processing of self-relevance and facial expressions

The self, like the concept of central "gravity", facilitates the processing of information that is directly relevant to the self. This phenomenon is known as the self-prioritization effect. However, it remains unclear whether the self-prioritization effect extends to the processing of emotional facial expressions. To fill this gap, we used a self-association paradigm to investigate the impact of self-relevance on the recognition of emotional facial expressions while controlling for confounding factors such as familiarity and overlearning. Using a large and diverse sample, we replicated the effect of self-relevance on face processing but found no evidence for a modulation of self-relevance on facial emotion recognition. We propose two potential theoretical explanations to account for these findings and emphasize that further research with different experimental designs and a multitasks measurement approach is needed to understand this mechanism fully. Overall, our study contributes to the literature on the parallel cognitive processing of self-relevance and facial emotion recognition, with implications for both social and cognitive psychology.

Supraphysiological testosterone levels from anabolic steroid use and reduced sensitivity to negative facial expressions in men

RationaleAnabolic-androgenic steroids (AAS) are used to improve physical performance and appearance, but have been associated with deficits in social cognitive functioning. Approximately 30% of people who use AAS develop a dependence, increasing the risk for undesired effects.ObjectivesTo assess the relationship between AAS use (current/previous), AAS dependence, and the ability to recognize emotional facial expressions, and investigate the potential mediating role of hormone levels.MethodsIn total 156 male weightlifters, including those with current (n = 45) or previous (n = 34) AAS use and never-using controls (n = 77), completed a facial Emotion Recognition Task (ERT). Participants were presented with faces expressing one out of six emotions (sadness, happiness, fear, anger, disgust, and surprise) and were instructed to indicate which of the six emotions each face displayed. ERT accuracy and response time were recorded and evaluated for association with AAS use status, AAS dependence, and serum reproductive hormone levels. Mediation models were used to evaluate the mediating role of androgens in the relationship between AAS use and ERT performance.ResultsCompared to never-using controls, men currently using AAS exhibited lower recognition accuracy for facial emotional expressions, particularly anger (Cohen’s d = −0.57, pFDR = 0.03) and disgust (d = −0.51, pFDR = 0.05). Those with AAS dependence (n = 47) demonstrated worse recognition of fear relative to men without dependence (d = 0.58, p = 0.03). Recognition of disgust was negatively correlated with serum free testosterone index (FTI); however, FTI did not significantly mediate the association between AAS use and recognition of disgust.ConclusionsOur findings demonstrate impaired facial emotion recognition among men currently using AAS compared to controls. While further studies are needed to investigate potential mechanisms, our analysis did not support a simple mediation effect of serum FTI.

Recalled protective childhood experiences are associated with positive emotional expression in active-duty firefighters.

There is a wealth of research linking adverse childhood experiences (ACEs) with negative outcomes later in life, but less research has focused on the effects of protective childhood experiences (PCEs). PCEs have been shown broadly to promote resilience, but the precise nature of this association is not clear, particularly in studies of at-risk adults. The current investigation explored the association between recollection of early life experiences and in vivo emotional responses in at-risk adults. In 2018, 56 active-duty firefighters reported childhood experiences via a questionnaire. Using a semistructured interview, firefighters then responded to questions about recent emotionally evocative experiences (positive and negative) as a firefighter. Emotion was measured in vivo via sympathetic arousal and recorded facial expressions of emotion. Individuals who reported more PCEs expressed significantly more facial expressions of positive emotion across contexts, sr2 = .11-.14. Early childhood experiences were not significantly associated with negative facial expressions or changes in sympathetic reactivity, but ACEs were associated with tonic levels of arousal in a negative context, r = -.36. Given the adaptive role of positive emotions in adjustment to adverse events, the current investigation suggests a potential protective association between PCEs and the expression of emotion even within a high-risk context, which could help explain the resilience promoting role of PCEs.

Automated coding Non-manual Markers: Sign language corpora analysis using FaceReader

The present study aims to introduce the possibility of coding facial expressions and head movements in different sign languages using the FaceReader program. Considering that Non-Manuals Markers (NMMs) (facial expressions, head and torso movements) are an important part of the signed languages grammar, the FaceReader’s automatic coding ability would be very useful for investigating a larger number of signed data, The program can calibrate participants’ faces, automatically annotate emotions (emotive facial expressions), Action Units (AUs) (grammatical NMMs), their intensities, as well as head movements. Overall, it seems highly beneficial to use the program for recognizing facial and head movements in sign languages. However, there are some disadvantages to adopting it, such as pricing, the lack of torso movements and head protraction/retraction coding, and difficulties in analyzing participants with beards and glasses. Some of these disadvantages can be addressed through the use of other softwares or methodologies strategies.

Trust in the police and affective evaluation of police faces: a preliminary study.

A study was conducted to investigate if an individual's trust in law enforcement affects their perception of the emotional facial expressions displayed by police officers. The study invited 77 participants to rate the valence of 360 face images. Images featured individuals without headgear (condition 1), or with a baseball cap (condition 2) or police hat (condition 3) digitally added to the original photograph. The images were balanced across sex, race/ethnicity (Asian, African American, Latine, and Caucasian), and facial expression (Happy, Neutral, and Angry). After rating the facial expressions, respondents completed a survey about their attitudes toward the police. The results showed that, on average, valence ratings for "Angry" faces were similar across all experimental conditions. However, a closer examination revealed that faces with police hats were perceived as angrier compared to the control conditions (those with no hat and those with a baseball cap) by individuals who held negative views of the police. Conversely, participants with positive attitudes toward the police perceived faces with police hats as less angry compared to the control condition. This correlation was highly significant for angry faces (p < 0.01), and stronger in response to male faces compared to female faces but was not significant for neutral or happy faces. The study emphasizes the substantial role of attitudes in shaping social perception, particularly within the context of law enforcement.

Emotional face expressions and group membership: Does affective mismatch induce conflict?

When asked to judge or react to a facial emotional display of a person, people do not only take the emotion into account, but also other socially important features of the face, such as, for example, ethnicity (Kozlik & Fischer, 2020; Paulus & Wentura, 2014). Importantly, the emotion-related and nonemotion-related features are seemingly not (or not always) processed in a simple, additive manner, but are-in a more functional manner-integrated to provide an "amalgamated signal" on which individuals base their judgment and responses. Whereas Paulus and Wentura (2014) put forward a social-message account of this amalgamated signal, Kozlik and Fischer (2020) recently proposed a processing-conflict explanation. The empirical evidence regarding this issue is, however, mixed. In three experiments, we aimed at replicating and extending Kozlik and Fischer's central experiment to gain further insight into the validity of the social-message versus the processing-conflict account. However, we failed to replicate their findings. The implications of the new evidence for the two accounts are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estradiol effects on an emotional interference task in adolescents with current and remitted depression

Attentional biases to emotional stimuli are thought to reflect vulnerability for mood disorder onset and maintenance. This study examined the association between the endogenous sex hormone estradiol and emotional attentional biases in adolescent females with either current or remitted depression. Three groups of participants (mean age ± SD) completed the Emotional Interrupt Task: 1) 20 adolescent females (15.1 ± 1.83 years) currently diagnosed with Major Depressive Disorder (MDD), 2) 16 adolescent females (16.4 ± 1.31 years) who had experienced at least one episode of MDD in their lifetime but currently met criteria for MDD in remission, and 3) 30 adolescent female (15.4 ± 1.83 years) healthy controls. Attentional interference (AI) scores were calculated as differences in target response reaction time between trials with emotional facial expressions versus neutral facial expressions. Estradiol levels were assayed by Salimetrics LLC using saliva samples collected within 30 min of waking on assessment days. Robust multiple regression with product terms evaluated estradiol's main effect on AI scores, as well as hypothesized estradiol × diagnostic group interactions. Although neither mean estradiol levels nor mean AI scores in the current-MDD and remitted-MDD groups differed from controls, the relationship between estradiol and overall AI score differed between control adolescents and the remitted-MDD group. Specifically, the remitted-MDD adolescents performed worse (i.e., showed greater attentional interference) when they had higher estradiol; no significant relationship existed in the current-MDD group. Because this finding was driven by angry and not happy stimuli, it appears higher estradiol levels were associated with greater susceptibility to the attention-capturing effects of negatively-valenced emotional content in girls at risk for MDD from prior history.

Attention biases for emotional facial expressions during a free viewing task increase between 2.5 and 5 years of age.

The normative, developmental changes in affect-biased attention during the preschool years are largely unknown. To investigate the attention bias for emotional versus neutral faces, an eye-tracking measurement and free viewing of paired pictures of facial expressions (i.e., happy, fearful, sad, or angry faces) and nonface pictures with neutral faces were conducted with 367 children participating in a Finnish cohort study at the age of 2.5 years and with 477 children at the age of 5 years, 216 of which having follow-up measurements. We found an attention-orienting bias for happy and fearful faces versus neutral faces at both age points. An attention-orienting bias for sad faces emerged between 2.5 and 5 years. In addition, there were significant biases in sustained attention toward happy, fearful, sad, and angry faces versus neutral faces, with a bias in sustained attention for fearful faces being the strongest. All biases in sustained attention increased between 2.5 and 5 years of age. Moderate correlations in saccadic latencies were found between 2.5 and 5 years. In conclusion, attention biases for emotional facial expressions seem to be age-specific and specific for the attentional subcomponent. This implies that future studies on affect-biased attention during the preschool years should use small age ranges and cover multiple subcomponents of attention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Face perception and facial emotional expression recognition ability: Both unique predictors of the broader autism phenotype.

Autism spectrum disorder (ASD) and the broader autistic phenotype (BAP) have been suggested to be associated with perceptual-cognitive difficulties processing human faces. However, the empirical results are mixed, arguably, in part due to inadequate samples and analyses. Consequently, we administered the Cambridge Face Perception Test (CFPT), the Reading the Mind in the Eyes Test (RMET), a vocabulary test, and the Autism Quotient (AQ) to a sample of 318 adults in the general community. Based on a disattenuated path analytic modelling strategy, we found that both face perception ability (β = -.21) and facial emotional expression recognition ability (β = -.27) predicted uniquely and significantly the Communication dimension of AQ. Vocabulary failed to yield a significant, direct effect onto the Communication dimension of the AQ. We conclude that difficulties perceiving information from the faces of others may contribute to difficulties in nonverbal communication, as conceptualised and measured within the context of BAP.

Functional connectivity between the amygdala and prefrontal cortex underlies processing of emotion ambiguity

Processing facial expressions of emotion draws on a distributed brain network. In particular, judging ambiguous facial emotions involves coordination between multiple brain areas. Here, we applied multimodal functional connectivity analysis to achieve network-level understanding of the neural mechanisms underlying perceptual ambiguity in facial expressions. We found directional effective connectivity between the amygdala, dorsomedial prefrontal cortex (dmPFC), and ventromedial PFC, supporting both bottom-up affective processes for ambiguity representation/perception and top-down cognitive processes for ambiguity resolution/decision. Direct recordings from the human neurosurgical patients showed that the responses of amygdala and dmPFC neurons were modulated by the level of emotion ambiguity, and amygdala neurons responded earlier than dmPFC neurons, reflecting the bottom-up process for ambiguity processing. We further found parietal-frontal coherence and delta-alpha cross-frequency coupling involved in encoding emotion ambiguity. We replicated the EEG coherence result using independent experiments and further showed modulation of the coherence. EEG source connectivity revealed that the dmPFC top-down regulated the activities in other brain regions. Lastly, we showed altered behavioral responses in neuropsychiatric patients who may have dysfunctions in amygdala-PFC functional connectivity. Together, using multimodal experimental and analytical approaches, we have delineated a neural network that underlies processing of emotion ambiguity.

Synchronized affect in shared experiences strengthens social connection

People structure their days to experience events with others. We gather to eat meals, watch TV, and attend concerts together. What constitutes a shared experience and how does it manifest in dyadic behavior? The present study investigates how shared experiences—measured through emotional, motoric, physiological, and cognitive alignment—promote social bonding. We recorded the facial expressions and electrodermal activity (EDA) of participants as they watched four episodes of a TV show for a total of 4 h with another participant. Participants displayed temporally synchronized and spatially aligned emotional facial expressions and the degree of synchronization predicted the self-reported social connection ratings between viewing partners. We observed a similar pattern of results for dyadic physiological synchrony measured via EDA and their cognitive impressions of the characters. All four of these factors, temporal synchrony of positive facial expressions, spatial alignment of expressions, EDA synchrony, and character impression similarity, contributed to a latent factor of a shared experience that predicted social connection. Our findings suggest that the development of interpersonal affiliations in shared experiences emerges from shared affective experiences comprising synchronous processes and demonstrate that these complex interpersonal processes can be studied in a holistic and multi-modal framework leveraging naturalistic experimental designs.