Human Observers and Automated Assessment of Dynamic Emotional Facial Expressions: KDEF-dyn Database Validation

Social cognition, psychosocial development and well-being in galactosemia

BACKGROUND

Classic galactosemia is a rare inherited metabolic disease with long-term complications, particularly in the psychosocial domain. Patients report a lower quality of social life, difficulties in interactions and social relationships, and a lower mental health. We hypothesised that social cognition deficits could partially explain this psychological symptomatology. Eleven adults with galactosemia and 31 control adults participated in the study. We measured social cognition skills in cognitive and affective theory of Mind, and in basic and complex emotion recognition. We explored psychosocial development and mental well-being.

RESULTS

We found significant deficits on all 4 social cognition measures. Compared to controls, participants with galactosemia were impaired in the 2nd-order cognitive theory of mind, in affective theory of mind, and in basic and complex emotion recognition. Participants with galactosemia had a significant delay in their psychosexual development, but we found no delay in social development and no significant decrease in mental health.

CONCLUSION

Social cognition processes seem impaired among our participants with galactosemia. We discuss the future path research may follow. More research is needed to replicate and strengthen these results and establish the links between psychosocial complications and deficits in social cognition.

EEG correlates of static and dynamic face perception: the role of naturalistic motion

Much of our understanding of how the brain processes dynamic faces comes from research that compares static photographs to dynamic morphs, which exhibit simplified, computer-generated motion. By comparing static, video recorded, and dynamic morphed expressions, we aim to identify the neural correlates of naturalistic facial dynamism, using time-domain and time-frequency analysis. Dynamic morphs were made from the neutral and peak frames of video recorded transitions of happy and fearful expressions, which retained expression change and removed asynchronous and non-linear features of naturalistic facial motion. We found that dynamic morphs elicited increased N400 amplitudes and lower LPP amplitudes compared to other stimulus types. Video recordings elicited higher LPP amplitudes and greater frontal delta activity compared to other stimuli. Thematic analysis of participant interviews using a large language model revealed that participants found it difficult to assess the genuineness of morphed expressions, and easier to analyse the genuineness of happy compared to fearful expressions. Our findings suggest that animating real faces with artificial motion may violate expectations (N400) and reduce the social salience (LPP) of dynamic morphs. Results also suggest that delta oscillations in the frontal region may be involved with the perception of naturalistic facial motion in happy and fearful expressions. Overall, our findings highlight the sensitivity of neural mechanisms required for face perception to subtle changes in facial motion characteristics, which has important implications for neuroimaging research using faces with simplified motion.

Horses can learn to identify joy and sadness against other basic emotions from human facial expressions

Recently, horses and other domestic mammals have been shown to perceive and react to human emotional signals, with most studies focusing on joy and anger. In this study, we tested whether horses can learn to identify human joyful and sad expressions against other emotions. We used a touchscreen-based automated device that presented pairs of human portraits and distributed pellets when the horse touched the rewarded face. Six horses were trained to touch the sad face and 5 the joyful face. By the end of training, horses' performances at the group level were significantly higher than chance level, with higher scores for horses trained with the sad face. At the individual level, evidence of task learning varied among horses, which could be explained by individual variations in horses' ability to identify different human facial expressions or attention issues during the tests. In a generalization test, we introduced portraits of different humans than those presented during training. Horses trained with the joyful face performed better than chance, demonstrating generalization. Conversely, horses trained with the sad face did not. Horses also showed differences in learning performance according to the non-rewarded emotion, providing insights into horses' cognitive processing of facial expressions.

Human and machine recognition of dynamic and static facial expressions: prototypicality, ambiguity, and complexity

A growing body of research suggests that movement aids facial expression recognition. However, less is known about the conditions under which the dynamic advantage occurs. The aim of this research was to test emotion recognition in static and dynamic facial expressions, thereby exploring the role of three featural parameters (prototypicality, ambiguity, and complexity) in human and machine analysis. In two studies, facial expression videos and corresponding images depicting the peak of the target and non-target emotion were presented to human observers and the machine classifier (FACET). Results revealed higher recognition rates for dynamic stimuli compared to non-target images. Such benefit disappeared in the context of target-emotion images which were similarly well (or even better) recognised than videos, and more prototypical, less ambiguous, and more complex in appearance than non-target images. While prototypicality and ambiguity exerted more predictive power in machine performance, complexity was more indicative of human emotion recognition. Interestingly, recognition performance by the machine was found to be superior to humans for both target and non-target images. Together, the findings point towards a compensatory role of dynamic information, particularly when static-based stimuli lack relevant features of the target emotion. Implications for research using automatic facial expression analysis (AFEA) are discussed.

New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review

Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; therefore, detection and recognition accuracies are very important. Similar to any computer vision task based on image analyses, FER solutions are also suitable for integration with artificial intelligence solutions represented by different neural network varieties, especially deep neural networks that have shown great potential in the last years due to their feature extraction capabilities and computational efficiency over large datasets. In this context, this paper reviews the latest developments in the FER area, with a focus on recent neural network models that implement specific facial image analysis algorithms to detect and recognize facial emotions. This paper's scope is to present from historical and conceptual perspectives the evolution of the neural network architectures that proved significant results in the FER area. This paper endorses convolutional neural network (CNN)-based architectures against other neural network architectures, such as recurrent neural networks or generative adversarial networks, highlighting the key elements and performance of each architecture, and the advantages and limitations of the proposed models in the analyzed papers. Additionally, this paper presents the available datasets that are currently used for emotion recognition from facial expressions and micro-expressions. The usage of FER systems is also highlighted in various domains such as healthcare, education, security, or social IoT. Finally, open issues and future possible developments in the FER area are identified.

Facial Expressions as an Index of Listening Difficulty and Emotional Response

Knowledge about listening difficulty experienced during a task can be used to better understand speech perception processes, to guide amplification outcomes, and can be used by individuals to decide whether to participate in communication. Another factor affecting these decisions is individuals' emotional response which has not been measured objectively previously. In this study, we describe a novel method of measuring listening difficulty and affect of individuals in adverse listening situations using automatic facial expression algorithm. The purpose of our study was to determine if facial expressions of confusion and frustration are sensitive to changes in listening difficulty. We recorded speech recognition scores, facial expressions, subjective listening effort scores, and subjective emotional responses in 33 young participants with normal hearing. We used the signal-to-noise ratios of -1, +2, and +5 dB SNR and quiet conditions to vary the difficulty level. We found that facial expression of confusion and frustration increased with increase in difficulty level, but not with change in each level. We also found a relationship between facial expressions and both subjective emotion ratings and subjective listening effort. Emotional responses in the form of facial expressions show promise as a measure of affect and listening difficulty. Further research is needed to determine the specific contribution of affect to communication in challenging listening environments.

Words matter when inferring emotions: a conceptual replication and extension

It is long known that facial configurations play a critical role when inferring mental and emotional states from others. Nevertheless, there is still a scientific debate on how we infer emotions from facial configurations. The theory of constructed emotion (TCE) suggests that we may infer different emotions from the same facial configuration, depending on the context (e.g. provided by visual and lexical cues) in which they are perceived. For instance, a recent study found that participants were more accurate in inferring mental and emotional states across three different datasets (i.e. RMET, static and dynamic emojis) when words were provided (i.e. forced-choice task), compared to when they were not (i.e. free-labelling task), suggesting that words serve as contexts that modulate the inference from facial configurations. The goal of the current within-subject study was to replicate and extend these findings by adding a fourth dataset (KDEF-dyn), consisting of morphed human faces (to increase the ecological validity). Replicating previous findings, we observed that words increased accuracy across the three (previously used) datasets, an effect that was also observed for the facial morphed stimuli. Our findings are in line with the TCE, providing support for the importance of contextual verbal cues in emotion perception.

Training machine learning algorithms for automatic facial coding: The role of emotional facial expressions' prototypicality

Automatic facial coding (AFC) is a promising new research tool to efficiently analyze emotional facial expressions. AFC is based on machine learning procedures to infer emotion categorization from facial movements (i.e., Action Units). State-of-the-art AFC accurately classifies intense and prototypical facial expressions, whereas it is less accurate for non-prototypical and less intense facial expressions. A potential reason might be that AFC is typically trained with standardized and prototypical facial expression inventories. Because AFC would be useful to analyze less prototypical research material as well, we set out to determine the role of prototypicality in the training material. We trained established machine learning algorithms either with standardized expressions from widely used research inventories or with unstandardized emotional facial expressions obtained in a typical laboratory setting and tested them on identical or cross-over material. All machine learning models' accuracies were comparable when trained and tested with held-out dataset from the same dataset (acc. = [83.4% to 92.5%]). Strikingly, we found a substantial drop in accuracies for models trained with the highly prototypical standardized dataset when tested in the unstandardized dataset (acc. = [52.8%; 69.8%]). However, when they were trained with unstandardized expressions and tested with standardized datasets, accuracies held up (acc. = [82.7%; 92.5%]). These findings demonstrate a strong impact of the training material's prototypicality on AFC's ability to classify emotional faces. Because AFC would be useful for analyzing emotional facial expressions in research or even naturalistic scenarios, future developments should include more naturalistic facial expressions for training. This approach will improve the generalizability of AFC to encode more naturalistic facial expressions and increase robustness for future applications of this promising technology.

Automatic facial coding predicts self-report of emotion, advertisement and brand effects elicited by video commercials

Introduction

Consumers' emotional responses are the prime target for marketing commercials. Facial expressions provide information about a person's emotional state and technological advances have enabled machines to automatically decode them.

Method

With automatic facial coding we investigated the relationships between facial movements (i.e., action unit activity) and self-report of commercials advertisement emotion, advertisement and brand effects. Therefore, we recorded and analyzed the facial responses of 219 participants while they watched a broad array of video commercials.

Results

Facial expressions significantly predicted self-report of emotion as well as advertisement and brand effects. Interestingly, facial expressions had incremental value beyond self-report of emotion in the prediction of advertisement and brand effects. Hence, automatic facial coding appears to be useful as a non-verbal quantification of advertisement effects beyond self-report.

Discussion

This is the first study to measure a broad spectrum of automatically scored facial responses to video commercials. Automatic facial coding is a promising non-invasive and non-verbal method to measure emotional responses in marketing.

The Feasibility of An Automatical Facial Evaluation System Providing Objective and Reliable Results for Facial Palsy

Facial palsy would lead to a series of physical and mental problems, as facial function plays an important role in various aspects of daily life. However, the current strategies for evaluating facial function relied heavily on raters and the results varied from the experience of raters. Thus, an objective and accurate facial evaluation system is always claimed. In this study, a customized automatical facial evaluation system (AFES) was proposed, which might have the potential to be employed as an adjunctive and efficient assessing method in clinic. In order to investigate the feasibility of AFES, ninety-two participants with facial palsy were recruited and received scale-based subjective manual evaluation (including mHBGS and mSFGS) and objective automatical evaluation of AFES (including aHBGS, aSFGS and indicators of facial regional features) at enrollment and after two weeks. The correlations between the results of the two methods were analyzed and the participants were stratified according to the severity of facial function for further analyses. Strong positive correlations between manual and automatical HBGS and SFGS were observed and higher correlations were reported in the participants with normal-mild and moderate facial palsy. Significant improvements in clinical scales and indicator of eye synkinesis were found in forty-two participants in two weeks. Furthermore, some of the indicators were correlated with scale scores (I4, I7) and one of them presented a significant change between the baseline evaluation and follow-up evaluation (I7). According to the results, AFES could be considered as a viable method to perform objective and reliable evaluation for patients with facial palsy and provide clarified results for prognosis.

What is missing in the study of emotion expression?

While approaching celebrations for the 150 years of "The Expression of the Emotions in Man and Animals", scientists' conclusions on emotion expression are still debated. Emotion expression has been traditionally anchored to prototypical and mutually exclusive facial expressions (e.g., anger, disgust, fear, happiness, sadness, and surprise). However, people express emotions in nuanced patterns and - crucially - not everything is in the face. In recent decades considerable work has critiqued this classical view, calling for a more fluid and flexible approach that considers how humans dynamically perform genuine expressions with their bodies in context. A growing body of evidence suggests that each emotional display is a complex, multi-component, motoric event. The human face is never static, but continuously acts and reacts to internal and environmental stimuli, with the coordinated action of muscles throughout the body. Moreover, two anatomically and functionally different neural pathways sub-serve voluntary and involuntary expressions. An interesting implication is that we have distinct and independent pathways for genuine and posed facial expressions, and different combinations may occur across the vertical facial axis. Investigating the time course of these facial blends, which can be controlled consciously only in part, is recently providing a useful operational test for comparing the different predictions of various models on the lateralization of emotions. This concise review will identify shortcomings and new challenges regarding the study of emotion expressions at face, body, and contextual levels, eventually resulting in a theoretical and methodological shift in the study of emotions. We contend that the most feasible solution to address the complex world of emotion expression is defining a completely new and more complete approach to emotional investigation. This approach can potentially lead us to the roots of emotional display, and to the individual mechanisms underlying their expression (i.e., individual emotional signatures).

Automated detection of smiles as discrete episodes

BACKGROUND

Patients seeking restorative and orthodontic treatment expect an improvement in their smiles and oral health-related quality of life. Nonetheless, the qualitative and quantitative characteristics of dynamic smiles are yet to be understood.

OBJECTIVE

To develop, validate, and introduce open-access software for automated analysis of smiles in terms of their frequency, genuineness, duration, and intensity.

MATERIALS AND METHODS

A software script was developed using the Facial Action Coding System (FACS) and artificial intelligence to assess activations of (1) cheek raiser, a marker of smile genuineness; (2) lip corner puller, a marker of smile intensity; and (3) perioral lip muscles, a marker of lips apart. Thirty study participants were asked to view a series of amusing videos. A full-face video was recorded using a webcam. The onset and cessation of smile episodes were identified by two examiners trained with FACS coding. A Receiver Operating Characteristic (ROC) curve was then used to assess detection accuracy and optimise thresholding. The videos of participants were then analysed off-line to automatedly assess the features of smiles.

RESULTS

The area under the ROC curve for smile detection was 0.94, with a sensitivity of 82.9% and a specificity of 89.7%. The software correctly identified 90.0% of smile episodes. While watching the amusing videos, study participants smiled 1.6 (±0.8) times per minute.

CONCLUSIONS

Features of smiles such as frequency, duration, genuineness, and intensity can be automatedly assessed with an acceptable level of accuracy. The software can be used to investigate the impact of oral conditions and their rehabilitation on smiles.

Unity Assumption in Audiovisual Emotion Perception

We experience various sensory stimuli every day. How does this integration occur? What are the inherent mechanisms in this integration? The “unity assumption” proposes a perceiver’s belief of unity in individual unisensory information to modulate the degree of multisensory integration. However, this has yet to be verified or quantified in the context of semantic emotion integration. In the present study, we investigate the ability of subjects to judge the intensities and degrees of similarity in faces and voices of two emotions (angry and happy). We found more similar stimulus intensities to be associated with stronger likelihoods of the face and voice being integrated. More interestingly, multisensory integration in emotion perception was observed to follow a Gaussian distribution as a function of the emotion intensity difference between the face and voice—the optimal cut-off at about 2.50 points difference on a 7-point Likert scale. This provides a quantitative estimation of the multisensory integration function in audio-visual semantic emotion perception with regards to stimulus intensity. Moreover, to investigate the variation of multisensory integration across the population, we examined the effects of personality and autistic traits of participants. Here, we found no correlation of autistic traits with unisensory processing in a nonclinical population. Our findings shed light on the current understanding of multisensory integration mechanisms.

What's in a face: Automatic facial coding of untrained study participants compared to standardized inventories

Automatic facial coding (AFC) is a novel research tool to automatically analyze emotional facial expressions. AFC can classify emotional expressions with high accuracy in standardized picture inventories of intensively posed and prototypical expressions. However, classification of facial expressions of untrained study participants is more error prone. This discrepancy requires a direct comparison between these two sources of facial expressions. To this end, 70 untrained participants were asked to express joy, anger, surprise, sadness, disgust, and fear in a typical laboratory setting. Recorded videos were scored with a well-established AFC software (FaceReader, Noldus Information Technology). These were compared with AFC measures of standardized pictures from 70 trained actors (i.e., standardized inventories). We report the probability estimates of specific emotion categories and, in addition, Action Unit (AU) profiles for each emotion. Based on this, we used a novel machine learning approach to determine the relevant AUs for each emotion, separately for both datasets. First, misclassification was more frequent for some emotions of untrained participants. Second, AU intensities were generally lower in pictures of untrained participants compared to standardized pictures for all emotions. Third, although profiles of relevant AU overlapped substantially across the two data sets, there were also substantial differences in their AU profiles. This research provides evidence that the application of AFC is not limited to standardized facial expression inventories but can also be used to code facial expressions of untrained participants in a typical laboratory setting.

Presenting KAPODI – The Searchable Database of Emotional Stimuli Sets

Emotional stimuli such as images, words, or video clips are often used in studies researching emotion. New sets are continuously being published, creating an immense number of available sets and complicating the task for researchers who are looking for suitable stimuli. This paper presents the KAPODI-database of emotional stimuli sets that are freely available or available upon request. Over 45 aspects including over 25 key set characteristics have been extracted and listed for each set. The database facilitates finding of and comparison between individual sets. It currently contains sets published between 1963 and 2020. A searchable online version ( https://airtable.com/shrnVoUZrwu6riP9b ) allows users to select specific set characteristics and to find matching sets accordingly, as well as to add new published sets.

Facial expressions of Asian people exposed to constructed urban forests: Accuracy validation and variation assessment

An outcome of building sustainable urban forests is that people's well-being is improved when they are exposed to trees. Facial expressions directly represents one's inner emotions, and can be used to assess real-time perception. The emergence and change in the facial expressions of forest visitors are an implicit process. As such, the reserved character of Asians requires an instrument rating to accurately recognize expressions. In this study, a dataset was established with 2,886 randomly photographed faces from visitors at a constructed urban forest park and at a promenade during summertime in Shenyang City, Northeast China. Six experts were invited to choose 160 photos in total with 20 images representing one of eight typical expressions: angry, contempt, disgusted, happy, neutral, sad, scared, and surprised. The FireFACE ver. 3.0 software was used to test hit-ratio validation as an accuracy measurement (ac.) to match machine-recognized photos with those identified by experts. According to the Kruskal-Wallis test on the difference from averaged scores in 20 recently published papers, contempt (ac. = 0.40%, P = 0.0038) and scared (ac. = 25.23%, P = 0.0018) expressions do not pass the validation test. Both happy and sad expression scores were higher in forests than in promenades, but there were no difference in net positive response (happy minus sad) between locations. Men had a higher happy score but lower disgusted score in forests than in promenades. Men also had a higher angry score in forests. We conclude that FireFACE can be used for analyzing facial expressions in Asian people within urban forests. Women are encouraged to visit urban forests rather than promenades to elicit more positive emotions.

Human and machine validation of 14 databases of dynamic facial expressions

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.

Automatic Facial Expression Recognition in Standardized and Non-standardized Emotional Expressions

Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems (Azure Face API, Microsoft; Face++, Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-standardized acted facial expressions (extracted from emotional movie scenes). For the standardized images, all three systems classify basic emotions accurately (FaceReader is most accurate) and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions.

Automatic facial coding versus electromyography of mimicked, passive, and inhibited facial response to emotional faces

Decoding someone's facial expressions provides insights into his or her emotional experience. Recently, Automatic Facial Coding (AFC) software has been developed to provide measurements of emotional facial expressions. Previous studies provided first evidence for the sensitivity of such systems to detect facial responses in study participants. In the present experiment, we set out to generalise these results to affective responses as they can occur in variable social interactions. Thus, we presented facial expressions (happy, neutral, angry) and instructed participants (N = 64) to either actively mimic, to look at them passively (n = 21), or to inhibit their own facial reaction (n = 22). A video stream for AFC and an electromyogram (EMG) of the zygomaticus and corrugator muscles were registered continuously. In the mimicking condition, both AFC and EMG differentiated well between facial expressions in response to the different emotional pictures. In the passive viewing and in the inhibition condition AFC did not detect changes in facial expressions whereas EMG was still highly sensitive. Although only EMG is sensitive when participants intend to conceal their facial reactions, these data extend previous findings that Automatic Facial Coding is a promising tool for the detection of intense facial reaction.

An eye-tracking study of interpersonal threat sensitivity and adverse childhood experiences in borderline personality disorder

BACKGROUND

Previous eye-tracking studies provide preliminary evidence for a hypersensitivity to negative, potentially threatening interpersonal cues in borderline personality disorder (BPD). From an etiological point of view, such interpersonal threat hypersensitivity might be explained by a biological vulnerability along with a history of early life adversities. The objective of the current study was to investigate interpersonal threat hypersensitivity and its association with adverse childhood experiences (ACE) in patients with BPD employing eye-tracking technology.

METHODS

We examined a sample of 46 unmedicated, adult female patients with BPD and 25 healthy female volunteers, matched on age and intelligence, with a well-established emotion classification paradigm with angry, fearful, happy, and neutral facial expressions. ACE were assessed retrospectively with the Childhood Trauma Questionnaire.

RESULTS

Patients as compared to healthy volunteers reflexively directed their gaze more quickly towards the eyes of emotional and neutral faces and did not adapt their fixation patterns according to the facial expression presented. Misclassifying emotional and neutral faces as angry correlated positively with the patients' self-reported ACE.

CONCLUSIONS

Building on and extending earlier findings, our results are likely to suggest a visual hypervigilance towards the eyes of emotional and neutral facial expressions and a childhood trauma-related anger bias in patients with BPD. Given the lack of a clinical control group, the question whether these findings are specific for BPD has to remain open. Thus, further research is needed to elucidate the specificity of altered visual attention allocation and the role of ACE in anger recognition in patients with BPD.

Read My Face: Automatic Facial Coding Versus Psychophysiological Indicators of Emotional Valence and Arousal

Facial expressions provide insight into a person's emotional experience. To automatically decode these expressions has been made possible by tremendous progress in the field of computer vision. Researchers are now able to decode emotional facial expressions with impressive accuracy in standardized images of prototypical basic emotions. We tested the sensitivity of a well-established automatic facial coding software program to detect spontaneous emotional reactions in individuals responding to emotional pictures. We compared automatically generated scores for valence and arousal of the Facereader (FR; Noldus Information Technology) with the current psychophysiological gold standard of measuring emotional valence (Facial Electromyography, EMG) and arousal (Skin Conductance, SC). We recorded physiological and behavioral measurements of 43 healthy participants while they looked at pleasant, unpleasant, or neutral scenes. When viewing pleasant pictures, FR Valence and EMG were both comparably sensitive. However, for unpleasant pictures, FR Valence showed an expected negative shift, but the signal differentiated not well between responses to neutral and unpleasant stimuli, that were distinguishable with EMG. Furthermore, FR Arousal values had a stronger correlation with self-reported valence than with arousal while SC was sensitive and specifically associated with self-reported arousal. This is the first study to systematically compare FR measurement of spontaneous emotional reactions to standardized emotional images with established psychophysiological measurement tools. This novel technology has yet to make strides to surpass the sensitivity of established psychophysiological measures. However, it provides a promising new measurement technique for non-contact assessment of emotional responses.

Horses Categorize Human Emotions Cross-Modally Based on Facial Expression and Non-Verbal Vocalizations

Simple Summary

Recently, an increasing number of studies have investigated the expression and perception of emotions by non-human animals. In particular, it is of interest to determine whether animals can link emotion stimuli of different modalities (e.g., visual and oral) based on the emotions that are expressed (i.e., to recognize emotions cross-modally). For domestic species that share a close relationship with humans, we might even wonder whether this ability extends to human emotions. Here, we investigated whether domestic horses recognize human emotions cross-modally. We simultaneously presented two animated pictures of human facial expressions, one typical of joy and the other of anger; simultaneously, a speaker played a human non-verbal vocalization expressing joy or anger. Horses looked at the picture that did not match the emotion of the vocalization more (probably because they were intrigued by the paradoxical combination). Moreover, their behavior and heart rate differed depending on the vocalization: they reacted more negatively to the anger vocalization and more positively to the joy vocalization. These results suggest that horses can match visual and vocal cues for the same emotion and can perceive the emotional valence of human non-verbal vocalizations.

Abstract

Over the last few years, an increasing number of studies have aimed to gain more insight into the field of animal emotions. In particular, it is of interest to determine whether animals can cross-modally categorize the emotions of others. For domestic animals that share a close relationship with humans, we might wonder whether this cross-modal recognition of emotions extends to humans, as well. In this study, we tested whether horses could recognize human emotions and attribute the emotional valence of visual (facial expression) and vocal (non-verbal vocalization) stimuli to the same perceptual category. Two animated pictures of different facial expressions (anger and joy) were simultaneously presented to the horses, while a speaker played an emotional human non-verbal vocalization matching one of the two facial expressions. Horses looked at the picture that was incongruent with the vocalization more, probably because they were intrigued by the paradoxical combination. Moreover, horses reacted in accordance with the valence of the vocalization, both behaviorally and physiologically (heart rate). These results show that horses can cross-modally recognize human emotions and react emotionally to the emotional states of humans, assessed by non-verbal vocalizations.

Selective eye fixations on diagnostic face regions of dynamic emotional expressions: KDEF-dyn database

Prior research using static facial stimuli (photographs) has identified diagnostic face regions (i.e., functional for recognition) of emotional expressions. In the current study, we aimed to determine attentional orienting, engagement, and time course of fixation on diagnostic regions. To this end, we assessed the eye movements of observers inspecting dynamic expressions that changed from a neutral to an emotional face. A new stimulus set (KDEF-dyn) was developed, which comprises 240 video-clips of 40 human models portraying six basic emotions (happy, sad, angry, fearful, disgusted, and surprised). For validation purposes, 72 observers categorized the expressions while gaze behavior was measured (probability of first fixation, entry time, gaze duration, and number of fixations). Specific visual scanpath profiles characterized each emotional expression: The eye region was looked at earlier and longer for angry and sad faces; the mouth region, for happy faces; and the nose/cheek region, for disgusted faces; the eye and the mouth regions attracted attention in a more balanced manner for surprise and fear. These profiles reflected enhanced selective attention to expression-specific diagnostic face regions. The KDEF-dyn stimuli and the validation data will be available to the scientific community as a useful tool for research on emotional facial expression processing.