Microexpressions Differentiate Truths From Lies About Future Malicious Intent

Trainee psychotherapists' emotion recognition accuracy during 1.5 years of psychotherapy education compared to a control group: no improvement after psychotherapy training

The ability to recognize and work with patients' emotions is considered an important part of most psychotherapy approaches. Surprisingly, there is little systematic research on psychotherapists' ability to recognize other people's emotional expressions. In this study, we compared trainee psychotherapists' nonverbal emotion recognition accuracy to a control group of undergraduate students at two time points: at the beginning and at the end of one and a half years of theoretical and practical psychotherapy training. Emotion recognition accuracy (ERA) was assessed using two standardized computer tasks, one for recognition of dynamic multimodal (facial, bodily, vocal) expressions and one for recognition of facial micro expressions. Initially, 154 participants enrolled in the study, 72 also took part in the follow-up. The trainee psychotherapists were moderately better at recognizing multimodal expressions, and slightly better at recognizing facial micro expressions, than the control group at the first test occasion. However, mixed multilevel modeling indicated that the ERA change trajectories for the two groups differed significantly. While the control group improved in their ability to recognize multimodal emotional expressions from pretest to follow-up, the trainee psychotherapists did not. Both groups improved their micro expression recognition accuracy, but the slope for the control group was significantly steeper than the trainee psychotherapists'. These results suggest that psychotherapy education and clinical training do not always contribute to improved emotion recognition accuracy beyond what could be expected due to time or other factors. Possible reasons for that finding as well as implications for the psychotherapy education are discussed.

Four Misconceptions About Nonverbal Communication

Research and theory in nonverbal communication have made great advances toward understanding the patterns and functions of nonverbal behavior in social settings. Progress has been hindered, we argue, by presumptions about nonverbal behavior that follow from both received wisdom and faulty evidence. In this article, we document four persistent misconceptions about nonverbal communication-namely, that people communicate using decodable body language; that they have a stable personal space by which they regulate contact with others; that they express emotion using universal, evolved, iconic, categorical facial expressions; and that they can deceive and detect deception, using dependable telltale clues. We show how these misconceptions permeate research as well as the practices of popular behavior experts, with consequences that extend from intimate relationships to the boardroom and courtroom and even to the arena of international security. Notwithstanding these misconceptions, existing frameworks of nonverbal communication are being challenged by more comprehensive systems approaches and by virtual technologies that ambiguate the roles and identities of interactants and the contexts of interaction.

Trainee psychotherapists' emotion recognition accuracy improves after training: emotion recognition training as a tool for psychotherapy education

Introduction

Psychotherapists' emotional and empathic competencies have a positive influence on psychotherapy outcome and alliance. However, it is doubtful whether psychotherapy education in itself leads to improvements in trainee psychotherapists' emotion recognition accuracy (ERA), which is an essential part of these competencies.

Methods

In a randomized, controlled, double-blind study (N = 68), we trained trainee psychotherapists (57% psychodynamic therapy and 43% cognitive behavioral therapy) to detect non-verbal emotional expressions in others using standardized computerized trainings - one for multimodal emotion recognition accuracy and one for micro expression recognition accuracy - and compared their results to an active control group one week after the training (n = 60) and at the one-year follow up (n = 55). The participants trained once weekly during a three-week period. As outcome measures, we used a multimodal emotion recognition accuracy task, a micro expression recognition accuracy task and an emotion recognition accuracy task for verbal and non-verbal (combined) emotional expressions in medical settings.

Results

The results of mixed multilevel analyses suggest that the multimodal emotion recognition accuracy training led to significantly steeper increases than the other two conditions from pretest to the posttest one week after the last training session. When comparing the pretest to follow-up differences in slopes, the superiority of the multimodal training group was still detectable in the unimodal audio modality and the unimodal video modality (in comparison to the control training group), but not when considering the multimodal audio-video modality or the total score of the multimodal emotion recognition accuracy measure. The micro expression training group showed a significantly steeper change trajectory from pretest to posttest compared to the control training group, but not compared to the multimodal training group. However, the effect vanished again until the one-year follow-up. There were no differences in change trajectories for the outcome measure about emotion recognition accuracy in medical settings.

Discussion

We conclude that trainee psychotherapists' emotion recognition accuracy can be effectively trained, especially multimodal emotion recognition accuracy, and suggest that the changes in unimodal emotion recognition accuracy (audio-only and video-only) are long-lasting. Implications of these findings for the psychotherapy education are discussed.

Attachment-Related Differences in Emotion Regulation in Adults: A Systematic Review on Attachment Representations

In recent years, there has been an increase in the prevalence of mental disorders connected with affective dysregulation and insecure attachment. Therefore, it is even more important to understand the interplay between an individual's attachment representation and patterns of emotion regulation. To our knowledge, this is the first systematic review to examine this association. PsycInfo, PsyArticles, and PubMed were searched for studies that examined attachment-related differences in emotion regulation in adults. To examine the unconscious attachment representation, only studies using the Adult Attachment Interview or the Adult Attachment Projective Picture System were included. Thirty-seven peer-reviewed studies (with a total of 2006 subjects) matched the PICO criteria. Emotion regulation was measured via four objective approaches: autonomic nervous system, brain activity, biochemistry, or nonverbal behavior. Across all measurements, results reveal a significant correlation between attachment representation and emotion regulation. Secure attachment correlates consistently with balanced emotion regulation, whereas it is impaired in insecure and dysfunctional in unresolved attachment. Specifically, unresolved individuals display counterintuitive responses and fail to use attachment as a resource. Insecure-dismissing attachment is associated with an emotionally deactivating strategy, while on a physiological, biochemical, and nonverbal level, emotional stress is still present. There is still a lack of studies examining preoccupied individuals. In addition to interpreting the results, we also discuss the risk of bias, implications for psychotherapy and coaching, and an outlook for future research.

An ingroup disadvantage in recognizing micro-expressions

Micro-expression is a fleeting facial expression of emotion that usually occurs in high-stake situations and reveals the true emotion that a person tries to conceal. Due to its unique nature, recognizing micro-expression has great applications for fields like law enforcement, medical treatment, and national security. However, the psychological mechanism of micro-expression recognition is still poorly understood. In the present research, we sought to expand upon previous research to investigate whether the group membership of the expresser influences the recognition process of micro-expressions. By conducting two behavioral studies, we found that contrary to the widespread ingroup advantage found in macro-expression recognition, there was a robust ingroup disadvantage in micro-expression recognition instead. Specifically, in Study 1A and 1B, we found that participants were more accurate at recognizing the intense and subtle micro-expressions of their racial outgroups than those micro-expressions of their racial ingroups, and neither the training experience nor the duration of micro-expressions moderated this ingroup disadvantage. In Study 2A and 2B, we further found that mere social categorization alone was sufficient to elicit the ingroup disadvantage for the recognition of intense and subtle micro-expressions, and such an effect was also unaffected by the duration of micro-expressions. These results suggest that individuals spontaneously employ the social category information of others to recognize micro-expressions, and the ingroup disadvantage in micro-expression stems partly from motivated differential processing of ingroup micro-expressions.

Oxytocin Impairs the Recognition of Micro-Expressions of Surprise and Disgust

As fleeting facial expressions which reveal the emotion that a person tries to conceal, micro-expressions have great application potentials for fields like security, national defense and medical treatment. However, the physiological basis for the recognition of these facial expressions is poorly understood. In the present research, we utilized a double-blind, placebo-controlled, mixed-model experimental design to investigate the effects of oxytocin on the recognition of micro-expressions in three behavioral studies. Specifically, in Studies 1 and 2, participants were asked to perform a laboratory-based standardized micro-expression recognition task after self-administration of a single dose of intranasal oxytocin (40 IU) or placebo (containing all ingredients except for the neuropeptide). In Study 3, we further examined the effects of oxytocin on the recognition of natural micro-expressions. The results showed that intranasal oxytocin decreased the recognition speed for standardized intense micro-expressions of surprise (Study 1) and decreased the recognition accuracy for standardized subtle micro-expressions of disgust (Study 2). The results of Study 3 further revealed that intranasal oxytocin administration significantly reduced the recognition accuracy for natural micro-expressions of surprise and disgust. The present research is the first to investigate the effects of oxytocin on micro-expression recognition. It suggests that the oxytocin mainly plays an inhibiting role in the recognition of micro-expressions and there are fundamental differences in the neurophysiological basis for the recognition of micro-expressions and macro-expressions.

We have to talk about emotional AI and crime

Emotional AI is an emerging technology used to make probabilistic predictions about the emotional states of people using data sources, such as facial (micro)-movements, body language, vocal tone or the choice of words. The performance of such systems is heavily debated and so are the underlying scientific methods that serve as the basis for many such technologies. In this article I will engage with this new technology, and with the debates and literature that surround it. Working at the intersection of criminology, policing, surveillance and the study of emotional AI this paper explores and offers a framework of understanding the various issues that these technologies present particularly to liberal democracies. I argue that these technologies should not be deployed within public spaces because there is only a very weak evidence-base as to their effectiveness in a policing and security context, and even more importantly represent a major intrusion to people’s private lives and also represent a worrying extension of policing power because of the possibility that intentions and attitudes may be inferred. Further to this, the danger in the use of such invasive surveillance for the purpose of policing and crime prevention in urban spaces is that it potentially leads to a highly regulated and control-oriented society. I argue that emotion recognition has severe impacts on the right to the city by not only undertaking surveillance of existing situations but also making inferences and probabilistic predictions about future events as well as emotions and intentions.

Training Emotion Recognition Accuracy: Results for Multimodal Expressions and Facial Micro Expressions

Nonverbal emotion recognition accuracy (ERA) is a central feature of successful communication and interaction, and is of importance for many professions. We developed and evaluated two ERA training programs-one focusing on dynamic multimodal expressions (audio, video, audio-video) and one focusing on facial micro expressions. Sixty-seven subjects were randomized to one of two experimental groups (multimodal, micro expression) or an active control group (emotional working memory task). Participants trained once weekly with a brief computerized training program for three consecutive weeks. Pre-post outcome measures consisted of a multimodal ERA task, a micro expression recognition task, and a task about patients' emotional cues. Post measurement took place approximately a week after the last training session. Non-parametric mixed analyses of variance using the Aligned Rank Transform were used to evaluate the effectiveness of the training programs. Results showed that multimodal training was significantly more effective in improving multimodal ERA compared to micro expression training or the control training; and the micro expression training was significantly more effective in improving micro expression ERA compared to the other two training conditions. Both pre-post effects can be interpreted as large. No group differences were found for the outcome measure about recognizing patients' emotion cues. There were no transfer effects of the training programs, meaning that participants only improved significantly for the specific facet of ERA that they had trained on. Further, low baseline ERA was associated with larger ERA improvements. Results are discussed with regard to methodological and conceptual aspects, and practical implications and future directions are explored.

Catching a Liar Through Facial Expression of Fear

High stakes can be stressful whether one is telling the truth or lying. However, liars can feel extra fear from worrying to be discovered than truth-tellers, and according to the "leakage theory," the fear is almost impossible to be repressed. Therefore, we assumed that analyzing the facial expression of fear could reveal deceits. Detecting and analyzing the subtle leaked fear facial expressions is a challenging task for laypeople. It is, however, a relatively easy job for computer vision and machine learning. To test the hypothesis, we analyzed video clips from a game show "The moment of truth" by using OpenFace (for outputting the Action Units (AUs) of fear and face landmarks) and WEKA (for classifying the video clips in which the players were lying or telling the truth). The results showed that some algorithms achieved an accuracy of >80% merely using AUs of fear. Besides, the total duration of AU20 of fear was found to be shorter under the lying condition than that from the truth-telling condition. Further analysis found that the reason for a shorter duration in the lying condition was that the time window from peak to offset of AU20 under the lying condition was less than that under the truth-telling condition. The results also showed that facial movements around the eyes were more asymmetrical when people are telling lies. All the results suggested that facial clues can be used to detect deception, and fear could be a cue for distinguishing liars from truth-tellers.

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.

Clusters of nonverbal behavior differentiate truths and lies about future malicious intent in checkpoint screening interviews

Recent research has shown that nonverbal behavior (NVB) assessed across multiple channels can differentiate truthtellers from liars. No study, however, has examined whether or not multiple NVBs can differentiate truths from lies about intent regarding future malicious behavior, or across multiple cultural/ethnic groups. We address this gap by examining truths and lies about intent to commit a malicious act in the future in brief, checkpoint-type security screening interviews. Data from four NVB channels producing twenty-one observable NVBs were coded and analyzed using different analytic strategies. Clusters of NVB were found to differentiate truthtellers from liars at statistically significant levels, and substantially beyond the ability of human observers. The findings showed that clusters of NVB can differentiate truthtellers from liars even in brief, checkpoint-type interviews.

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.