Darwin the detective: Observable facial muscle contractions reveal emotional high-stakes lies

Interpersonal Consequences of Deceptive Expressions of Sadness

Emotional expressions evoke predictable responses from observers; displays of sadness are commonly met with sympathy and help from others. Accordingly, people may be motivated to feign emotions to elicit a desired response. In the absence of suspicion, we predicted that emotional and behavioral responses to genuine (vs. deceptive) expressers would be guided by empirically valid cues of sadness authenticity. Consistent with this hypothesis, untrained observers (total N = 1,300) reported less sympathy and offered less help to deceptive (vs. genuine) expressers of sadness. This effect was replicated using both posed, low-stakes, laboratory-created stimuli, and spontaneous, real, high-stakes emotional appeals to the public. Furthermore, lens models suggest that sympathy reactions were guided by difficult-to-fake facial actions associated with sadness. Results suggest that naive observers use empirically valid cues to deception to coordinate social interactions, providing novel evidence that people are sensitive to subtle cues to deception.

Responses of functional brain networks in micro-expressions: An EEG study

Micro-expressions (MEs) can reflect an individual’s subjective emotions and true mental state, and they are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, one of the major challenges of working with MEs is that their neural mechanism is not entirely understood. To the best of our knowledge, the present study is the first to use electroencephalography (EEG) to investigate the reorganizations of functional brain networks involved in MEs. We aimed to reveal the underlying neural mechanisms that can provide electrophysiological indicators for ME recognition. A real-time supervision and emotional expression suppression experimental paradigm was designed to collect video and EEG data of MEs and no expressions (NEs) of 70 participants expressing positive emotions. Based on the graph theory, we analyzed the efficiency of functional brain network at the scalp level on both macro and micro scales. The results revealed that in the presence of MEs compared with NEs, the participants exhibited higher global efficiency and nodal efficiency in the frontal, occipital, and temporal regions. Additionally, using the random forest algorithm to select a subset of functional connectivity features as input, the support vector machine classifier achieved a classification accuracy for MEs and NEs of 0.81, with an area under the curve of 0.85. This finding demonstrates the possibility of using EEG to recognize MEs, with a wide range of application scenarios, such as persons wearing face masks or patients with expression disorders.

Differences in brain activations between micro- and macro-expressions based on electroencephalography

Micro-expressions can reflect an individual's subjective emotions and true mental state and are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, the current approach based on image and expert assessment-based micro-expression recognition technology has limitations such as limited application scenarios and time consumption. Therefore, to overcome these limitations, this study is the first to explore the brain mechanisms of micro-expressions and their differences from macro-expressions from a neuroscientific perspective. This can be a foundation for micro-expression recognition based on EEG signals. We designed a real-time supervision and emotional expression suppression (SEES) experimental paradigm to synchronously collect facial expressions and electroencephalograms. Electroencephalogram signals were analyzed at the scalp and source levels to determine the temporal and spatial neural patterns of micro- and macro-expressions. We found that micro-expressions were more strongly activated in the premotor cortex, supplementary motor cortex, and middle frontal gyrus in frontal regions under positive emotions than macro-expressions. Under negative emotions, micro-expressions were more weakly activated in the somatosensory cortex and corneal gyrus regions than macro-expressions. The activation of the right temporoparietal junction (rTPJ) was stronger in micro-expressions under positive than negative emotions. The reason for this difference is that the pathways of facial control are different; the production of micro-expressions under positive emotion is dependent on the control of the face, while micro-expressions under negative emotions are more dependent on the intensity of the emotion.

Novel Method for Three-Dimensional Facial Expression Recognition Using Self-Normalizing Neural Networks and Mobile Devices

Introduction To date, most ways to perform facial expression recognition rely on two-dimensional images, advanced approaches with three-dimensional data exist. These however demand stationary apparatuses and thus lack portability and possibilities to scale deployment. As human emotions, intent and even diseases may condense in distinct facial expressions or changes therein, the need for a portable yet capable solution is signified. Due to the superior informative value of three-dimensional data on facial morphology and because certain syndromes find expression in specific facial dysmorphisms, a solution should allow portable acquisition of true three-dimensional facial scans in real time. In this study we present a novel solution for the three-dimensional acquisition of facial geometry data and the recognition of facial expressions from it. The new technology presented here only requires the use of a smartphone or tablet with an integrated TrueDepth camera and enables real-time acquisition of the geometry and its categorization into distinct facial expressions. Material and Methods Our approach consisted of two parts: First, training data was acquired by asking a collective of 226 medical students to adopt defined facial expressions while their current facial morphology was captured by our specially developed app running on iPads, placed in front of the students. In total, the list of the facial expressions to be shown by the participants consisted of "disappointed", "stressed", "happy", "sad" and "surprised". Second, the data were used to train a self-normalizing neural network. A set of all factors describing the current facial expression at a time is referred to as "snapshot". Results In total, over half a million snapshots were recorded in the study. Ultimately, the network achieved an overall accuracy of 80.54% after 400 epochs of training. In test, an overall accuracy of 81.15% was determined. Recall values differed by the category of a snapshot and ranged from 74.79% for "stressed" to 87.61% for "happy". Precision showed similar results, whereas "sad" achieved the lowest value at 77.48% and "surprised" the highest at 86.87%. Conclusions With the present work it can be demonstrated that respectable results can be achieved even when using data sets with some challenges. Through various measures, already incorporated into an optimized version of our app, it is to be expected that the training results can be significantly improved and made more precise in the future. Currently a follow-up study with the new version of our app that encompasses the suggested alterations and adaptions, is being conducted. We aim to build a large and open database of facial scans not only for facial expression recognition but to perform disease recognition and to monitor diseases' treatment progresses.

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.

The cultural evolution of shamanism

Shamans, including medicine men, mediums, and the prophets of religious movements, recur across human societies. Shamanism also existed among nearly all documented hunter-gatherers, likely characterized the religious lives of many ancestral humans, and is often proposed by anthropologists to be the "first profession," representing the first institutionalized division of labor beyond age and sex. In this article, I propose a cultural evolutionary theory to explain why shamanism consistently develops and, in particular, (1) why shamanic traditions exhibit recurrent features around the world; (2) why shamanism professionalizes early, often in the absence of other specialization; and (3) how shifting social conditions affect the form or existence of shamanism. According to this theory, shamanism is a set of traditions developed through cultural evolution that adapts to people's intuitions to convince observers that a practitioner can influence otherwise unpredictable, significant events. The shaman does this by ostensibly transforming during initiation and trance, violating folk intuitions of humanness to assure group members that he or she can interact with the invisible forces that control uncertain outcomes. Entry requirements for becoming a shaman persist because the practitioner's credibility depends on his or her "transforming." This contrasts with dealing with problems that have identifiable solutions (such as building a canoe), in which credibility hinges on showing results and outsiders can invade the jurisdiction by producing the outcome. Shamanism is an ancient human institution that recurs because of the capacity of cultural evolution to produce practices adapted to innate psychological tendencies.

Comparing a Perceptual and an Automated Vision-Based Method for Lie Detection in Younger Children

The present study investigates how easily it can be detected whether a child is being truthful or not in a game situation, and it explores the cue validity of bodily movements for such type of classification. To achieve this, we introduce an innovative methodology – the combination of perception studies (in which eye-tracking technology is being used) and automated movement analysis. Film fragments from truthful and deceptive children were shown to human judges who were given the task to decide whether the recorded child was being truthful or not. Results reveal that judges are able to accurately distinguish truthful clips from lying clips in both perception studies. Even though the automated movement analysis for overall and specific body regions did not yield significant results between the experimental conditions, we did find a positive correlation between the amount of movement in a child and the perception of lies, i.e., the more movement the children exhibited during a clip, the higher the chance that the clip was perceived as a lie. The eye-tracking study revealed that, even when there is movement happening in different body regions, judges tend to focus their attention mainly on the face region. This is the first study that compares a perceptual and an automated method for the detection of deceptive behavior in children whose data have been elicited through an ecologically valid paradigm.

Attending to emotions is sharing of emotions - A multidisciplinary perspective to social attention and emotional sharing. Comment on Zahavi and Rochat (2015)

Attending to bodily expression of emotions plays an important role in the human social world. It provides subjects with valuable information, constructs opportunities to act, and importantly, as Daniel Stern pointed out, it is involved in the constitution of the direct experience of others. Whether mutual or one-sided, these direct experiences, in which the subject can share the perspectives and attitudes of other subjects, always comprise one person's bodily expression of emotions that is available to another person. In this article I suggest that attending to other subjects' expressed emotions involves a special (social) mode of attention and emotional sharing. This suggestion challenges Dan Zahavi's view that confines the sharing of emotions solely to reciprocal experiences.

Hiding true emotions: micro-expressions in eyes retrospectively concealed by mouth movements

When we encounter someone we dislike, we may momentarily display a reflexive disgust expression, only to follow-up with a forced smile and greeting. Our daily lives are replete with a mixture of true and fake expressions. Nevertheless, are these fake expressions really effective at hiding our true emotions? Here we show that brief emotional changes in the eyes (micro-expressions, thought to reflect true emotions) can be successfully concealed by follow-up mouth movements (e.g. a smile). In the same manner as backward masking, mouth movements of a face inhibited conscious detection of all types of micro-expressions in that face, even when viewers paid full attention to the eye region. This masking works only in a backward direction, however, because no disrupting effect was observed when the mouth change preceded the eye change. These results provide scientific evidence for everyday behaviours like smiling to dissemble, and further clarify a major reason for the difficulty we face in discriminating genuine from fake emotional expressions.

How private is private information? The ability to spot deception in an economic game

We provide experimental evidence on the ability to detect deceit in a buyer-seller game with asymmetric information. Sellers have private information about the value of a good and sometimes have incentives to mislead buyers. We examine if buyers can spot deception in face-to-face encounters. We vary whether buyers can interrogate the seller and the contextual richness. The buyers' prediction accuracy is above chance, and is substantial for confident buyers. There is no evidence that the option to interrogate is important and only weak support that contextual richness matters. These results show that the information asymmetry is partly eliminated by people's ability to spot deception.

Lying in neuropsychology

The issue of lying occurs in neuropsychology especially when examinations are conducted in a forensic context. When a subject intentionally either presents non-existent deficits or exaggerates their severity to obtain financial or material compensation, this behaviour is termed malingering. Malingering is discussed in the general framework of lying in psychology, and the different procedures used by neuropsychologists to evidence a lack of collaboration at examination are briefly presented and discussed. When a lack of collaboration is observed, specific emphasis is placed on the difficulty in unambiguously establishing that this results from the patient's voluntary decision.