scholarly journals Responsibility and the sense of agency enhance empathy for pain

2015 ◽  
Vol 282 (1799) ◽  
pp. 20142288 ◽  
Author(s):  
Evelyne Lepron ◽  
Michaël Causse ◽  
Chlöé Farrer
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Sense Of Agency ◽  
Intensity Estimation ◽  
Facial Electromyography ◽  
Empathic Response ◽  
Video Clips ◽  
Moral Conduct ◽  
Empathy For Pain ◽  
Observer Ratings

Being held responsible for our actions strongly determines our moral judgements and decisions. This study examined whether responsibility also influences our affective reaction to others' emotions. We conducted two experiments in order to assess the effect of responsibility and of a sense of agency (the conscious feeling of controlling an action) on the empathic response to pain. In both experiments, participants were presented with video clips showing an actor's facial expression of pain of varying intensity. The empathic response was assessed with behavioural (pain intensity estimation from facial expressions and unpleasantness for the observer ratings) and electrophysiological measures (facial electromyography). Experiment 1 showed enhanced empathic response (increased unpleasantness for the observer and facial electromyography responses) as participants' degree of responsibility for the actor's pain increased. This effect was mainly accounted for by the decisional component of responsibility (compared with the execution component). In addition, experiment 2 found that participants' unpleasantness rating also increased when they had a sense of agency over the pain, while controlling for decision and execution processes. The findings suggest that increased empathy induced by responsibility and a sense of agency may play a role in regulating our moral conduct.

Suppression of Facial Mimicry of Negative Facial Expressions in an Incongruent Context

2018 ◽  
Vol 32 (4) ◽  
pp. 160-171 ◽  
Author(s):  
Léonor Philip ◽  
Jean-Claude Martin ◽  
Céline Clavel
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Affective Priming ◽  
Emotional Expressions ◽  
Facial Electromyography ◽  
Emotional Cues ◽  
Priming Task ◽  
Priming Word ◽  
Affective Priming Task ◽  
Neutral Expression

Abstract. People react with Rapid Facial Reactions (RFRs) when presented with human facial emotional expressions. Recent studies show that RFRs are not always congruent with emotional cues. The processes underlying RFRs are still being debated. In our study described herein, we manipulate the context of perception and its influence on RFRs. We use a subliminal affective priming task with emotional labels. Facial electromyography (EMG) (frontalis, corrugator, zygomaticus, and depressor) was recorded while participants observed static facial expressions (joy, fear, anger, sadness, and neutral expression) preceded/not preceded by a subliminal word (JOY, FEAR, ANGER, SADNESS, or NEUTRAL). For the negative facial expressions, when the priming word was congruent with the facial expression, participants displayed congruent RFRs (mimicry). When the priming word was incongruent, we observed a suppression of mimicry. Happiness was not affected by the priming word. RFRs thus appear to be modulated by the context and type of emotion that is presented via facial expressions.

scholarly journals Catching a Liar Through Facial Expression of Fear

2021 ◽  
Vol 12 ◽  
Author(s):  
Xunbing Shen ◽  
Gaojie Fan ◽  
Caoyuan Niu ◽  
Zhencai Chen
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Time Window ◽  
Total Duration ◽  
Truth Telling ◽  
High Stakes ◽  
Game Show ◽  
Facial Movements ◽  
Video Clips ◽  
The Moment

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.

Finding Useful Features for Facial Expression Recognition and Intensity Estimation by Neural Network

2020 ◽  
Vol 8 (2) ◽  
pp. 68-84
Author(s):  
Naoki Imamura ◽  
Hiroki Nomiya ◽  
Teruhisa Hochin
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Facial Features ◽  
Expression Recognition ◽  
Intensity Estimation ◽  
Expression Intensity ◽  
The Neural Network

Facial expression intensity has been proposed to digitize the degree of facial expressions in order to retrieve impressive scenes from lifelog videos. The intensity is calculated based on the correlation of facial features compared to each facial expression. However, the correlation is not determined objectively. It should be determined statistically based on the contribution score of the facial features necessary for expression recognition. Therefore, the proposed method recognizes facial expressions by using a neural network and calculates the contribution score of input toward the output. First, the authors improve some facial features. After that, they verify the score correctly by comparing the accuracy transitions depending on reducing useful and useless features and process the score statistically. As a result, they extract useful facial features from the neural network.

scholarly journals EMPLOYING A NOVEL PARADIGM AND SETS OF STIMULI TO EXAMINE EMOTIONAL INTENSITY RECOGNITION AND ITS MODULATION BY EMPATHY FOR PAIN

2019 ◽  
Author(s):  
Navot Naor ◽  
Simone Shamay-Tsoory ◽  
Hadas Okon-Singer
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Cultural Factors ◽  
Empathic Accuracy ◽  
Emotional Intensity ◽  
Emotional Facial Expressions ◽  
Still Images ◽  
Language And Culture ◽  
Empathy For Pain

Over the last two decades, research has devoted increasing attention to the examination of empathy. Yet research examining empathic accuracy, defined as how well we judge the emotional intensity felt by another, has grown more modestly. This asymmetry may be due to the complexity of paradigms used to study empathic accuracy, as well as to the fact that the stimuli used so far are dependent upon linguistic and cultural factors. To circumvent these issues, here we present a novel paradigm that examines the ability to assess empathic accuracy in a simple and implicit manner by using stimuli that are not dependent on language and culture. To this end, we devised two sets of stimuli: (1) a painful scenario set consisting of empathy-evoking still images; and (2) a facial expression set comprising morphed intensities of emotional facial expressions. Together, these sets can be used to study the effect of the empathic experience on an individual's ability to make accurate judgments of others' emotional facial intensity, a sub-process of empathic accuracy. We contend that adopting these sets may facilitate the replicability of findings across countries and populations, which in turn will increase the number of investigations of empathic accuracy.

scholarly journals Catching a liar through facial expression of fear

2021 ◽  
Author(s):  
Xunbing Shen ◽  
Gaojie Fan ◽  
Caoyuan Niu ◽  
Zhencai Chen
Keyword(s):  
Machine Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Deception Detection ◽  
Truth Telling ◽  
High Stake ◽  
Game Show ◽  
Facial Movements ◽  
Video Clips ◽  
The Moment

AbstractThe leakage theory in the field of deception detection predicted that liars could not repress the leaked felt emotions (e.g., the fear or delight); and people who were lying would feel fear (to be discovered), especially under the high-stake situations. Therefore, we assumed that the aim of revealing deceits could be reached via analyzing the facial expression of fear. 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 of fear and face landmarks) and WEKA (for classifying the video clips in which the players was lying or telling the truth). The results showed that some algorithms could achieve an accuracy of greater than 80% merely using AUs of fear. Besides, the total durations of AU 20 of fear were found to be shorter under the lying condition than under the truth-telling condition. Further analysis found the cause why durations of fear were shorter was that the duration from peak to offset of AU20 under the lying condition was less than that under the truth-telling condition. The results also showed that the facial movements around the eyes were more asymmetrical while people telling lies. All the results suggested that there do exist facial clues to deception, and fear could be a cue for distinguishing liars from truth-tellers.

The face value of feedback: Facial behavior is shaped by goals and punishments during interaction with dynamic faces

2020 ◽  
Author(s):  
Jonathan Yi ◽  
Philip Pärnamets ◽  
Andreas Olsson
Keyword(s):  
Decision Making ◽  
Facial Expression ◽  
Facial Expressions ◽  
Large Body ◽  
Mechanistic Explanation ◽  
Experimental Manipulation ◽  
Corrugator Supercilii ◽  
Dynamic Faces ◽  
Facial Behavior ◽  
Reinforcement Learning Models

Responding appropriately to others’ facial expressions is key to successful social functioning. Despite the large body of work on face perception and spontaneous responses to static faces, little is known about responses to faces in dynamic, naturalistic situations, and no study has investigated how goal directed responses to faces are influenced by learning during dyadic interactions. To experimentally model such situations, we developed a novel method based on online integration of electromyography (EMG) signals from the participants’ face (corrugator supercilii and zygomaticus major) during facial expression exchange with dynamic faces displaying happy and angry facial expressions. Fifty-eight participants learned by trial-and-error to avoid receiving aversive stimulation by either reciprocate (congruently) or respond opposite (incongruently) to the expression of the target face. Our results validated our method, showing that participants learned to optimize their facial behavior, and replicated earlier findings of faster and more accurate responses in congruent vs. incongruent conditions. Moreover, participants performed better on trials when confronted with smiling, as compared to frowning, faces, suggesting it might be easier to adapt facial responses to positively associated expressions. Finally, we applied drift diffusion and reinforcement learning models to provide a mechanistic explanation for our findings which helped clarifying the underlying decision-making processes of our experimental manipulation. Our results introduce a new method to study learning and decision-making in facial expression exchange, in which there is a need to gradually adapt facial expression selection to both social and non-social reinforcements.

Capacity-Free Automatic Processing of Facial Expressions of Emotion

2020 ◽  
Author(s):  
Joshua W Maxwell ◽  
Eric Ruthruff ◽  
michael joseph
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Face Processing ◽  
Emotional Expressions ◽  
Facial Expression Of Emotion ◽  
Expression Of Emotion ◽  
Facial Expressions Of Emotion ◽  
Correspondence Effect ◽  
Experimental Logic ◽  
Face Task

Are facial expressions of emotion processed automatically? Some authors have not found this to be the case (Tomasik et al., 2009). Here we revisited the question with a novel experimental logic – the backward correspondence effect (BCE). In three dual-task studies, participants first categorized a sound (Task 1) and then indicated the location of a target face (Task 2). In Experiment 1, Task 2 required participants to search for one facial expression of emotion (angry or happy). We observed positive BCEs, indicating that facial expressions of emotion bypassed the central attentional bottleneck and thus were processed in a capacity-free, automatic manner. In Experiment 2, we replicated this effect but found that morphed emotional expressions (which were used by Tomasik) were not processed automatically. In Experiment 3, we observed similar BCEs for another type of face processing previously shown to be capacity-free – identification of familiar faces (Jung et al., 2013). We conclude that facial expressions of emotion are identified automatically when sufficiently unambiguous.

The face of future: Face expressions during future thinking

2021 ◽  
pp. 174702182199299
Author(s):  
Mohamad El Haj ◽  
Emin Altintas ◽  
Ahmed A Moustafa ◽  
Abdel Halim Boudoukha
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Emotional Experience ◽  
Future Scenarios ◽  
Future Thinking ◽  
Analysis Software ◽  
Emotional Facial Expressions ◽  
Facial Analysis ◽  
The Face ◽  
Face Expressions

Future thinking, which is the ability to project oneself forward in time to pre-experience an event, is intimately associated with emotions. We investigated whether emotional future thinking can activate emotional facial expressions. We invited 43 participants to imagine future scenarios, cued by the words “happy,” “sad,” and “city.” Future thinking was video recorded and analysed with a facial analysis software to classify whether facial expressions (i.e., happy, sad, angry, surprised, scared, disgusted, and neutral facial expression) of participants were neutral or emotional. Analysis demonstrated higher levels of happy facial expressions during future thinking cued by the word “happy” than “sad” or “city.” In contrast, higher levels of sad facial expressions were observed during future thinking cued by the word “sad” than “happy” or “city.” Higher levels of neutral facial expressions were observed during future thinking cued by the word “city” than “happy” or “sad.” In the three conditions, the neutral facial expressions were high compared with happy and sad facial expressions. Together, emotional future thinking, at least for future scenarios cued by “happy” and “sad,” seems to trigger the corresponding facial expression. Our study provides an original physiological window into the subjective emotional experience during future thinking.

scholarly journals Natural human postural oscillations enhance the empathic response to a facial pain expression in a virtual character

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thomas Treal ◽  
Philip L. Jackson ◽  
Jean Jeuvrey ◽  
Nicolas Vignais ◽  
Aurore Meugnot
Keyword(s):  
Facial Pain ◽  
Human Motion ◽  
Virtual Human ◽  
Motion Processing ◽  
Affective Neuroscience ◽  
Virtual Character ◽  
Empathic Response ◽  
Video Clips ◽  
Emotional Processes ◽  
Pain Expression

AbstractVirtual reality platforms producing interactive and highly realistic characters are being used more and more as a research tool in social and affective neuroscience to better capture both the dynamics of emotion communication and the unintentional and automatic nature of emotional processes. While idle motion (i.e., non-communicative movements) is commonly used to create behavioural realism, its use to enhance the perception of emotion expressed by a virtual character is critically lacking. This study examined the influence of naturalistic (i.e., based on human motion capture) idle motion on two aspects (the perception of other’s pain and affective reaction) of an empathic response towards pain expressed by a virtual character. In two experiments, 32 and 34 healthy young adults were presented video clips of a virtual character displaying a facial expression of pain while its body was either static (still condition) or animated with natural postural oscillations (idle condition). The participants in Experiment 1 rated the facial pain expression of the virtual human as more intense, and those in Experiment 2 reported being more touched by its pain expression in the idle condition compared to the still condition, indicating a greater empathic response towards the virtual human’s pain in the presence of natural postural oscillations. These findings are discussed in relation to the models of empathy and biological motion processing. Future investigations will help determine to what extent such naturalistic idle motion could be a key ingredient in enhancing the anthropomorphism of a virtual human and making its emotion appear more genuine.

scholarly journals Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1428
Author(s):  
Haopeng Wu ◽  
Zhiying Lu ◽  
Jianfeng Zhang ◽  
Xin Li ◽  
Mingyue Zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Video Data ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Movements ◽  
The Face ◽  
Deep Convolutional Network ◽  
Selection Of

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

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