scholarly journals Surgical Masks Impair People's Ability To Accurately Classify Emotional Expressions, Except For Anger

2021 ◽  
Author(s):  
Alice Mado Proverbio ◽  
Alice Cerri
Keyword(s):  
Emotion Recognition ◽  
Repeated Measures ◽  
Startle Reaction ◽  
Emotional Expressions ◽  
Emotional States ◽  
Negative Effects ◽  
Natural Conditions ◽  
Social Processing ◽  
Surgical Masks ◽  
Scale Scores

Abstract Background Recently, the need to continuously wear surgical masks in everyday life has drawn the attention of neuroscientists and psychologists to the negative effects of face covering on social processing. A very recent but not very homogeneous literature has highlighted large costs in the ability to recognize emotions. Methods Here it was investigated how surgical masks covering impaired the recognition of facial mimicry in a large group of 220 undergraduate Italian students. Sex differences in emotion recognition were also observed in 2 subgroups of 94 age-matched participants. Subjects were presented with 112 pictures displaying the faces of 8 actors (4 women and 4 men) wearing or not wearing real facemasks, and expressing 7 emotional states (neutrality, surprise, happiness, sadness, disgust, anger and fear). The task consisted in categorizing the emotion while indicating the recognizability degree with a 3-point Likert scale. Scores underwent repeated measures ANOVAs. Results Overall, face masking reduced emotion recognition by 31%. All emotions were affected by mask covering except for anger. Face covering was most detrimental for sadness and disgust, both relying on mouth and nose inspection. Women showed a better performance for subtle expressions such as surprise and sadness, both in masked and natural conditions, and men for fear recognition (in natural but especially masked conditions). Conclusions Anger display was unaffected by masking, since corrugated forehead and frowning eyebrows were clearly exposed. Unlike digitally created masks, real masks were able to show inhalation-related sucking associated with startle reaction (in surprise, and especially fear expressions), thus providing further cues for emotion recognition.

scholarly journals Interoceptive sensibility predicts the ability to infer others’ emotional states

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258089
Author(s):  
Amelie M. Hübner ◽  
Ima Trempler ◽  
Corinna Gietmann ◽  
Ricarda I. Schubotz
Keyword(s):  
Emotion Recognition ◽  
Reaction Times ◽  
Emotional Expressions ◽  
Emotional States ◽  
Differential Modulation ◽  
Skin Conductance Responses ◽  
Weak Evidence ◽  
Fearful Expression ◽  
Bodily Sensations ◽  
Response Task

Emotional sensations and inferring another’s emotional states have been suggested to depend on predictive models of the causes of bodily sensations, so-called interoceptive inferences. In this framework, higher sensibility for interoceptive changes (IS) reflects higher precision of interoceptive signals. The present study examined the link between IS and emotion recognition, testing whether individuals with higher IS recognize others’ emotions more easily and are more sensitive to learn from biased probabilities of emotional expressions. We recorded skin conductance responses (SCRs) from forty-six healthy volunteers performing a speeded-response task, which required them to indicate whether a neutral facial expression dynamically turned into a happy or fearful expression. Moreover, varying probabilities of emotional expressions by their block-wise base rate aimed to generate a bias for the more frequently encountered emotion. As a result, we found that individuals with higher IS showed lower thresholds for emotion recognition, reflected in decreased reaction times for emotional expressions especially of high intensity. Moreover, individuals with increased IS benefited more from a biased probability of an emotion, reflected in decreased reaction times for expected emotions. Lastly, weak evidence supporting a differential modulation of SCR by IS as a function of varying probabilities was found. Our results indicate that higher interoceptive sensibility facilitates the recognition of emotional changes and is accompanied by a more precise adaptation to emotion probabilities.

scholarly journals Human Emotional State Assessment Based on a Video Portrayal

2020 ◽  
Vol 13 (4) ◽  
pp. 4-24 ◽  
Author(s):  
V.A. Barabanschikov ◽  
E.V. Suvorova
Keyword(s):  
Emotion Recognition ◽  
Recognition Test ◽  
Emotional State ◽  
State Assessment ◽  
Identification Accuracy ◽  
Emotional Expressions ◽  
Emotional States ◽  
Wide Range ◽  
Similarities And Differences ◽  
Valence And Arousal

The article is devoted to the results of approbation of the Geneva Emotion Recognition Test (GERT), a Swiss method for assessing dynamic emotional states, on Russian sample. Identification accuracy and the categorical fields’ structure of emotional expressions of a “living” face are analysed. Similarities and differences in the perception of affective groups of dynamic emotions in the Russian and Swiss samples are considered. A number of patterns of recognition of multi-modal expressions with changes in valence and arousal of emotions are described. Differences in the perception of dynamics and statics of emotional expressions are revealed. GERT method confirmed it’s high potential for solving a wide range of academic and applied problems.

scholarly journals Dogs can infer implicit information from human emotional expressions

2021 ◽  
Author(s):  
Natalia Albuquerque ◽  
Daniel S. Mills ◽  
Kun Guo ◽  
Anna Wilkinson ◽  
Briseida Resende
Keyword(s):  
Emotional State ◽  
Food Resource ◽  
Emotional Expressions ◽  
Emotional States ◽  
Emotional Information ◽  
Implicit Information ◽  
Emotional Display ◽  
Affective Information ◽  
Social Decision Making ◽  
Current Behaviour

AbstractThe ability to infer emotional states and their wider consequences requires the establishment of relationships between the emotional display and subsequent actions. These abilities, together with the use of emotional information from others in social decision making, are cognitively demanding and require inferential skills that extend beyond the immediate perception of the current behaviour of another individual. They may include predictions of the significance of the emotional states being expressed. These abilities were previously believed to be exclusive to primates. In this study, we presented adult domestic dogs with a social interaction between two unfamiliar people, which could be positive, negative or neutral. After passively witnessing the actors engaging silently with each other and with the environment, dogs were given the opportunity to approach a food resource that varied in accessibility. We found that the available emotional information was more relevant than the motivation of the actors (i.e. giving something or receiving something) in predicting the dogs’ responses. Thus, dogs were able to access implicit information from the actors’ emotional states and appropriately use the affective information to make context-dependent decisions. The findings demonstrate that a non-human animal can actively acquire information from emotional expressions, infer some form of emotional state and use this functionally to make decisions.

scholarly journals Recognition of Emotion from Facial Expressions with Direct or Averted Eye Gaze and Varying Expression Intensities in Children with Autism Disorder and Typically Developing Children

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Dina Tell ◽  
Denise Davidson ◽  
Linda A. Camras
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Eye Gaze ◽  
Children With Autism ◽  
Gaze Direction ◽  
Emotional Expressions ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Expression Intensity ◽  
Autism Disorder

Eye gaze direction and expression intensity effects on emotion recognition in children with autism disorder and typically developing children were investigated. Children with autism disorder and typically developing children identified happy and angry expressions equally well. Children with autism disorder, however, were less accurate in identifying fear expressions across intensities and eye gaze directions. Children with autism disorder rated expressions with direct eyes, and 50% expressions, as more intense than typically developing children. A trend was also found for sad expressions, as children with autism disorder were less accurate in recognizing sadness at 100% intensity with direct eyes than typically developing children. Although the present research showed that children with autism disorder are sensitive to eye gaze direction, impairments in the recognition of fear, and possibly sadness, exist. Furthermore, children with autism disorder and typically developing children perceive the intensity of emotional expressions differently.

Fractal dimension pattern-based multiresolution analysis for rough estimator of speaker-dependent audio emotion recognition

2017 ◽  
Vol 15 (05) ◽  
pp. 1750042 ◽  
Author(s):  
Miao Cheng ◽  
Ah Chung Tsoi
Keyword(s):  
Fractal Dimension ◽  
Emotion Recognition ◽  
Multiresolution Analysis ◽  
Real Life ◽  
Emotional States ◽  
Acoustic Feature ◽  
Comparative Performance ◽  
Audio Analysis ◽  
The Arts ◽  
The Given

As a general means of expression, audio analysis and recognition have attracted much attention for its wide applications in real-life world. Audio emotion recognition (AER) attempts to understand the emotional states of human with the given utterance signals, and has been studied abroad for its further development on friendly human–machine interfaces. Though there have been several the-state-of-the-arts auditory methods devised to audio recognition, most of them focus on discriminative usage of acoustic features, while feedback efficiency of recognition demands is ignored. This makes possible application of AER, and rapid learning of emotion patterns is desired. In order to make predication of audio emotion possible, the speaker-dependent patterns of audio emotions are learned with multiresolution analysis, and fractal dimension (FD) features are calculated for acoustic feature extraction. Furthermore, it is able to efficiently learn the intrinsic characteristics of auditory emotions, while the utterance features are learned from FDs of each sub-band. Experimental results show the proposed method is able to provide comparative performance for AER.

scholarly journals Speech-based human emotion recognition

2021 ◽  
Author(s):  
Talieh Seyed Tabtabae
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Speaker Identification ◽  
Recognition System ◽  
Research Area ◽  
Speech Signals ◽  
Spectral Features ◽  
Emotional States ◽  
Human Communication ◽  
Set Up

Automatic Emotion Recognition (AER) is an emerging research area in the Human-Computer Interaction (HCI) field. As Computers are becoming more and more popular every day, the study of interaction between humans (users) and computers is catching more attention. In order to have a more natural and friendly interface between humans and computers, it would be beneficial to give computers the ability to recognize situations the same way a human does. Equipped with an emotion recognition system, computers will be able to recognize their users' emotional state and show the appropriate reaction to that. In today's HCI systems, machines can recognize the speaker and also content of the speech, using speech recognition and speaker identification techniques. If machines are equipped with emotion recognition techniques, they can also know "how it is said" to react more appropriately, and make the interaction more natural. One of the most important human communication channels is the auditory channel which carries speech and vocal intonation. In fact people can perceive each other's emotional state by the way they talk. Therefore in this work the speech signals are analyzed in order to set up an automatic system which recognizes the human emotional state. Six discrete emotional states have been considered and categorized in this research: anger, happiness, fear, surprise, sadness, and disgust. A set of novel spectral features are proposed in this contribution. Two approaches are applied and the results are compared. In the first approach, all the acoustic features are extracted from consequent frames along the speech signals. The statistical values of features are considered to constitute the features vectors. Suport Vector Machine (SVM), which is a relatively new approach in the field of machine learning is used to classify the emotional states. In the second approach, spectral features are extracted from non-overlapping logarithmically-spaced frequency sub-bands. In order to make use of all the extracted information, sequence discriminant SVMs are adopted. The empirical results show that the employed techniques are very promising.

scholarly journals Recognition Characteristics of Facial and Bodily Expressions: Evidence From ERPs

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoxiao Li
Keyword(s):  
Emotion Recognition ◽  
Facial Expressions ◽  
Cognitive Processing ◽  
Natural Environment ◽  
Early Stage ◽  
Event Related Potential ◽  
Emotional Expressions ◽  
Emotional Information ◽  
Prolonged Latency ◽  
Electrophysiological Mechanism

In the natural environment, facial and bodily expressions influence each other. Previous research has shown that bodily expressions significantly influence the perception of facial expressions. However, little is known about the cognitive processing of facial and bodily emotional expressions and its temporal characteristics. Therefore, this study presented facial and bodily expressions, both separately and together, to examine the electrophysiological mechanism of emotional recognition using event-related potential (ERP). Participants assessed the emotions of facial and bodily expressions that varied by valence (positive/negative) and consistency (matching/non-matching emotions). The results showed that bodily expressions induced a more positive P1 component and a shortened latency, whereas facial expressions triggered a more negative N170 and prolonged latency. Among N2 and P3, N2 was more sensitive to inconsistent emotional information and P3 was more sensitive to consistent emotional information. The cognitive processing of facial and bodily expressions had distinctive integrating features, with the interaction occurring in the early stage (N170). The results of the study highlight the importance of facial and bodily expressions in the cognitive processing of emotion recognition.

scholarly journals Investigating emotion recognition in the first year of life using eye-tracking Methodology

2021 ◽  
Author(s):  
Shira C. Segal
Keyword(s):  
Individual Differences ◽  
Eye Tracking ◽  
Emotion Recognition ◽  
Emotional Expressions ◽  
First Year ◽  
Facial Features ◽  
Fearful Faces ◽  
Facial Expressions Of Emotion ◽  
First Year Of Life ◽  
Key Words Emotion

The ability to recognize facial expressions of emotion is a critical part of human social interaction. Infants improve in this ability across the first year of life, but the mechanisms driving these changes and the origins of individual differences in this ability are largely unknown. This thesis used eye tracking to characterize infant scanning patterns of expressions. In study 1 (n = 40), I replicated the preference for fearful faces, and found that infants either allocated more attention to the eyes or the mouth across both happy and fearful expressions. In study 2 (n = 40), I found that infants differentially scanned the critical facial features of dynamic expressions. In study 3 (n = 38), I found that maternal depressive symptoms and positive and negative affect were related to individual differences in infants’ scanning of emotional expressions. Implications for our understanding of the development of emotion recognition are discussed. Key Words: emotion recognition, infancy eye tracking, socioemotional development

scholarly journals A Machine Learning Approach to EEG-based Prediction of Human Affective States Using Recursive Feature Elimination Method

2021 ◽  
Vol 335 ◽  
pp. 04001
Author(s):  
Didar Dadebayev ◽  
Goh Wei Wei ◽  
Tan Ee Xion
Keyword(s):  
Feature Selection ◽  
Fractal Dimension ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Feature Selection Method ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Emotional States ◽  
Affective States ◽  
Emotion Classification

Emotion recognition, as a branch of affective computing, has attracted great attention in the last decades as it can enable more natural brain-computer interface systems. Electroencephalography (EEG) has proven to be an effective modality for emotion recognition, with which user affective states can be tracked and recorded, especially for primitive emotional events such as arousal and valence. Although brain signals have been shown to correlate with emotional states, the effectiveness of proposed models is somewhat limited. The challenge is improving accuracy, while appropriate extraction of valuable features might be a key to success. This study proposes a framework based on incorporating fractal dimension features and recursive feature elimination approach to enhance the accuracy of EEG-based emotion recognition. The fractal dimension and spectrum-based features to be extracted and used for more accurate emotional state recognition. Recursive Feature Elimination will be used as a feature selection method, whereas the classification of emotions will be performed by the Support Vector Machine (SVM) algorithm. The proposed framework will be tested with a widely used public database, and results are expected to demonstrate higher accuracy and robustness compared to other studies. The contributions of this study are primarily about the improvement of the EEG-based emotion classification accuracy. There is a potential restriction of how generic the results can be as different EEG dataset might yield different results for the same framework. Therefore, experimenting with different EEG dataset and testing alternative feature selection schemes can be very interesting for future work.

Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition

2016 ◽  
Vol 7 (1) ◽  
pp. 58-68 ◽  
Author(s):  
Imen Trabelsi ◽  
Med Salim Bouhlel
Keyword(s):  
Emotion Recognition ◽  
Linear Prediction ◽  
Recognition Rate ◽  
Gaussian Mixture ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Emotional States ◽  
Wide Range ◽  
Leibler Divergence ◽  
Perceptual Linear Prediction

Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

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