Search results for: affect/emotion

Authors: Da Jiang, Helene H. Fung, Jennifer C. Lay, Maureen C. Ashe, Peter Graf, Christiane A. Hoppmann

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Being alone is often equated with loneliness. Yet, recent findings suggest that the objective state of being alone (i.e., solitude) can have both positive and negative connotations. The present research aimed to examine (1) affective experience in daily solitude; and (2) the association between everyday affect in solitude and well-being. We examined the distinct roles of culture and immigration in moderating these associations. Using up to 35 daily life assessments of momentary affect, solitude, and emotional well-being in two samples (Vancouver, Canada, and China), the study compared older adults who aged in place (local Caucasians in Vancouver Canada and local Hong Kong Chinese in Hong Kong, China) and older adults of different cultural heritages who immigrated to Canada (immigrated Caucasians and immigrated East Asians). We found that older adults of East Asian heritage experienced more positive and less negative affect when alone than did Caucasians. Reporting positive affect in solitude was more positively associated with well-being in older adults who had immigrated to Canada as compared to those who had aged in place. These findings speak to the unique effects of culture and immigration on the affective correlates of solitude and their associations with well-being in old age.

Keywords: solitude, emotion, age, immigration, culture

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Authors: Valentina Zhang

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While facial expression is a complex and individualized behavior, all facial emotion recognition (FER) systems known to us rely on a single facial representation and are trained on universal data. We conjecture that: (i) different facial representations can provide different, sometimes complementing views of emotions; (ii) when employed collectively in a discussion group setting, they enable more accurate emotion reading which is highly desirable in autism care and other applications context sensitive to errors. In this paper, we first study FER using pixel-based DL vs semantics-based DL in the context of deepfake videos. Our experiment indicates that while the semantics-trained model performs better with articulated facial feature changes, the pixel-trained model outperforms on subtle or rare facial expressions. Armed with these findings, we have constructed an adaptive FER system learning from both types of models for dyadic or small interacting groups and further leveraging the synthesized group emotions as the ground truth for individualized FER training. Using a collection of group conversation videos, we demonstrate that FER accuracy and personalization can benefit from such an approach.

Keywords: neurodivergence care, facial emotion recognition, deep learning, ground truth for supervised learning

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Authors: Jadisha Cornejo, Helio Pedrini

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Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

Keywords: emotion recognition, facial expression, occlusion, fiducial landmarks

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Authors: Esther Burkitt

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Background: Evidence is mounting that mixed emotions can be experienced simultaneously in different ways across the lifespan. Four types of patterns of simultaneously mixed emotions (sequential, prevalent, highly parallel, and inverse types) have been identified in middle childhood and adolescence. Moreover, the recognition of these experiences tends to develop firstly when children consider peers rather than the self. This evidence from children and adolescents is based on examining the presence of experiences specified in adulthood. The present study, therefore, applied an exhaustive coding scheme to investigate whether children experience types of previously unidentified simultaneous mixed emotional experiences. Methodology: One hundred and twenty children (60 girls) aged 7 years 1 month - 9 years 2 months (X=8 years 1 month; SD = 10 months) were recruited from mainstream schools across the UK. Two age groups were formed (youngest, n = 61, 7 years 1 month- 8 years 1 months: oldest, n = 59, 8 years 2 months – 9 years 2 months) and allocated to one of two conditions hearing vignettes describing happy and sad mixed emotion events in age and gender-matched protagonist or themselves. Results: Loglinear analyses identified new types of flexuous, vertical, and other experiences along with established sequential, prevalent, highly parallel, and inverse types of experience. Older children recognised more complex experiences other than the self-condition. Conclusion: Several additional types of simultaneously mixed emotions are recognised in middle childhood. The theoretical relevance of simultaneous mixed emotion processing in childhood is considered, and the potential utility of the findings in emotion assessments is discussed.

Keywords: emotion, childhood, self, other

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: emotion detection, TF-IDF, WEKA tool, classification algorithms

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Authors: Hossein Ilanloo, Sedigheh Ahmadi, Kianoosh Zahrakar

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The present study aims at investigating the effectiveness of group counseling of mindfulness-based cognitive therapy on cognitive emotion regulation in high school students. The research design was quasi-experimental and pre-test-post-test type and a two-month follow-up with a control group. The statistical population of the study consisted of all-male high school students in Takestan city in the Academic Year 2020-2021. The sample comprised 30 high school male students selected through the convenience sampling method and randomly assigned to experimental (n=15) and control (n=15) groups. The experimental group then received ten sessions of 90-minute group counseling of mindfulness-based cognitive therapy, and the control group did not receive any intervention. In order to collect data, the author used the Cognitive Emotion Regulation Questionnaire (CERQ). The researcher also used multivariate analysis of covariance, repeated measures, LSD post hoc test, and SPSS-26 software for data analysis.

Keywords: mindfulness-based cognitive therapy, cognitive emotion regulation, students, high schools

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Authors: Amina Ottozbeer, Nazanin Derakhshan

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Emotion regulation, a transdiagnostic process, is often impaired in individuals with high trait anxiety due to compromised executive functioning and attentional control. Recent research underscores the importance of studying individual differences and contextual factors in understanding the adaptability of emotion regulation processes, particularly in those with high trait anxiety. Prior studies have emphasized the role of self-efficacy in promoting positive cognitive flexibility outcomes and mitigating executive function impairments in highly anxious individuals. Accordingly, the objective of this study was to examine the moderating influence of attentional control, cognitive flexibility, and self-efficacy on the relationship between trait anxiety and emotion regulation. Using a correlational design, an online study was conducted with a sample of 82 participants (mean age: 22 years). Self-report questionnaires measured individual difference variables. The Classic Stroop Task assessed attentional control as an objective measure of cognitive flexibility . The findings revealed three significant interactions. Firstly, high cognitive flexibility and self-efficacy were linked to reduced expressive suppression in individuals with low trait anxiety. Secondly, elevations in cognitive flexibility and self-efficacy were associated with increased suppression in those with high trait anxiety. Thirdly, high trait anxiety was associated with reduced attentional control. The results suggest that typically adaptive processes can yield different outcomes in highly anxious populations, highlighting the need to explore additional variables that could alter the impact of cognitive flexibility and self-efficacy on emotion regulation in individuals with high anxiety. These findings have significant clinical implications, emphasizing the need to consider individual differences in emotion regulation and trait anxiety to inform more effective psychological treatments.

Keywords: attentional control, trait anxiety, emotional dysregulation, transdiagnostic, individual differences

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: John McDowall, Crysta Derham

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Broaden and build theory of emotion describes how experiencing positive emotions, such as happiness, broadens our ‘thought-action repertoire’ leading us to be more likely to go out and act on our positive emotions. This results in the building of new relationships, resources and skills, which we can draw on in times of need throughout life. In contrast, the experience of negative emotion is thought to narrow our ‘thought-action repertoire’, leading to specific actions to aid in survival. Three experiments aimed to explore the effect of briefly presented schematic faces (happy, sad, and neutral) on attentional scope using the flanker task. Based on the broaden and build theory it was hypothesised that there would be an increase in reaction time in trials primed with a happy face due to a broadening of attention, leading to increased flanker interference. A decrease in reaction time was predicted for trials primed with a sad face, due to a narrowing of attention leading to less flanker interference. Results lended partial support to the broaden and build hypothesis, with reaction times being slower following happy primes in incongruent flanker trials. Recent research is discussed in regards to potential mediators of the relationship between emotion and attention.

Keywords: emotion, attention, broaden and build, flanker task

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Authors: Anvikshaa Bisen, Krish Makkar

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Professionals misdiagnose autism by ticking off symptoms on a checklist without questioning the causes of said symptoms, and without understanding the innate neurophysiology of the autistic brain. A dysfunctional cingulate gyrus (CG) hyperfocuses attention in the left frontal lobe (logical/analytical) with no ability to access the right frontal lobe (emotional/creative), which plays a central role in spontaneity, social behavior, and nonverbal abilities. Autistic people live in a specialized inner space that is entirely intellectual, free from emotional and social distractions. They have no innate biological way of emotionally connecting with other people. Autistic people process their emotions intellectually, a process that can take 24 hours, by which time it is too late to have felt anything. An inactive amygdala makes it impossible for autistic people to experience fear. Because they do not feel emotion, they have no emotional memories. All memories are of events that happened about which they felt no emotion at the time and feel no emotion when talking about it afterward.

Keywords: autism, Asperger, Asd, neuropsychology, neuroscience

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Authors: Jaishree Ranganathan, Poonam Rajurkar, Angelina A. Tzacheva, Zbigniew W. Ras

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In today’s world, business often depends on the customer feedback and reviews. Sentiment analysis helps identify and extract information about the sentiment or emotion of the of the topic or document. Attribute selection is a challenging problem, especially with large datasets in actionable pattern mining algorithms. Action Rule Mining is one of the methods to discover actionable patterns from data. Action Rules are rules that help describe specific actions to be made in the form of conditions that help achieve the desired outcome. The rules help to change from any undesirable or negative state to a more desirable or positive state. In this paper, we present a Lexicon based weighted scheme approach to identify emotions from customer feedback data in the area of manufacturing business. Also, we use Rough sets and explore the attribute selection method for large scale datasets. Then we apply Actionable pattern mining to extract possible emotion change recommendations. This kind of recommendations help business analyst to improve their customer service which leads to customer satisfaction and increase sales revenue.

Keywords: actionable pattern discovery, attribute selection, business data, data mining, emotion

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Authors: Bubai Maji, Monorama Swain

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Speech is considered an essential and most natural medium for the interaction between machines and humans. However, extracting effective features for speech emotion recognition (SER) is remains challenging. The present studies show that the temporal information captured but high-level temporal-feature learning is yet to be investigated. In this paper, we present an efficient novel method using the Self-attention (SA) mechanism in a combination of Convolutional Neural Network (CNN) and Bi-directional Gated Recurrent Unit (Bi-GRU) network to learn high-level temporal-feature. In order to further enhance the representation of the high-level temporal-feature, we integrate a Bi-GRU output with learnable weights features by SA, and improve the performance. We evaluate our proposed method on our created SITB-OSED and IEMOCAP databases. We report that the experimental results of our proposed method achieve state-of-the-art performance on both databases.

Keywords: Bi-GRU, 1D-CNNs, self-attention, speech emotion recognition

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Authors: García López

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The Government Departments of Health have the task of informing and educating citizens about public health issues. For this, they use channels like Twitter, key in the search for health information and the propagation of content. The tweets, important in the virality of the content, may contain emotions that influence the contagion and exchange of knowledge. The goal of this study is to perform an analysis of the emotional projection of health information shared on Twitter by official American accounts: the disease control account CDCgov, National Institutes of Health, NIH, the government agency HHSGov, and the professional organization PublicHealth. For this, we used Tone Analyzer, an International Business Machines Corporation (IBM) tool specialized in emotion detection in text, corresponding to the categorical model of emotion representation. For 15 days, all tweets from these accounts were analyzed with the emotional analysis tool in text. The results showed that their tweets contain an important emotional load, a determining factor in the success of their communications. This exposes that official accounts also use subjective language and contain emotions. The predominance of emotion joy over sadness and the strong presence of emotions in their tweets stimulate the virality of content, a key in the work of informing that government health departments have.

Keywords: emotions in tweets, emotion detection in the text, health information on Twitter, American health official accounts, emotions on Twitter, emotions and content

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Authors: Michaela Porubanova

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In this exploratory study to examine the effects of emotional significance on change detection using the flicker paradigm, three different categories of scenes were randomly presented (neutral, positive and negative) in three different blocks. We hypothesized that because of the different effects on attention, performance in change detection tasks differs for scenes with different effective values. We found the greatest accuracy of change detection was for changes occurring in positive and negative scenes (compared with neutral scenes). Secondly and most importantly, changes in negative scenes (and also positive scenes, though not with statistical significance) were detected faster than changes in neutral scenes. Interestingly, women were less accurate than men in detecting changes in emotionally significant scenes (both negative and positive), i.e., women detected fewer changes in emotional scenes in the time limit of 40s. But on the other hand, women were quicker to detect changes in positive and negative images than men. The study makes important contributions to the area of the role of emotions on information processing. The role of emotion in attention will be discussed.

Keywords: attention, emotion, flicker task, IAPS

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Hyojung Shin, Munhee Kweon

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Non-suicide Self Injuri (NSSI) can be defined as the act of an individual who does not intend to die directly and intentionally damaging his or her body tissues. According to a study conducted by the Korean Ministry of Education in 2018, the NSSI is widely spreading among teenagers, with 7.9 percent of all middle school students and 6.4 percent of high school students reporting experience in NSSI. As such, it is understood that the first time of the NSSI is in adolescence. However, the NSSI may not start and stop at a certain time, but may last longer. However, despite the widespread prevalence of NSSI among teenagers, little is known about the process and maintenance of NSSI college students on a continuous development basis. Korea's NSSI research trends are mainly focused on individual internal vulnerabilities (high levels of painful emotions/awareness, lack of pain tolerance) and interpersonal vulnerabilities (poor communication skills and social problem solving), and little studies have been done on individuals' unique characteristics and environmental factors such as substrate or environmental vulnerability factors. In particular, environmental factors are associated with the occurrence of NSSI by acting as a vulnerability factor that can interfere with the emotional control of individuals, whereas individual factors play a more direct role by contributing to the maintenance of NSSI, so it is more important to consider this for personal environmental involvement in NSSI. This study focused on the Fight-Flight-Freeze System as a factor in the defensive avoidance system of Reward Sensitivity in individual factors. Also, Environmental factors include the level of expressed emotion in family. Wedig and Nock (2007) said that if parents with a self-critical cognitive style take the form of criticizing their children, the experience of NSSI increases. The high level of parental criticism is related to the increasing frequency of NSSI acts as well as to serious levels of NSSI. If the normal coping mechanism fails to control emotions, people want to overcome emotional difficulties even through NSSI, and emotional disturbances experienced by individuals within an unsupported social relationship increase vulnerability to NSSI. Based on these theories, this study is to find ways to prevent NSSI and intervene in counseling effectively by verifying the differences between the characteristics experienced NSSI persons and non-experienced NSSI persons. Therefore, the purpose of this research was to examine the relationship of Fight-Flight-Freeze System (FFFS), level of expressed emotion in family and emotion regulation difficulties, comparing those who experienced Non-Suicidal Self-Injury (NSSI) with those who did not experienced Non-Suicidal Self-Injury (NSSI). The data were collected from university students in Seoul Korea and Gyeonggi-do province. 99 subjects were experienced student of NSSI, while 375 were non- experienced student of NSSI. The results of this study are as follows. First, the result of t-test indicated that NSSI attempters showed a significant difference in fight-flight-freeze system, level of expressed emotion and emotion regulation difficulties, compared with non-attempters. Second, fight-flight-freeze system, level of expressed emotion in family and emotion regulation difficulties of NSSI attempters showed a significant difference in correlation. The correlation was significant only freeze system of fight-flight-freeze system, Level of expressed emotion in family and emotion regulation difficulties. Third, freeze system and level of expressed emotion in family predicted emotion regulation difficulties of NSSI attempters. Fight-freeze system and level of expressed emotion in family predicted emotion regulation difficulties of non-NSSI attempters. Lastly, Practical implications for counselors and limitations of this study are discussed.

Keywords: fight-flight-freeze system, level of expressed emotion in family, emotion regulation difficulty, non-suicidal self injury

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Authors: Amber Hatch, Eric Wright, Feihong Wang

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Research suggests that attachment in close relationships affects one’s emotional processes, mindfulness, conflict-management behaviors, and interpersonal interactions. Attachment insecurity is often associated with maladaptive social interactions and suboptimal relationship qualities. Past studies have considered how the nature of emotion regulation and mindfulness in teachers may be related to student or classroom outcomes. Still, no research has examined how the relationship between such internal experiences and classroom management outcomes may also be related to teachers’ attachment insecurity. This study examined the interrelationships between teachers’ attachment insecurity, mindfulness tendencies, emotion regulation abilities, and classroom management efficacy as indexed by students’ classroom behavior and teachers’ response effectiveness. Teachers’ attachment insecurity was evaluated using the global ECRS-SF, which measures both attachment anxiety and avoidance. The present study includes a convenient sample of 357 American elementary school teachers who responded to a survey regarding their classroom management efficacy, attachment in/security, dispositional mindfulness, emotion regulation strategies, and difficulties in emotion regulation, primarily assessed via pre-existing instruments. Good construct validity was demonstrated for all scales used in the survey. Sample demographics, including gender (94% female), race (92% White), age (M = 41.9 yrs.), years of teaching experience (M = 15.2 yrs.), and education level were similar to the population from which it was drawn, (i.e., American elementary school teachers). However, white women were slightly overrepresented in our sample. Correlational results suggest that teacher attachment insecurity is associated with poorer classroom management efficacy as indexed by students’ disruptive behavior and teachers’ response effectiveness. Attachment anxiety was a much stronger predictor of adverse student behaviors and ineffective teacher responses to adverse behaviors than attachment avoidance. Mindfulness, emotion regulation abilities, and years of teaching experience predicted positive classroom management outcomes. Attachment insecurity and mindfulness were more strongly related to frequent adverse student behaviors, while emotion regulation abilities were more strongly related to teachers’ response effectiveness. The teaching experience was negatively related to attachment insecurity and positively related to mindfulness and emotion regulation abilities. Although the data were cross-sectional, path analyses revealed that attachment insecurity is directly related to classroom management efficacy. Through two routes, this relationship is further mediated by emotion regulation and mindfulness in teachers. The first route of indirect effect suggests double mediation by teacher’s emotion regulation and then teacher mindfulness in the relationship between teacher attachment insecurity and classroom management efficacy. The second indirect effect suggests mindfulness directly mediated the relationship between attachment insecurity and classroom management efficacy, resulting in improved model fit statistics. However, this indirect effect is much smaller than the double mediation route through emotion regulation and mindfulness in teachers. Given the significant predication of teacher attachment insecurity, mindfulness, and emotion regulation on teachers’ classroom management efficacy both directly and indirectly, the authors recommend improving teachers’ classroom management efficacy via a three-pronged approach aiming at enhancing teachers’ secure attachment and supporting their learning adaptive emotion regulation strategies and mindfulness techniques.

Keywords: Classroom management efficacy, student behavior, teacher attachment, teacher emotion regulation, teacher mindfulness

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Authors: Norzaliza Md. Nor, Sh-Hussain Salleh, Mahyar Hamedi, Hadrina Hussain, Wahab Abdul Rahman

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Motivational factor is mainly the students’ desire to involve in learning process. However, it also depends on the goal towards their involvement or non-involvement in academic activity. Even though, the students’ motivation might be in the same level, but the basis of their motivation may differ. In this study, it focuses on the intrinsic motivational factor which student enjoy learning or feeling of accomplishment the activity or study for its own sake. The intrinsic motivational factor of students in learning mathematics and science has found as difficult to be achieved because it depends on students’ interest. In the Program for International Student Assessment (PISA) for mathematics and science, Malaysia is ranked as third lowest. The main problem in Malaysian educational system, students tend to have extrinsic motivation which they have to score in exam in order to achieve a good result and enrolled as university students. The use of electroencephalogram (EEG) signals has found to be scarce especially to identify the students’ intrinsic motivational factor in learning science and mathematics. In this research study, we are identifying the correlation between precursor emotion and its dynamic emotion to verify the intrinsic motivational factor of students in learning mathematics and science. The 2-D Affective Space Model (ASM) was used in this research in order to identify the relationship of precursor emotion and its dynamic emotion based on the four basic emotions, happy, calm, fear and sad. These four basic emotions are required to be used as reference stimuli. Then, in order to capture the brain waves, EEG device was used, while Mel Frequency Cepstral Coefficient (MFCC) was adopted to be used for extracting the features before it will be feed to Multilayer Perceptron (MLP) to classify the valence and arousal axes for the ASM. The results show that the precursor emotion had an influence the dynamic emotions and it identifies that most students have no interest in mathematics and science according to the negative emotion (sad and fear) appear in the EEG signals. We hope that these results can help us further relate the behavior and intrinsic motivational factor of students towards learning of mathematics and science.

Keywords: EEG, MLP, MFCC, intrinsic motivational factor

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Authors: Leah Lee

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This paper examines the correlation between deep learning models and emotions with noises to see whether or not noises mask emotions. The deep learning models used are plain convolutional neural networks (CNN), auto-encoder, long short-term memory (LSTM), and Visual Geometry Group-16 (VGG-16). Emotion datasets used are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Toronto Emotional Speech Set (TESS), and Surrey Audio-Visual Expressed Emotion (SAVEE). To make it four times bigger, audio set files, stretch, and pitch augmentations are utilized. From the augmented datasets, five different features are extracted for inputs of the models. There are eight different emotions to be classified. Noise variations are white noise, dog barking, and cough sounds. The variation in the signal-to-noise ratio (SNR) is 0, 20, and 40. In summation, per a deep learning model, nine different sets with noise and SNR variations and just augmented audio files without any noises will be used in the experiment. To compare the results of the deep learning models, the accuracy and receiver operating characteristic (ROC) are checked.

Keywords: auto-encoder, convolutional neural networks, long short-term memory, speech emotion recognition, visual geometry group-16

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Authors: Merve Denizci Nazligul

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Dancers seem to have much higher risk levels for the development of eating disorders, compared to non-dancing counterparts. In a remarkably competitive nature of dance environment, perfectionism and emotion regulation difficulties become inevitable risk factors. Moreover, early maladaptive schemas are associated with various eating disorders. In the current study, it was aimed to investigate the mediating role of difficulties with emotion regulation on the relationship between perfectionism and disordered eating behaviors, as well as on the relationship between early maladaptive schemas and disordered eating behaviors. A total of 70 volunteer dancers (n = 47 women, n = 23 men) were recruited in the study (M age = 25.91, SD = 8.9, range 19–63) from the university teams or private clubs in Turkey. The sample included various types of dancers (n = 26 ballets or ballerinas, n =32 Latin, n = 10 tango, n = 2 hiphop). The mean dancing hour per week was 11.09 (SD = 7.09) within a range of 1-30 hours. The participants filled a questionnaire set including demographic information form, Dutch Eating Behavior Questionnaire, Multidimensional Perfectionism Scale, three subscales (Emotional Inhibition, Unrelenting Standards-Hypercriticalness, Approval Seeking-Recognition Seeking) from Young Schema Questionnaire-Short Form-3 and Difficulties in Emotion Regulation Scale. The mediation hypotheses were tested using the PROCESS macro in SPSS. The findings revealed that emotion regulation difficulties significantly mediated the relationship between three distinct subtypes of perfectionism and emotional eating. The results of the Sobel test suggested that there were significant indirect effects of self-oriented perfectionism (b = .06, 95% CI = .0084, .1739), other-oriented perfectionism (b = .15, 95% CI = .0136, .4185), and socially prescribed perfectionism (b = .09, 95% CI = .0104, .2344) on emotional eating through difficulties with emotion regulation. Moreover, emotion regulation difficulties significantly mediated the relationship between emotional inhibition and emotional eating (F(1,68) = 4.67, R2 = .06, p < .05). These results seem to provide some evidence that perfectionism might become a risk factor for disordered eating behaviors when dancers are not able to regulate their emotions. Further, gaining an understanding of how inhibition of emotions leads to inverse effects on eating behavior may be important to develop intervention strategies to manage their disordered eating patterns in risk groups. The present study may also support the importance of using unified protocols for transdiagnostic approaches which focus on identifying, accepting, prompting to express maladaptive emotions and appraisals.

Keywords: dancers, disordered eating, emotion regulation difficulties, perfectionism

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Authors: Mikel Alonso López

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The early eighties saw the rise of a new research trend in several prestigious journals, mainly articles that related emotions with the decision-making processes of the consumer, and stopped treating them as external elements. That is why we ask questions such as: what are emotions? Are there different types of emotions? What components do they have? Which theories exist about them? In this study, we will review the main theories and components of emotion analysing the cognitive factor and the different emotional states that are generally recognizable with a focus in the classic debate as to whether they occur before the cognitive process or the affective process.

Keywords: emotion, consumer behaviour, feelings, decision making

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Authors: Songyun Chen

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Recent scholarly attention to sentiment analysis has provided researchers with a deeper understanding of how emotions are conveyed in writing and leveraged by academic authors as a persuasive tool. Using the National Research Council (NRC) Sentiment Lexicons (version 1.0) created by the National Research Council Canada, this study examined specific emotions in research articles (RAs) across four disciplines, including literature, education, biology, and computer & information science based on four datasets totaling over three million tokens, aiming to reveal how the emotions are conveyed by authors in academic writing. The results showed that four emotions—trust, anticipation, joy, and surprise—were observed in all four disciplines, while sadness emotion was spotted solely in literature. With the emotion of trust being overwhelmingly prominent, the rest emotions varied significantly across disciplines. The findings contribute to our understanding of emotion strategy applied in academic writing and genre characteristics of RAs.

Keywords: sentiment analysis, specific emotions, emotional landscape, research articles, academic writing

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Authors: Avyarthana Dey, Naren P. Rao, Arpitha Jacob, Chaitra V. Hiremath, Shivarama Varambally, Ganesan Venkatasubramanian, Rose Dawn Bharath, Bangalore N. Gangadhar

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Objective: Impaired facial emotion recognition is widely reported in schizophrenia. Neuropeptide oxytocin is known to modulate brain regions involved in facial emotion recognition, namely amygdala, in healthy volunteers. However, its effect on facial emotion recognition deficits seen in schizophrenia is not well explored. In this study, we examined the effect of intranasal OXT on processing facial emotions and its neural correlates in patients with schizophrenia. Method: 12 male patients (age= 31.08±7.61 years, education= 14.50±2.20 years) participated in this single-blind, counterbalanced functional magnetic resonance imaging (fMRI) study. All participants underwent three fMRI scans; one at baseline, one each after single dose 24IU intranasal OXT and intranasal placebo. The order of administration of OXT and placebo were counterbalanced and subject was blind to the drug administered. Participants performed a facial emotion recognition task presented in a block design with six alternating blocks of faces and shapes. The faces depicted happy, angry or fearful emotions. The images were preprocessed and analyzed using SPM 12. First level contrasts comparing recognition of emotions and shapes were modelled at individual subject level. A group level analysis was performed using the contrasts generated at the first level to compare the effects of intranasal OXT and placebo. The results were thresholded at uncorrected p < 0.001 with a cluster size of 6 voxels. Neuropeptide oxytocin is known to modulate brain regions involved in facial emotion recognition, namely amygdala, in healthy volunteers. Results: Compared to placebo, intranasal OXT attenuated activity in inferior temporal, fusiform and parahippocampal gyri (BA 20), premotor cortex (BA 6), middle frontal gyrus (BA 10) and anterior cingulate gyrus (BA 24) and enhanced activity in the middle occipital gyrus (BA 18), inferior occipital gyrus (BA 19), and superior temporal gyrus (BA 22). There were no significant differences between the conditions on the accuracy scores of emotion recognition between baseline (77.3±18.38), oxytocin (82.63 ± 10.92) or Placebo (76.62 ± 22.67). Conclusion: Our results provide further evidence to the modulatory effect of oxytocin in patients with schizophrenia. Single dose oxytocin resulted in significant changes in activity of brain regions involved in emotion processing. Future studies need to examine the effectiveness of long-term treatment with OXT for emotion recognition deficits in patients with schizophrenia.

Keywords: recognition, functional connectivity, oxytocin, schizophrenia, social cognition

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Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

Abstract:

Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: Sook Ching Yee, Angela Siew Hoong Lee

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Big data technologies have become a trend to exploit business opportunities and provide valuable business insights through the analysis of big data. However, there are still many organizations that have yet to adopt big data technologies especially small and medium organizations (SME). This study uses the technology acceptance model (TAM) to look into several constructs in the TAM and other additional constructs which are positive affect, negative affect, organizational factor and motivational factor. The conceptual model proposed in the study will be tested on the relationship and influence of positive affect, negative affect, organizational factor and motivational factor towards the intention to use big data technologies to produce an outcome. Empirical research is used in this study by conducting a survey to collect data.

Keywords: big data technologies, motivational factor, negative affect, organizational factor, positive affect, technology acceptance model (TAM)

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Authors: Gabrielle Veloso, Welison Ty

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Trait anxiety is a pervasive tendency to attend to and experience fears and worries to a disproportionate degree, across various situations. This study sought to determine if participants who undergo emotional working memory training will have significantly lower scores on the trait anxiety scales post-intervention. The study also sought to determine if emotional regulation mediated the relationship between working memory training and trait anxiety. Forty-nine participants underwent 20 days of computerized emotional working memory training called Emotional Dual n-back, which involves viewing a continuous stream of emotional content on a grid, and then remembering the location and color of items presented on the grid. Participants of the treatment group had significantly lower trait anxiety compared to controls post-intervention. Mediation analysis determined that working memory training had no significant relationship to anxiety as measured by the Beck’s Anxiety Inventory-Trait (BAIT), but was significantly related to anxiety as measured by form Y2 of the Spielberger State-Trait Anxiety Inventory (STAI-Y2). Emotion regulation, as measured by the Emotional Regulation Questionnaire (ERQ), was found not to mediate between working memory training and trait anxiety reduction. Results suggest that working memory training may be useful in reducing psychoemotional symptoms rather than somatic symptoms of trait anxiety. Moreover, it proposes for future research to further look into the mediating role of emotion regulation via neuroimaging and the development of more comprehensive measures of emotion regulation.

Keywords: anxiety, emotion regulation, working-memory, working-memory training

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Authors: H. Abas Nejad, A. R. Teymoori

Abstract:

Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, etc. in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as the user stays neutral for the majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this work, we propose a light-weight neutral vs. emotion classification engine, which acts as a preprocessor to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at Key Emotion (KE) points using a textural statistical model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a textural statistical model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves ER accuracy and simultaneously reduces the computational complexity of ER system, as validated on multiple databases.

Keywords: neutral vs. emotion classification, Constrained Local Model, procrustes analysis, Local Binary Pattern Histogram, statistical model

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Authors: Junwen Chen, Celina Feleppa, Tingyue Sun, Satoko Sasagawa, Michael Smithson

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School refusal behaviours refer to behaviours to avoid school attendance, chronic lateness in arriving at school, or regular early dismissal. Poor attendance in schools is highly correlated with anxiety, depression, suicide attempts, delinquency, violence, and substance use and abuse. Poor attendance is also a strong indicator of lower achievement in school, as well as problematic social-emotional development. Long-term consequences of school refusal behaviours include fewer opportunities for higher education, employment, and social difficulties, and high risks of later psychiatric illness. Given its negative impacts on youth educational outcomes and well-being, a thorough understanding of factors that are involved in the development of this phenomenon is warranted for developing effective management approaches. This study investigated parental and adolescent factors that may contribute to school refusal behaviours by specifically focusing on the role of parental and adolescents’ anxiety and depression, emotion dysregulation, and parental rearing style. Findings are expected to inform the identification of both parental and adolescents’ factors that may contribute to school refusal behaviours. This knowledge will enable novel and effective approaches that incorporate these factors to managing school refusal behaviours in adolescents, which in turn improve their school and daily functioning. Results are important for an integrative understanding of school refusal behaviours. Furthermore, findings will also provide information for policymakers to weigh the benefits of interventions targeting school refusal behaviours in adolescents. One-hundred-and-six adolescents aged 12-18 years (mean age = 14.79 years old, SD = 1.78, males = 44) and their parents (mean age = 47.49 years old, SD = 5.61, males = 27) completed an online questionnaire measuring both parental and adolescents’ anxiety, depression, emotion dysregulation, parental rearing styles, and adolescents’ school refusal behaviours. Adolescents with school refusal behaviours reported greater anxiety and depression, with their parents showing greater emotion dysregulation. Parental emotion dysregulation and adolescents’ anxiety and depression predicted school refusal behaviours independently. To date, only limited studies have investigated the interplay between parental and youth factors in relation to youth school refusal behaviours. Although parental emotion dysregulation has been investigated in relation to youth emotion dysregulation, little is known about its role in the context of school refusal. This study is one of the very few that investigated both parental and adolescent factors in relation to school refusal behaviours in adolescents. The findings support the theoretical models that emphasise the role of youth and parental psychopathology in school refusal behaviours. Future management of school refusal behaviours should target adolescents’ anxiety and depression while incorporating training for parental emotion regulation skills.

Keywords: adolescents, school refusal behaviors, parental factors, anxiety and depression, emotion dysregulation

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Authors: Dorottya Horváth, Andras Lang, Diana Varro-Horvath

Abstract:

This research activity and study is part of a major research effort to gain an integrative, neuropsychological, and personality psychological understanding of Attention Deficit Hyperactivity Disorder (ADHD) and thus improve the specification of diagnostic and therapeutic care. In the past, the neuropsychology section has investigated working memory, executive function, attention, and behavioural manifestations in children. Currently, we are looking for personality psychological protective factors for ADHD and its symptomatic exacerbation. We hypothesise that secure attachment, adaptive emotion regulation, and high resilience are protective factors. The aim of this study is to measure and report the results of a Hungarian sample of the Cognitive Emotion Regulation Questionnaire for Children (CERQ-k) because before studying groups with different developmental differences, it is essential to know the average scores of groups with neurotypical devel-opment. Until now, there was no Hungarian version of the above test, so we used our own translation. This questionnaire has been developed to assess children's thoughts after experiencing negative life events. It consists of 4-4 items per subscale, for a total of 36 items. The response categories for each item range from 1 (almost never) to 5 (almost always). The subscales were self-blame, blaming others, acceptance, planning, positive refocusing, rumination or thought-focusing, positive reappraisal, putting into perspective, and catastrophizing. The data for this study were collected from 120 children aged 9-14 years. It was analysed using descriptive statistical analysis, where the mean and standard deviation values for each age group, as well as the Cronbach's alpha value, were significant in testing the reliability of the questionnaire. The results showed that the questionnaire is a reliable and valid measuring instrument also on a Hungarian sample. These developments and results will allow the use of a version of the Cognitive Emotion Regulation Questionnaire for children in Hungarian and pave the way for the study of different developmental groups such as children with learning disabilities and/or with ADHD.

Keywords: neurotypical development, emotion regulation, negative life events, CERQ-k, Hungarian average scores

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