Epilepsy is a chronic neurological disorder characterised by epileptic seizures. It may be accompanied by cognitive deficits and unappropriate affective changes. This theses addresses an as yet not well investigated - emotion recognition and empathy in epilepsy. Firstly, the theoretical part describes emotion recognition that uses information from facial expression, posture, gestures and utterances. Then, it discusses empathy as the capacity to comprehend other persons' feelings and incentives from their own perspective, which increases individuals' prosocial behaviour. Neurobiological correlates of both are described thereafter. As a part of social cognition, these can also be impaired in epilepsy disorder - which is depicted in two most extensive thesis subchapters. The theoretical part is followed by the empirical one. It introduces own research which have explorative character, and is focused on 28 patients with idiopathic generalised epilepsy and their 21 asymptomatic biological siblings in comparision to healthy control groups. It uses translated testing methods and verifies in a basic manner their appropriatness of usage. In consequence, it reports the outputs which do not confirm any significant differences among participant groups. However, they show mild deviation of the patients' and...

Indiana University-Purdue University Indianapolis (IUPUI) / Emotions are essential for our existence, as they exert great influence on the mental health of people. Speech is the most powerful mode to communicate. It controls our intentions and emotions. Over the past years many researchers worked hard to recognize emotion from speech samples. Many systems have been proposed to make the Speech Emotion Recognition (SER) process more correct and accurate. This thesis research discusses the design of speech emotion recognition system implementing a comparatively new method, i-vector model. I-vector model has found much success in the areas of speaker identification, speech recognition, and language identification. But it has not been much explored in recognition of emotion. In this research, i-vector model was implemented in processing extracted features for speech representation. Two different classification schemes were designed using two different classifiers - Gaussian Mixture Model (GMM) and Support Vector Machine (SVM), along with i-vector algorithm. Performance of these two systems was evaluated using the same emotional speech database to identify four emotional speech signals: Angry, Happy, Sad and Neutral. Results were analyzed, and more than 75% of accuracy was obtained by both systems, which proved that our proposed i-vector algorithm can identify speech emotions with less error and with more accuracy.

Depressed persons have demonstrated emotion based cognitive biases, specifically surrounding vigilance of negative information and avoidance of positivity. These biases are sometimes operationalized through emotion recognition tasks. However, previous emotion recognition tasks lack in their ability to accurately measure and decompose positivity avoidance with enhanced negativity, while accounting for basic cognitive processes that can drive the results. Therefore, we developed a novel emotion recognition task that examines emotional intensity thresholds, while accounting for general response bias. Linear mixed effects modeling revealed substantial individual differences on all conditions in the task, using both frequentist and Bayesian approaches. Additionally, the findings suggest that those with higher depression scores exhibit a greater cognitive response bias on emotion recognition tasks. Ultimately, this study provides evidence that there is variability in performance on the morphing paradigm, as further researcher is needed to assess the influence general response biases have on emotion recognition performance in depression.

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Individuals with autism spectrum disorders (ASD) struggle with identifying others' emotions, which impacts their ability to successfully interact in social situations. Because of the increasing number of children identified with ASD, effective techniques are needed to help children identify emotions in others. The use of technology is being researched as a way to help children with emotion identification. However, technology is not always available for teachers to use in classrooms, whereas picture books are much easier to access and have been successfully used to improve students' social skills. Picture books are naturally used in classroom, home, and therapy settings. This study investigated the effectiveness of using picture books as a teaching tool with students with ASD, helping them learn how to identify emotions. A multiple baseline across three male subjects between the ages of six and ten was employed. Each picture book focused on teaching one specific emotion: scared, sad, and furious. Following intervention, when shown novel photographs, two of the participants identified three target emotions. One participant successfully identified one target emotion and showed marked improvement in identifying the other two target emotions. Using picture books is an easy, inexpensive way to teach emotions and can be naturally included in a classroom. Parents and other professionals can use picture books in a home or therapy setting to help children with ASD learn emotions and improve their social understanding.

BACKGROUND: Obesity is a serious health condition that also a risk factor for socio-emotional challenges and medical problems. Preliminary evidence suggests obesity may also be associated with difficulty in accurately identifying emotions, particularly negative emotions. In addition, poor emotion recognition has been linked to weaker executive functioning skills, which is a common challenge in obesity. The direct relationship between body mass index (BMI) and emotion recognition is poorly understood in young adults and warrants further exploration. HYPOTHESES: We predicted that 1) after controlling for sociodemographic, clinical, and executive functioning variables and that 2) BMI would be negatively associated with emotion recognition accuracy for negative emotions (i.e., anger, sadness, and fear) but not positive emotions. METHODS: Using a subset of the Human Connectome Project dataset (N=799), we conducted a hierarchal linear regression (HLR) to test the relationship between overall emotion recognition and the following predictors, adding in steps: 1) sociodemographic and clinical variables, 2) executive functioning variables, and 3) BMI. RESULTS: Contrary to our hypotheses, BMI was not significantly associated with overall emotion recognition accuracy. Instead, Hispanic ethnicity, greater cognitive flexibility (Dimensional Change Card Sort task), and larger working memory (List Sorting Working Memory Test) was associated with better overall emotion recognition accuracy. Similarly, these same dimensions, as well as being female, was associated with better negative emotion recognition accuracy. / Psychology

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<p> In this paper, the authors explore different approaches to animating 3D facial emotions, some of which use manual keyframe facial animation and some of which use machine learning. To compare approaches the authors conducted an experiment consisting of side-by-side comparisons of animation clips generated by skeleton, blendshape, audio-driven, and vision-based capture techniques.</p> <p>Ninety-five participants viewed twenty face animation clips of characters expressing five distinct emotions (anger, sadness, happiness, fear, neutral), which were created using four different facial animation techniques. After viewing each clip, the participants were asked to score the naturalness on a 5-point Likert scale and to identify the emotions that the characters appeared to be conveying.</p> <p>Although the happy emotion clips differed slightly in the naturalness ratings, the naturalness scores of happy emotions produced by the four methods tended to be consistent. The naturalness ratings of the fear emotion created with skeletal animation were higher than other methods.Recognition of sad and neutral were very low for all methods as compared to other emotions. Findings also showed that a few people participants were able to identify the clips that were machine generated rather than created by a human artist.The means, boxplots and HSD revealed that the skeleton approach had significantly higher ratings for naturalness and higher recognition rate than the other methods.</p>