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Littérature scientifique sur le sujet « Recognition of emotions »

Auteur : Grafiati

Publié le 10 mars 2023

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Articles de revues sur le sujet "Recognition of emotions"

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Liao, Songyang, Katsuaki Sakata, and Galina V. Paramei. "Color Affects Recognition of Emoticon Expressions." i-Perception 13, no. 1 (2022): 204166952210807. http://dx.doi.org/10.1177/20416695221080778.

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In computer-mediated communication, emoticons are conventionally rendered in yellow. Previous studies demonstrated that colors evoke certain affective meanings, and face color modulates perceived emotion. We investigated whether color variation affects the recognition of emoticon expressions. Japanese participants were presented with emoticons depicting four basic emotions (Happy, Sad, Angry, Surprised) and a Neutral expression, each rendered in eight colors. Four conditions (E1–E4) were employed in the lab-based experiment; E5, with an additional participant sample, was an online replication

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Mallikarjuna, Basetty, M. Sethu Ram, and Supriya Addanke. "An Improved Face-Emotion Recognition to Automatically Generate Human Expression With Emoticons." International Journal of Reliable and Quality E-Healthcare 11, no. 1 (2022): 1–18. http://dx.doi.org/10.4018/ijrqeh.314945.

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Any human face image expression naturally identifies expressions of happy, sad etc.; sometimes human facial image expression recognition is complex, and it is a combination of two emotions. The existing literature provides face emotion classification and image recognition, and the study on deep learning using convolutional neural networks (CNN), provides face emotion recognition most useful for healthcare and with the most complex of the existing algorithms. This paper improves the human face emotion recognition and provides feelings of interest for others to generate emoticons on their smartp

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Kamińska, Dorota, Kadir Aktas, Davit Rizhinashvili, et al. "Two-Stage Recognition and beyond for Compound Facial Emotion Recognition." Electronics 10, no. 22 (2021): 2847. http://dx.doi.org/10.3390/electronics10222847.

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Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to addres

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Werner, S., and G. N. Petrenko. "Speech Emotion Recognition: Humans vs Machines." Discourse 5, no. 5 (2019): 136–52. http://dx.doi.org/10.32603/2412-8562-2019-5-5-136-152.

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Introduction. The study focuses on emotional speech perception and speech emotion recognition using prosodic clues alone. Theoretical problems of defining prosody, intonation and emotion along with the challenges of emotion classification are discussed. An overview of acoustic and perceptional correlates of emotions found in speech is provided. Technical approaches to speech emotion recognition are also considered in the light of the latest emotional speech automatic classification experiments.Methodology and sources. The typical “big six” classification commonly used in technical applications

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Hatem, Ahmed Samit, and Abbas M. Al-Bakry. "The Information Channels of Emotion Recognition: A Review." Webology 19, no. 1 (2022): 927–41. http://dx.doi.org/10.14704/web/v19i1/web19064.

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Humans are emotional beings. When we express about emotions, we frequently use several modalities, whether we want to so overtly (i.e., Speech, facial expressions,..) or implicitly (i.e., body language, text,..). Emotion recognition has lately piqued the interest of many researchers, and various techniques have been studied. A review on emotion recognition is given in this article. The survey seeks single and multiple source of data or information channels that may be utilized to identify emotions and includes a literature analysis on current studies published to each information channel, as w

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Morgan, Shae D. "Comparing Emotion Recognition and Word Recognition in Background Noise." Journal of Speech, Language, and Hearing Research 64, no. 5 (2021): 1758–72. http://dx.doi.org/10.1044/2021_jslhr-20-00153.

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Purpose Word recognition in quiet and in background noise has been thoroughly investigated in previous research to establish segmental speech recognition performance as a function of stimulus characteristics (e.g., audibility). Similar methods to investigate recognition performance for suprasegmental information (e.g., acoustic cues used to make judgments of talker age, sex, or emotional state) have not been performed. In this work, we directly compared emotion and word recognition performance in different levels of background noise to identify psychoacoustic properties of emotion recognition

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Israelashvili, Jacob, Lisanne S. Pauw, Disa A. Sauter, and Agneta H. Fischer. "Emotion Recognition from Realistic Dynamic Emotional Expressions Cohere with Established Emotion Recognition Tests: A Proof-of-Concept Validation of the Emotional Accuracy Test." Journal of Intelligence 9, no. 2 (2021): 25. http://dx.doi.org/10.3390/jintelligence9020025.

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Individual differences in understanding other people’s emotions have typically been studied with recognition tests using prototypical emotional expressions. These tests have been criticized for the use of posed, prototypical displays, raising the question of whether such tests tell us anything about the ability to understand spontaneous, non-prototypical emotional expressions. Here, we employ the Emotional Accuracy Test (EAT), which uses natural emotional expressions and defines the recognition as the match between the emotion ratings of a target and a perceiver. In two preregistered studies (

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Ekberg, Mattias, Josefine Andin, Stefan Stenfelt, and Örjan Dahlström. "Effects of mild-to-moderate sensorineural hearing loss and signal amplification on vocal emotion recognition in middle-aged–older individuals." PLOS ONE 17, no. 1 (2022): e0261354. http://dx.doi.org/10.1371/journal.pone.0261354.

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Previous research has shown deficits in vocal emotion recognition in sub-populations of individuals with hearing loss, making this a high priority research topic. However, previous research has only examined vocal emotion recognition using verbal material, in which emotions are expressed through emotional prosody. There is evidence that older individuals with hearing loss suffer from deficits in general prosody recognition, not specific to emotional prosody. No study has examined the recognition of non-verbal vocalization, which constitutes another important source for the vocal communication

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Lim, Myung-Jin, Moung-Ho Yi, and Ju-Hyun Shin. "Intrinsic Emotion Recognition Considering the Emotional Association in Dialogues." Electronics 12, no. 2 (2023): 326. http://dx.doi.org/10.3390/electronics12020326.

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Computer communication via text messaging or Social Networking Services (SNS) has become increasingly popular. At this time, many studies are being conducted to analyze user information or opinions and recognize emotions by using a large amount of data. Currently, the methods for the emotion recognition of dialogues requires an analysis of emotion keywords or vocabulary, and dialogue data are mostly classified as a single emotion. Recently, datasets classified as multiple emotions have emerged, but most of them are composed of English datasets. For accurate emotion recognition, a method for re

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Jaratrotkamjorn, Apichart. "Bimodal Emotion Recognition Using Deep Belief Network." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 15, no. 1 (2021): 73–81. http://dx.doi.org/10.37936/ecti-cit.2021151.226446.

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The emotions are very important in human daily life. In order to make the machine can recognize the human emotional state, and it can intelligently respond to need for human, which are very important in human-computer interaction. The majority of existing work concentrate on the classification of six basic emotions only. In this research work propose the emotion recognition system through the multimodal approach, which integrated information from both facial and speech expressions. The database has eight basic emotions (neutral, calm, happy, sad, angry, fearful, disgust, and surprised). Emotio

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Thèses sur le sujet "Recognition of emotions"

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Stanley, Jennifer Tehan. "Emotion recognition in context." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24617.

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Thesis (Ph.D.)--Psychology, Georgia Institute of Technology, 2008.<br>Committee Chair: Blanchard-Fields, Fredda; Committee Member: Corballis, Paul; Committee Member: Hertzog, Christopher; Committee Member: Isaacowitz, Derek; Committee Member: Kanfer, Ruth

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Zhang, Jiaming. "Contextual recognition of robot emotions." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/3809/.

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In the field of human-robot interaction, socially interactive robots are often equipped with the ability to detect the affective states of users, the ability to express emotions through the use of synthetic facial expressions, speech and textual content, and the ability for imitating and social learning. Past work on creating robots that can make convincing emotional expressions has concentrated on the quality of those expressions, and on assessing people’s ability to recognize them. Previous recognition studies presented the facial expressions of the robots in neutral contexts, without any st

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Xiao, Zhongzhe. "Recognition of emotions in audio signals." Ecully, Ecole centrale de Lyon, 2008. http://www.theses.fr/2008ECDL0002.

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Les travaux de recherche réalisés dans le cadre de cette thèse de doctorat portent sur la reconnaissance automatique de l’émotion et de l’humeur au sein de signaux sonores. En effet, l’émotion portée par les signaux audio constitue une information sémantique particulièrement importante dont l’analyse automatique offre de nombreuses possibilités en termes d’applications, telles que les interactions homme-machine intelligentes et l’indexation multimédia. L’objectif de cette thèse est ainsi d’étudier des solutions informatique d’analyse de l’émotion audio tant pour la parole que pour les signaux

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Xiao, Zhongzhe Chen Liming. "Recognition of emotions in audio signals." Ecully : Ecole Centrale de Lyon, 2008. http://bibli.ec-lyon.fr/exl-doc/zxiao.pdf.

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Golan, Ofer. "Systemising emotions : teaching emotion recognition to people with autism using interactive multimedia." Thesis, University of Cambridge, 2007. https://www.repository.cam.ac.uk/handle/1810/252028.

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Recognition of emotions and mental states (ER) in others is a core difficulty for individuals with autism spectrum conditions (ASC). In contrast, they show good skills in ‘systemizing’- understanding non-agentive systems. This thesis evaluated the effectiveness of <i>Mind Reading</i>, a computer program teaching ER from a wide range of facial expression videos and recorded speech segments, systematically presented. Three different experiments tested the effectiveness of a minimum of 10 hours of software use over a period of 10-15 weeks among individuals with ASC. Experiments included evaluatio

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Cheung, Ching-ying Crystal. "Cognition of emotion recognition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B29740277.

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Reichert, Nils. "CORRELATION BETWEEN COMPUTER RECOGNIZED FACIAL EMOTIONS AND INFORMED EMOTIONS DURING A CASINO COMPUTER GAME." Thesis, Fredericton: University of New Brunswick, 2012. http://hdl.handle.net/1882/44596.

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Emotions play an important role for everyday communication. Different methods allow computers to recognize emotions. Most are trained with acted emotions and it is unknown if such a model would work for recognizing naturally appearing emotions. An experiment was setup to estimate the recognition accuracy of the emotion recognition software SHORE, which could detect the emotions angry, happy, sad, and surprised. Subjects played a casino game while being recorded. The software recognition was correlated with the recognition of ten human observers. The results showed a strong recognition for happ

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Gohar, Kadar Navit. "Diagnostic colours of emotions." University of Sydney, 2008. http://hdl.handle.net/2123/2298.

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Doctor of Philosophy<br>This thesis investigates the role of colour in the cognitive processesing of emotional information. The research is guided by the effect of colour diagnosticity which has been shown previously to influence recognition performance of several types of objects as well as natural scenes. The research presented in Experiment 1 examined whether colour information is considered a diagnostic perceptual feature of seven emotional categories: happiness, sadness, anger, fear, disgust, surprise and neutral. Participants (N = 119), who were naïve to the specific purpose and expecta

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Lau, Yuet-han Jasmine. "Ageing-related effect on emotion recognition." Click to view E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37101730.

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Gohar, Kadar Navit. "Diagnostic colours of emotions." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/2298.

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This thesis investigates the role of colour in the cognitive processesing of emotional information. The research is guided by the effect of colour diagnosticity which has been shown previously to influence recognition performance of several types of objects as well as natural scenes. The research presented in Experiment 1 examined whether colour information is considered a diagnostic perceptual feature of seven emotional categories: happiness, sadness, anger, fear, disgust, surprise and neutral. Participants (N = 119), who were naïve to the specific purpose and expectations of the experiment,

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Livres sur le sujet "Recognition of emotions"

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Yang, Yi-Hsuan. Music emotion recognition. CRC, 2011.

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My mixed emotions: Help your kids handle their feelings. Dorling Kindersley, 2018.

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H, Chen Homer, ed. Music emotion recognition. CRC, 2011.

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A, Tsihrintzis George, ed. Visual affect recognition. IOS Press, 2010.

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Gevarter, William B. MoCogl: A computer simulation of recognition-primed human decision making, considering emotions. National Aeronautics and Space Administration, 1992.

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author, Mao Qirong, Lin, Qing, active 2013 author, and Cheng Keyang author, eds. Shi jue yu yin qing gan shi bie. Ke xue chu ban she, 2013.

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N, Emde Robert, Osofsky Joy D, and Butterfield Perry M. 1932-, eds. The IFEEL pictures: A new instrument for interpreting emotions. International Universities Press, 1993.

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Lambelet, Clément. Happiness is the only true emotion: Clément Lambelet. RVB books, 2019.

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Michela, Balconi, ed. Neuropsychology and cognition of emotional face comprehension, 2006. Research Signpost, 2006.

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Konar, Amit, and Aruna Chakraborty, eds. Emotion Recognition. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118910566.

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Chapitres de livres sur le sujet "Recognition of emotions"

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Rao, K. Sreenivasa, and Shashidhar G. Koolagudi. "Emotion Recognition on Real Life Emotions." In SpringerBriefs in Electrical and Computer Engineering. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6360-3_6.

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Bonomi, Alberto G. "Physical Activity Recognition Using a Wearable Accelerometer." In Sensing Emotions. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3258-4_3.

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Haines, Simon. "Recognition in Shakespeare and Hegel." In Shakespeare and Emotions. Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137464750_20.

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Hupont, Isabelle, Sergio Ballano, Eva Cerezo, and Sandra Baldassarri. "From a Discrete Perspective of Emotions to Continuous, Dynamic, and Multimodal Affect Sensing." In Emotion Recognition. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118910566.ch18.

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Zhang, Jiaming, and Amanda J. C. Sharkey. "Contextual Recognition of Robot Emotions." In Towards Autonomous Robotic Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23232-9_8.

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Laitinen, Arto. "Collective Intentionality and Recognition from Others." In Institutions, Emotions, and Group Agents. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6934-2_13.

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Singh, Rajiv, Swati Nigam, Amit Kumar Singh, and Mohamed Elhoseny. "Biometric Recognition of Emotions Using Wavelets." In Intelligent Wavelet Based Techniques for Advanced Multimedia Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31873-4_9.

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González-Meneses, Yesenia N., Josefina Guerrero-García, Carlos Alberto Reyes-García, and Ramón Zatarain-Cabada. "Automatic Recognition of Learning-Centered Emotions." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77004-4_4.

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Grekow, Jacek. "Representations of Emotions." In From Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70609-2_2.

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Pereira, Lara, Susana Brás, and Raquel Sebastião. "Characterization of Emotions Through Facial Electromyogram Signals." In Pattern Recognition and Image Analysis. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04881-4_19.

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Actes de conférences sur le sujet "Recognition of emotions"

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Sinha, Arryan, and G. Suseela. "Deep Learning-Based Speech Emotion Recognition." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-0892re.

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Speech Emotion Recognition, as described in this study, uses Neural Networks to classify the emotions expressed in each speech (SER). It’s centered upon concept where voice tone and pitch frequently reflect underlying emotion. Speech Emotion Recognition aids in the classification of elicited emotions. The MLP-Classifier is a tool for classifying emotions in a circumstance. As wave signal, allowing for flexible learning rate selection. RAVDESS (Ryerson Audio-Visual Dataset Emotional Speech and Song Database data) will be used. To extract the characteristics from particular audio input, Contrast

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Schmid, Ramona, Sophia Maria Saat, Knut Möller, and Verena Wagner-Hartl. "Induction method influence on emotion recognition based on psychophysiological parameters." In Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002851.

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Recognizing emotions is an essential ability in our daily social interactions. However, there are individuals who have difficulties interpreting emotions, such as patients with autism spectrum disorders (ASD). In order to cope better with everyday life, emotion training can be a supporting factor for them. However, studies show that emotion training is not only helpful for patients with ASD, but also in the working environment, for example in trainings for managers or teams. In recent research, there are already approaches to use new technologies such as virtual reality to train emotional and

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Esau, Natascha, Lisa Kleinjohann, and Bernd Kleinjohann. "Emotional Competence in Human-Robot Communication." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49409.

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Since emotional competence is an important factor in human communication, it will certainly also improve communication between humans and robots or other machines. Emotional competence is defined by the aspects emotion recognition, emotion representation, emotion regulation and emotional behavior. In this paper we present how these aspects are intergrated into the architecture of the robot head MEXI. MEXI is able to recognize emotions from facial expressions and prosody of natural speech and represents its internal state made up of emotions and drives by according facial expressions, head move

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Hou, Tianyu, Nicoletta Adamo, and Nicholas J. Villani. "Micro-expressions in Animated Agents." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001081.

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The purpose of this research was to examine the perception of micro-expressions in animated agents with different visual styles. Specifically, the work reported in the paper sought to examine: (1) whether people can recognize micro-expressions in animated agents, (2) the extent to which the degree of exaggeration of micro-expressions affects recognition, perceived naturalness and intensity of the animated agents’ emotions, and (3) whether there are differences in recognition and perception based on the agent’s visual style (realistic vs stylized). The research work involved two experiments: a

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Schmid, Ramona, Linn Braunmiller, Lena Hansen, Christopher Schonert, Knut Möller, and Verena Wagner-Hartl *. "Emotion recognition - Validation of a measurement environment based on psychophysiological parameters." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001065.

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Emotions are a fundamental part of our social interaction. A person for whom it is difficult or impossible to interpret emotions may face major problems in everyday life, e.g., patients with autism spectrum disorders. However, understanding emotions is not only of great importance in private social interactions but also in the working environment, e.g., for managers or collaborative work. Hence, there is a great interest in emotion research, including how emotions can be measured. For this purpose, a measuring environment was developed. The aim of the presented study was to validate this measu

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Veltmeijer, Emmeke, Charlotte Gerritsen, and Koen Hindriks. "Automatic Recognition of Emotional Subgroups in Images." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/190.

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Both social group detection and group emotion recognition in images are growing fields of interest, but never before have they been combined. In this work we aim to detect emotional subgroups in images, which can be of great importance for crowd surveillance or event analysis. To this end, human annotators are instructed to label a set of 171 images, and their recognition strategies are analysed. Three main strategies for labeling images are identified, with each strategy assigning either 1) more weight to emotions (emotion-based fusion), 2) more weight to spatial structures (group-based fusio

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Tivatansakula, Somchanok, Gantaphon Chalumpornb, and Supadchaya Puangpontipb. "Healthcare System Focusing on Emotional Aspect Using Augmented Reality: Emotion Detection by Facial Expression." In Applied Human Factors and Ergonomics Conference. AHFE International, 2021. http://dx.doi.org/10.54941/ahfe100521.

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Current research includes many proposals of systems that provide assistances and services to people in the healthcare fields; however, these systems emphasize the support physical rather than emotional aspects. Emotional health is as important as physical health. Negative emotional health can lead to social or mental health problems. To cope with negative emotional health in daily life, we propose a healthcare system that focuses on emotional aspects. This system provides services to improve user emotion. To improve user emotion, we need to recognize users’ current emotional state. Therefore,

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Goodarzi, Farhad, Fakhrul Zaman Rokhani, M. Iqbal Saripan, and Mohammad Hamiruce Marhaban. "Mixed emotions in multi view face emotion recognition." In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2017. http://dx.doi.org/10.1109/icsipa.2017.8120643.

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Iskra, Andrej. "Analysis of emotion expression on frontal and profile facial images." In 11th International Symposium on Graphic Engineering and Design. University of Novi Sad, Faculty of technical sciences, Department of graphic engineering and design, 2022. http://dx.doi.org/10.24867/grid-2022-p22.

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Expressions of emotions are often found in facial images. In addition to the neutral facial expression, we know six basic expressions of emotion: joy, anger, sadness, fear, surprise, and disgust. The similarity of some emotion expressions sometimes leads to incorrect recognition or confusion of two emotions. In our study, we tried to find out how these substitutions manifest in the recognition of emotions on frontal and profile face images. The results of the substitutions in emotion recognition were presented with a substitution matrix. The second part of the study focused on confirming these

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Liu, Taiao, Yajun Du, and Qiaoyu Zhou. "Text Emotion Recognition Using GRU Neural Network with Attention Mechanism and Emoticon Emotions." In RICAI 2020: 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence. ACM, 2020. http://dx.doi.org/10.1145/3438872.3439094.

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Rapports d'organisations sur le sujet "Recognition of emotions"

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Ivanova, E. S. PERFORMANCE INDICATORS OF THE VOLUME Active vocabulary EMOTIONS AND ACCURACY Recognition of facial expressions STUDENTS. LJournal, 2017. http://dx.doi.org/10.18411/a-2017-002.

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Metaxas, D. Human Identification and Recognition of Emotional State from Visual Input. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada448621.

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Gou, Xinyun, Jiaxi Huang, Liuxue Guo, et al. The conscious recognition of emotion in depression disorder: A meta-analysis of neuroimaging studies. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.11.0057.

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Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

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Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmar

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Lin, XiaoGuang, XueLing Zhang, QinQin Liu, et al. Facial emotion recognition in adult with traumatic brain injury: a protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2020. http://dx.doi.org/10.37766/inplasy2020.5.0109.

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Sun, Yang, Jing Zhao, PanWen Zhao, et al. Social cognition in children and adolescents with epilepsy: a meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.3.0011.

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Review question / Objective: To our knowledge, no meta-analysis has summarized social cognitive performance in children and adolescents with epilepsy as independent groups. Therefore, we conducted this meta-analysis to examine differences between children and adolescents with epilepsy and HCs in terms of ToM and FER performance. Condition being studied: Epilepsy is characterized by chronic, unprovoked and recurrent seizures, is the most frequent neurological disease in childhood and usually occurs in early development. Worldwide, it is estimated that approximately 50 million people suffer from

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Clarke, Alison, Sherry Hutchinson, and Ellen Weiss. Psychosocial support for children. Population Council, 2005. http://dx.doi.org/10.31899/hiv14.1003.

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Masiye Camp in Matopos National Park, and Kids’ Clubs in downtown Bulawayo, Zimbabwe, are examples of a growing number of programs in Africa and elsewhere that focus on the psychological and social needs of AIDS-affected children. Given the traumatic effects of grief, loss, and other hardships faced by these children, there is increasing recognition of the importance of programs to help them strengthen their social and emotional support systems. This Horizons Report describes findings from operations research in Zimbabwe and Rwanda that examines the psychosocial well-being of orphans and vulne

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Comorbid anxiety disorder has a protective effect in conduct disorder. ACAMH, 2019. http://dx.doi.org/10.13056/acamh.10622.

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