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Objective: The aim of this study is to identify the relationship between personality and psychological responses induced by emotional stimuli (happiness, sadness, anger, boring and stress) for children. Background: Many researches are interested in assertion that there is close correlation between personality and emotion. The relationship between personality and emotion needs to be studied in view of the extended integration, not in view of respective property, because personality is deeply ingrained, and the relatively enduring patterns of thought, feeling and behavior and emotion can take advantage of individual differences in sensitivities to situational cues and predispositions to emotional state. In particular, studies on the personality and emotion for children are necessary in that childhood is an important period for formation of their personality and emotion expression and regulation. Method: Prior to the experiment, we made parents of 94 children rate personalities of their children, based on Korean Personality Inventory for Children (K-PIC). Results of 64 children without missing answers to all questions were analyzed. 64 children were exposed to five emotional stimuli and were asked to report the classification and intensity of their experienced emotion. Results: Children were classified into two groups of the lower 25% and higher 25% scores in twenty sub-scales of K-PIC, and psychological responses to five emotional stimuli between two groups were compared. Accuracy of emotion experienced by emotional stimuli showed a significant difference between the two groups, the lower and higher scores in Hyperactivity and Adjustment. Also, there was a significant difference in the intensity of experienced emotions between the two groups in Intellectual Screening and Psychosis. Conclusion: Our result has shown that hyperactivity, adjustment, intellectual screening and psychosis influence the accuracy and intensity of emotional responses. Application: This study can offer a guideline to overcome methodological limitation of emotion studies for children and help researcher basically understand and recognize human emotion in HCI.

Objective: In this study, we used resting-state fMRI data to map differences in functional connectivity between a comprehensive set of 8 distinct cortical and subcortical brain regions in healthy controls and Internet addicts. We also investigated the relationship between resting state connectivity strength and the level of psychopathology (ex. score of internet addiction scale and score of Barratt impulsiveness scale). Background: There is a lot of evidence of relationship between Internet addiction and impaired inhibitory control. Clinical evidence suggests that Internet addicts have a high level of impulsivity as measured by behavioral task of response inhibition and a self report questionnaire. Method: 15 Internet addicts and 15 demographically similar non-addicts participated in the current resting-state fMRI experiment. For the connectivity analysis, regions of interests (ROIs) were defined based on the previous studies of addictions. Functional connectivity assessment for each subject was obtained by correlating time-series across the ROIs, resulting in 8x8 matrixs for each subject. Within-group, functional connectivity patterns were observed by entering the z maps of the ROIs of each subject into second-level one sample t test. Two sample t test was also performed to examine between group differences. Results: Between group, the analysis revealed that the connectivity in between the orbito frontal cortex and inferior parietal cortex, between orbito frontal cortex and putamen, between the orbito frontal cortex and anterior cingulate cortex, between the insula and anterior cingulate cortex, and between amydgala and insula was significantly stronger in control group than in the Internet addicts, while the connectivity in between the orbito frontal cortex and insula showed stronger negative correlation in the Internet addicts relative to control group (p Conclusion: This study found that Internet addicts had declined connectivity strength in the orbitofrontal cortex (OFC) and other regions (e.g., ACC, IPC, and insula) during resting-state. It may reflect deficits in the OFC function to process information from different area in the corticostriatal reward network. Application: The results might help to develop theoretical modeling of Internet addiction for Internet addiction discrimination.

ccThis is an open-access article distributedunder the terms of the Creative CommonsAttribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), whichpermits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objective: The aim of this study is to investigate facial temperature changes induced by facial expression and emotional state in order to recognize a persons emotion using facial thermal images. Background: Facial thermal images have two advantages compared to visual images. Firstly, facial temperature measured by thermal camera does not depend on skin color, darkness, and lighting condition. Secondly, facial thermal images are changed not only by facial expression but also emotional state. To our knowledge, there is no study to concurrently investigate these two sources of facial temperature changes. Method: 231 students participated in the experiment. Four kinds of stimuli inducing anger, fear, boredom, and neutral were presented to participants and the facial temperatures were measured by an infrared camera. Each stimulus consisted of baseline and emotion period. Baseline period lasted during 1min and emotion period 1~3min. In the data analysis, the temperature differences between the baseline and emotion state were analyzed. Eyes, mouth, and glabella were selected for facial expression features, and forehead, nose, cheeks were selected for emotional state features. Results: The temperatures of eyes, mouth, glanella, forehead, and nose area were significantly decreased during the emotional experience and the changes were significantly different by the kind of emotion. The result of linear discriminant analysis for emotion recognition showed that the correct classification percentage in four emotions was 62.7% when using both facial expression features and emotional state features. The accuracy was slightly but significantly decreased at 56.7% when using only facial expression features, and the accuracy was 40.2% when using only emotional state features. Conclusion: Facial expression features are essential in emotion recognition, but emotion state features are also important to classify the emotion. Application: The results of this study can be applied to human-computer interaction system in the work places or the automobiles.

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

Objective: This study is to review three representative psychological perspectives that explain scientific construct of emotion, that are the discrete emotion model, appraisal model, and dimensional model. Background: To develop emotion sensitive interface is the fusion area of emotion and scientific technology, it is necessary to have a balanced mixture of both the scientific theory of emotion and practical engineering technology. Extensional theories of the emotional structure can provide engineers with relevant knowledge in functional application of the systems. Method: To achieve this purpose, firstly, literature review on the basic emotion model and the circuit model of discrete emotion model as well as representative theories was done. Secondly, review on the classical and modern theories of the appraisal model emphasizing cognitive appraisal in emotion provoking events was conducted. Lastly, a review on dimensional theories describing emotion by dimensions and representative theories was conducted. Results: The paper compared the three models based on the prime points of the each model. In addition, this paper also made a comment on a need for a comprehensive model an alternative to each model, which is componential model by Scherer(2001) describing numerous emotional aspects. Conclusion: However, this review suggests a need for an evolved comprehensive model taking consideration of social context effect and discrete neural circuit while pinpointing the limitation of componential model. Application: Insight obtained by extensive scientific research in human emotion can be valuable in development of emotion sensitive interface and emotion recognition technology.