• 정신신체의학
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• 제9권2호
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• pp.143-152
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• 2001

연구목적: 정상인에서 표정 자극에 의해 유발된 P3가 긍정적, 부정적 정서 범주에 따라 서로 다른지 여부를 알아보고자 하였다. 대상 및 방법: 정상인 12명을 대상으로 표정 사진에 의해 유발된 사건 관련 전위인 P3를 측정하였다. 모든 연구 대상에게 고빈도로 제시되는 비표적 바둑판 모양의 체크보드 자극으로부터 저빈도로 제시되는 표적 정서 자극에 대해 느끼고, 반응하도록 지시하였다. 결과: 정상인에서 부정적 정서 자극에 의해 유발된 P3의 진폭이 긍정적 자극에 의한 경우 보다 유의하게 더 컸다. 결론: 표정 자극에 의해 유발된 P3는 긍정적, 부정적 정서 처리의 정신생리적 지표로서 활용될 가능성이 시사된다.

• 대한인간공학회지
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• 제31권3호
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• pp.427-435
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• 2012

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.

• 감성과학
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• 제11권1호
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• pp.11-21
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• 2008

본 연구에서는 얼굴 표정 정서를 인식하는데 있어서 우울 집단과 통제 집단간에 차이가 있는지를 알아보고자 하였다. 연구 대상은 305명의 대학생에게 BDI-II를 실시하여 14점(상위 20%) 이상을 얻은 학생을 우울 집단으로, 5점 이하(하위 20%)를 얻은 학생을 통제 집단으로 선정하였다. 최종적으로 우울 집단 20명, 통제 집단 20명이 분석에 포함되었으며, 이들에게 기쁨, 슬픔, 화남, 두려움의 각 얼굴 표정이 중립에서부터 시작되어 점점 정서 강도가 커지면서 가장 강한 강도의 표정 사진에 이르도록 변하는 자극을 제시하였다. 그 결과, 집단과 정서(특히 기쁨-슬픔 조건)간의 유의한 상호작용 효과가 관찰되었고, 이러한 결과는 우울감이 얼굴 표정과 같은 정서적 정보처리에 영향을 미친다는 것을 의미한다. 우울한 개인의 이러한 정서 일치적 정보 처리 경향이 가지는 함의에 대하여 논의하였다.

• 감성과학
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• 제10권1호
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• pp.127-133
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• 2007

본 연구에서는 얼굴표정을 통하여 다른 사람의 정서 상태를 판단하는 능력이 연령(3세, 5세, 대학생), 성별(남, 여), 얼굴제시영역(얼굴전체, 눈), 정서의 종류(기본정서, 복합정서)에 따라 어떻게 다른지 알아보고자 하였다. 본 연구에서는 얼굴표정과 정서어휘 간의 연결이 비교적 분명하게 나타나는 32개의 정서 상태를 자극으로 사용하였으며, 표정사진은 32개의 정서 상태에 해당하는 얼굴표정을 배우에게 연기하도록 하여 사용하였다. 과제는 각 실험참가자에게 정서유발 상황에 대한 이야기를 들려주고 이야기 속의 주인공이 어떤 얼굴표정을 할 것인지를 판단하게 한 후 네 개의 얼굴표정 중에 적절한 것을 선택하도록 한 것이었다. 그 결과 연령이 증가함에 따라 얼굴표정을 판단하는 능력이 증가하였으며, 눈만 제시한 경우보다는 얼굴전체를 제시하였을 때, 복합정서보다는 기본정서에서 더 좋은 수행을 보였다. 또한 여자는 제시영역에 따른 수행의 차이가 없는 것에 반해, 남자는 눈 조건에 비해 얼굴조건의 경우에 더 좋은 수행을 보였다. 본 연구의 결과는 연령, 얼굴제시영역, 정서의 종류가 얼굴표정을 통해 타인의 정서를 판단하는데 영향을 줌을 시사한다.

• 생물정신의학
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• 제18권4호
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• pp.239-244
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• 2011

Objectives Schizophrenic patients have been shown to be impaired in both emotional self-awareness and recognition of others' facial emotions. Alexithymia refers to the deficits in emotional self-awareness. The relationship between alexithymia and recognition of others' facial emotions needs to be explored to better understand the characteristics of emotional deficits in schizophrenic patients. Methods Thirty control subjects and 31 schizophrenic patients completed the Toronto Alexithymia Scale-20-Korean version (TAS-20K) and facial emotion recognition task. The stimuli in facial emotion recognition task consist of 6 emotions (happiness, sadness, anger, fear, disgust, and neutral). Recognition accuracy was calculated within each emotion category. Correlations between TAS-20K and recognition accuracy were analyzed. Results The schizophrenic patients showed higher TAS-20K scores and lower recognition accuracy compared with the control subjects. The schizophrenic patients did not demonstrate any significant correlations between TAS-20K and recognition accuracy, unlike the control subjects. Conclusions The data suggest that, although schizophrenia may impair both emotional self-awareness and recognition of others' facial emotions, the degrees of deficit can be different between emotional self-awareness and recognition of others' facial emotions. This indicates that the emotional deficits in schizophrenia may assume more complex features.

• 한국융합학회논문지
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• 제9권3호
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• pp.79-90
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• 2018

본 연구의 목적은 긍정 얼굴 자극에 대한 주의의 과정에서 정서 맥락적 배경의 유무에 따라 사회불안성향자들과 정상인 간 차이가 나타나는지 살펴보고자 하는 것이다. 이를 위해 무맥락적 배경에서 긍정 얼굴 자극에 대한 주의 과정을 확인하였고, 다음으로 정서 맥락적 배경이 존재하는 상황에서 동일한 주의 과정을 탐색하였다. D시의 800명의 대학생에게 SADS와 CES-D를 실시하고, 사회불안집단(SA, n=24)과 정상 대조군(NC, n=24)을 선별하였다. 주의 개입과 주의 이탈 두 요소를 측정하기 위해 최초 응시 방향, 최초 응시 시간을 안구 운동 추적을 통해 측정하였다. 그 결과, 사회불안집단의 경우 배경이 없는 상태에서 통제집단에 비해 긍정 얼굴 자극으로부터 더 빠른 주의 이탈이 관찰되었다. 그러나 긍정 얼굴 자극이 긍정 배경 자극 안에 제시되면 사회불안집단과 통제집단 간의 차이가 관찰되지 않았다. 이 결과는 긍정 배경이 사회불안장애성향자의 정서 처리에 영향을 미친다는 것을 시사한다.

• 감성과학
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• 제25권4호
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• pp.107-118
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• 2022

Human can rapidly detect and deal with dangerous elements in their environment, and they generally manifest as attentional bias toward threat. Past studies have reported that this attentional bias is affected by anxiety level. Other studies, however, have argued that children and adolescents show attentional bias to threatening stimuli, regardless of their anxiety levels. Few studies directly have compared the two age groups in terms of attentional bias to threat, and furthermore, most previous studies have focused on attentional capture and the early stages of attention, without investigating further attentional holding by the stimuli. In this study, we investigated both attentional bias patterns (attentional capture and holding) with respect to negative emotional stimulus in neurotypical adults and adolescents. The effects of anxiety level on attentional bias were also examined. The results obtained for adult participants showed that abrupt onset of a distractor delayed attentional capture to the target, regardless of distractor type (angry or neutral faces), while it had no effect on attention holding. In adolescents, on the other hand, only the angry face distractor resulted in longer reaction time for detecting a target. Regarding anxiety, state anxiety revealed a significant positive correlation with attentional capture to a face distractor in adult participants but not in adolescents. Overall, this is the first study to investigate developmental tendencies of attentional bias to negative facial emotion in both adults and adolescents, providing novel evidence on attentional bias to threats at different ages. Our results can be applied to understanding the attentional mechanisms in people with emotion-related developmental disorders, as well as typical development.

• 한국의류산업학회지
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• 제16권6호
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• pp.995-1000
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• 2014

This study, using the free language association method, examined the characteristics of emotional words of respondents who were exposed to facial photos of women in 60s, and favorableness and favorable styles of them. To analyze mood characteristics on the faces, they were divided into positive mood words and negative mood words. Following previous researches, they were divided into introversion, extraversion, and ambiversion. It was found that the proportion of positive emotional words respondents used was 37%, and that of negative ones was 63%, demonstrating that respondents are more likely than not to get the negative impressions from the faces of their contemporaries. The characteristics of the words consists of 38% introversion, 47% extraversion, and 14% ambiversion. And, respondents used the words like 'beautiful' and 'good-looking' to the stimuli to which they felt favorable, and 'ill-tempered' and 'stubborn' to the stimuli to which they felt unfavorable. Third, the most favorable style to both male and female respondents in 60s were sentimental and good-mannered. They generally favor women who are soft and caring, and dislike talkative, snobbish, and thick make-up women. The analysis results in this paper may help image making and personal relations. Further study needs to expand the survey area to ensure more significant influence on the social life and interpersonal relationship of senior citizens.

• Journal of Information Technology Applications and Management
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• 제21권4호
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• pp.203-223
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• 2014

This study proposes a novel context-aware recommender system, which is designed to recommend the items according to the customer's responses to the previously recommended item. In specific, our proposed system predicts the user's emotional state from his or her responses (such as facial expressions and movements) to the previous recommended item, and then it recommends the items that are similar to the previous one when his or her emotional state is estimated as positive. If the customer's emotional state on the previously recommended item is regarded as negative, the system recommends the items that have characteristics opposite to the previous item. Our proposed system consists of two sub modules-(1) emotion prediction module, and (2) responsive recommendation module. Emotion prediction module contains the emotion prediction model that predicts a customer's arousal level-a physiological and psychological state of being awake or reactive to stimuli-using the customer's reaction data including facial expressions and body movements, which can be measured using Microsoft's Kinect Sensor. Responsive recommendation module generates a recommendation list by using the results from the first module-emotion prediction module. If a customer shows a high level of arousal on the previously recommended item, the module recommends the items that are most similar to the previous item. Otherwise, it recommends the items that are most dissimilar to the previous one. In order to validate the performance and usefulness of the proposed recommender system, we conducted empirical validation. In total, 30 undergraduate students participated in the experiment. We used 100 trailers of Korean movies that had been released from 2009 to 2012 as the items for recommendation. For the experiment, we manually constructed Korean movie trailer DB which contains the fields such as release date, genre, director, writer, and actors. In order to check if the recommendation using customers' responses outperforms the recommendation using their demographic information, we compared them. The performance of the recommendation was measured using two metrics-satisfaction and arousal levels. Experimental results showed that the recommendation using customers' responses (i.e. our proposed system) outperformed the recommendation using their demographic information with statistical significance.

• 대한인간공학회지
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• 제31권2호
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• pp.271-279
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• 2012

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.