• Anxiety and mood
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• v.12 no.1
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• pp.7-12
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• 2016

Objective : This study was conducted to evaluate the relationships between selective attention bias for fear stimuli and hallucination in patients with schizophrenia Method : A total of 66 patients with schizophrenia admitted to psychiatry clinics were included in the study. Selective attention bias was measured by the dot-probe task. Patient symptoms were measured using the Positive and Negative Symptom Scale, Psychotic Symptom Rating Scale (PSYRATS), Korean version of the Scale to Assess Unawareness of Mental Disorder, and Clinical Global Impression-Severity scale. Results : Selective attention bias was correlated with the hallucination subscale of PSYTATS (r=0.268, p=0.029). No correlation was found between selective attention bias and other clinical measures. There was no significant difference, but a statistical trend was found (p=0.092) in hallucination severities between the biased and non-biased groups. Conclusion : The results suggest that selective attention bias for fear stimuli is associated with auditory hallucination. This preliminary study suggests the possibility of correlation between auditory hallucination in the psychotic domain and anxiety of the affective component.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.369-381
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• 2018

Nowadays a personalization algorithm is gaining huge attention. It gives users selective information which is helpful and interesting in a deluge of information based on their past behavior on the internet. However there is also a fatal side effect that the user can only get restricted information on restricted topics selected by the algorithm. Basically, the personalization algorithm makes users have a narrower perspective and even stronger bias because users have less chances to get views of opponent. Eli Pariser called this problem the 'filter bubble' in his book. It is important to understand exactly what a filter bubble is to solve the problem. Therefore, this paper shows how much Google's personalized search algorithm influences search result through an experiment with deep neural networks acting like users. At the beginning of the experiment, two Google accounts are newly created, not to be influenced by the Google's personalized search algorithm. Then the two pure accounts get politically biased by two methods. We periodically calculate the numerical score depending on the character of links and it shows how biased the account is. In conclusion, this paper shows the formation process of filter bubble by a personalization algorithm through the experiment.

• Korean Journal of Biological Psychiatry
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• v.17 no.2
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• pp.79-85
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• 2010

Objectives : Trauma patients have attentional bias which enforces traumatic memories and causes cognitive errors. Understanding of such selective attention may explain many aspects of the posttraumatic stress disorder(PTSD) symptoms. Methods : We used the rapid serial visual presentation(RSVP) method to verify attentional blink in burn patients with PTSD. International affective picture system(IAPS) was used as stimuli and distracters. In the 'neutral test', patients have been presented series of pictures with human face picture as target stimuli. Each picture had 100ms interval. However the distance between target facial pictures was randomized and recognition of second facial picture accuracy was measured. In the 'stress test', the first target was stress picture which arouses patient emotions instead of the facial picture. Neutral and Stress tests were done with seven PTSD patients and 20 controls. In '85ms test' the interval was reduced to 85ms. The accuracy of recognition of second target facial picture was rated in all three tests. Eighty-five ms study was done with eighteen PTSD patients. Results : Attentional blinks were observed in 100-400ms of RSVP. PTSD patients showed increased recognition rate in the 'stress test' compared with the 'neutral test'. When presentation interval was decreased to 85 ms, PTSD patient showed decrease of attentional blink effect when target facial picture interval was 170ms. Conclusion : We found attentional blink effect could be affected by stress stimulus in burn patients. And attentional blink may be affected by stimulus interval and the character of stimulus. There may be some other specific mechanism related with selective attention in attentional blink especially with facial picture processing.