지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2012.1
- 수록면
- 524 - 531 (8page)
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초록· 키워드
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Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI.
Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed.
Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.
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참고문헌 (27)
참고문헌 신청
Ansari, D. / 2006 / Bridge over troubled waters: education and cognitive neuroscience / Trends in Cognitive Sciences 10 (4) : 146 ~ 151
Ashby, F. G. / 1998 / A neuropsychological theory of multiple systems in category learning / Psychological Review 105 : 442 ~ 481
Ashby, F. G. / 2003 / The cognitive neuroscience of implicit category learning. Attention and implicit learning / John Benjamins Publishing Company : 109 ~ 141
Ashby, F. G. / 2005 / Human category learning / Annual Review of Psychology 56 : 149 ~ 178
Boccalettia, S. / 2006 / Complex networks: structure and dynamics / Physic Reports 424 (4-5) : 175 ~ 308
Ashby, F. G. / 1998 / A neuropsychological theory of multiple systems in category learning / Psychological Review 105 : 442 ~ 481
Ashby, F. G. / 2003 / The cognitive neuroscience of implicit category learning. Attention and implicit learning / John Benjamins Publishing Company : 109 ~ 141
Ashby, F. G. / 2005 / Human category learning / Annual Review of Psychology 56 : 149 ~ 178
Boccalettia, S. / 2006 / Complex networks: structure and dynamics / Physic Reports 424 (4-5) : 175 ~ 308
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