한국과학교육학회지 (Journal of The Korean Association For Science Education)

  • 제32권3호
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  • Pages.524-531
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  • 2012
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  • 1226-5187(pISSN)
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  • 2288-8489(eISSN)
한국과학교육학회 (The Korean Association for Science Education)
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Explicit Categorization Ability Predictor for Biology Classification using fMRI

  • 투고 : 2012.03.30
  • 심사 : 2012.04.16
  • 발행 : 2012.06.30
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초록

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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