Explicit Categorization Ability Predictor for Biology Classification using fMRI
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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.
Abstract
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.
참고문헌 (34)
- Ansari, D., "Bridge over troubled waters: education and cognitive neuroscience", Trends in Cognitive Sciences, 10(4), pp.146~151 (2006)
- Ashby, F. G., "The cognitive neuroscience of implicit category learning. Attention and implicit learning", pp.109~141 (2003), John Benjamins Publishing Company
- Ashby, F. G., "Human category learning", Annual Review of Psychology, 56, pp.149~178 (2005)
- Ashby, F. G., "A neuropsychological theory of multiple systems in category learning", Psychological Review, 105, pp.442~481 (1998)
- Boccalettia, S., "Complex networks: structure and dynamics", Physic Reports, 424(4-5), pp.175~308 (2006)
- Borg, W. R., "Educational research: An introduction" (1989), Longman Inc
- 변정호, "Brain activation pattern and functional connectivity network during classification on the living organisms", 한국과학교육학회지, 29(7), pp.751~758 (2009), 한국과학교육학회
- Chen, C., "Sex determines the neurofunctional predictors of visual word learning", Neuropsychologia, 45, pp.741~747 (2007)
- Choi, Y. Y., "Multiple bases of human intelligence revealed by cortical thickness and neural activation", The Journal of Neuroscience, 28(41), pp.10323~10329 (2008)
- Eckert, M. A., "Manual and automated measures of superior temporal gyrus asymmetry : Concordant structural predictors of verbal ability in children", NeuroImage, 41, pp.813~822 (2008)
- Erickson, M. A., "Rules and exemplars in category learning", Journal of Experimental Psychology: General, 127, pp.107~140 (1998)
- Evans, A. C., "3D Statistical neuroanatomical model from 305 MRI volumes", IEEE Conference Record, Nuclear Science Symposium and Medical Imaging Conference (San Francisco), pp.1813~1817 (1993)
- Filoteo, J. V., "Cortical and subcortical brain regions involved in rule-based category learning", Neuroreport, 16(2), pp.111~115 (2005)
- Friston, K. J., "Time-dependent changes in effective connectivity measured with PET", Human Brain Mapping, 1, pp.69~79 (1993)
- Friston, K. J., "How many subjects constitute a study?", NeuroImage, 10, pp.1~5 (1999)
- Genovese, C. R., "Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate", NeuroImage, 15, pp.870~878 (2002)
- Harnad, S., "To cognize is to categorize: Cognition is categorization, In Handbook on Categorization" (2005), Elservier
- Honey, J. N., "The importance of taxonomy in biological education at advanced level", Journal of Biological Education, 20, pp.103~111 (1986)
- Huang-Pollock, C. L., "Development of implicit and explicit category learning", Journal of Experimental Child Psychology, 109, pp.321~335 (2011)
- Jacob, E. K., "Classification and Categorization : A difference that makes a difference", Library Trends, 52(3), pp.515~540 (2004)
- Jiang, X., "Categorization training results in shape and category-selective human neural plasticity", Neuron, 53, pp.891~903 (2007)
- Koshino, H., "Functional connectivity in an fMRI working memory task in high-functioning autism", Neuroimage, 24, pp.810~821 (2005)
- 권용주, "대학생들의 귀납적 탐구에서 나타난 과학적 사고의 유형과 과정", 한국과학교육학회지, 23(3), pp.286~298 (2003), 한국과학교육학회
- Kwon, Y. J., "Development of the classification ability quotient equation through the analysis of science teachers' classification knowledge generated in the pollen classification task", The Secondary Education Research, 55(3), pp.21~43 (2007)
- Loring, D. W., "Now you see it, now you don' t : statistical and methodological considerations in fMRI", Epilepsy & Behavior, 3, pp.539~547 (2002)
- Maddox, W. T., , Neuropsychologia, 48, pp.2998~3008 (2010)
- Margulis, L., "How many kingdoms? Current views of biological classification", The American Biology Teacher, 43, pp.482~489 (1981)
- Mayr, E., "This is biology: the science of the living world" (1997), The belknap press of Harvard University press
- Pickering, A. D., "New approaches to the study of amnesic patients: What can a neurofunctional philosophy and neural network methods offer?", Memory, 5, pp.255~300 (1997)
- Schmithorst, V. J., "Functional MRI evidence for disparate developmental processes underlying intelligence in boys and girls", NeuroImage, 31, pp.1366~1379 (2006)
- Schnyer, D. M., "Prefrontal contributions to rule-based and information-integration category learning", Neuropsychologia, 47(13), pp.2995~3006 (2009)
- Seger, C. A., "How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback", Neuroscience and Biobehavioral Review, 32, pp.265~278 (2008)
- Talairach, J., "Co-Planner stereotaxic atlas of the human brain" (1988), Thieme Medical publisher, Inc
- Worsley, K. J., "Analysis of fMRI time-series revisited-again", NeuroImage, 2, pp.173~181 (1995)