• Title/Summary/Keyword: mechanistic reasoning

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Development of a Mechanistic Reasoning Model Based on Biologist's Inquiries (생물학자의 탐구에 기반한 메커니즘 추론 모델 개발)

  • Jeong, Sunhee;Yang, Ilho
    • Journal of The Korean Association For Science Education
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    • v.38 no.5
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    • pp.599-610
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    • 2018
  • The purpose of this study is to analyze mechanistic reasoning in Fabre's inquires and to develop mechanistic reasoning model. To analyze the order of the process elements in mechanistic reasoning, 30 chapters were selected in book. Inquiries were analyzed through a framework which is based on Russ et al. (2008). The nine process elements of mechanistic reasoning that was presented in Fabre's inquires were as follows: Describing the Target Phenomenon, Identifying prior Knowledge, Identifying Properties of Objects, Identifying Setup Conditions, Identifying Activities, Conjecturing Entities, Identifying Properties of Entities, Identifying Entities, and Organization of Entities. The order of process elements of mechanistic reasoning was affected by inquiry's subject, types of question, prior knowledge and situation. Three mechanistic reasoning models based on the process elements of mechanistic reasoning were developed: Mechanistic reasoning model for Identifying Entities(MIE), Mechanistic reasoning model for Identifying Activities(MIA), and Mechanistic reasoning model for Identifying Properties of entities (MIP). Science teacher can help students to use the questions of not only "why" but also "How", "If", "What", when students identify entities or generate hypotheses. Also science teacher should be required to understand mechanistic reasoning to give students opportunities to generate diverse hypotheses. If students can't conjecture entities easily, MIA and MIP would be helpful for students.

Functional Neuroimaging of General Fluid Intelligencein Prodigies

  • Lee, Kun-Ho
    • Proceedings of the Korean Society for the Gifted Conference
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    • 2003.05a
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    • pp.137-138
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    • 2003
  • Understanding how and why people differ is a fundamental, if distant, goal of research efforts to bridge psychological and biological levels of analysis. General fluid intelligence (gF) is a major dimension of individual differences and refers to reasoning and novel problemsolving ability. A conceptual integration of evidence from cognitive (behavioral) and anatomical studies suggeststhat gF should covary with both task performance and neural activity in specific brain systems when specific cognitive demands are present, with the neural activity mediating the relation between gF and performance. Direct investigation of this possibility will be a critical step toward a mechanistic model of human intelligence. In turn, a mechanistic model might suggest ways to enhance gF through targeted behavioral or neurobiological intervent ions, We formed two different groups as subjects based on their scholarly attainments. Each group consists of 20 volunteers(aged 16-17 years, right-handed males) from the National Gifted School and a local high school respectively. To test whether individual differences in general intelligence are mediated at a neural level, we first assessed intellectual characteristics in 40 subjects using standard intelligence tests (Raven's Advanced Progressive Matrices, Wechsler Adult Intelligence Scale, Torrance Tests of Creative Thinking) administered outside of the MR scanner. We then used functional magnetic resonance imaging (fMRl) to measure task-related brain activity as participants performed three different kinds of computerized reasoning tasks that were intended to activate the relevant neural systems. To examine the difference of neural activity according to discrepancy in general intelligence, we compared the brain activity of both extreme groups (each, n=10) of the participants based on the standard intelligence test scores. In contrast to the common expectation, there was no significant difference of brain region involved in high-g tasks between both groups. Random effect analysis exhibited that lateral prefrontal, anterior cingulate and parietal cortex are associated with gF. Despite very different task contents in the three high-g-low-g contrasts, recruitment of multiple regions is markedly similar in each case, However, on the task with high 9F correlations, the Prodigy group, (intelligence rank: >99%) showed higher task-related neural activity in several brain regions. These results suggest that the relationship between gF and brain activity should be stronger under high-g conditions than low-g conditions.

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