• Korean Journal of Cognitive Science
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• v.25 no.3
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• pp.233-257
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• 2014

Category-based induction is one of major inferential reasoning methods used by humans. This research tested the effect of perceived within-category variability on the inductive generalization. Experiment 1 manipulated variability by directly presenting category exemplars. After displaying low variable (low variability condition) or highly variable exemplars (high variability condition) depending on condition, participants performed inductive generalization task about a category in question. The results showed that participants have greater confidence in generalization when category variability was low than when it was high. Rather than directly presenting category exemplars in Experiment 2, participants performed induction task after they formed category variability impression by categorization task of identifying category exemplars. Experiment 2 also found the tendency that participants have greater inductive confidence when category variability was low. The variability effect discovered in this research is distinct from the diversity effect in previous research and the category-based induction model proposed by Osherson et al. (1990) cannot fully account for the variability effect in this research. Test of variability effect in category-based induction is discussed in the general discussion section.

• Journal of The Korean Association For Science Education
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• v.38 no.6
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• pp.793-812
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• 2018

This study, from a critical view on knowledge-centered science education, aims to explore the wisdom that can be acquired from science. In other words, to find the categories and examples of "Wisdom of Science(WOS)" that can be shared in science classroom is the purpose of this study. For the data collection, twelve hours of physics classes of three high schools were observed, together with teacher interviews and student interviews. Collected data were analyzed qualitatively based on the operational definition of WOS. In this study, WOS was defined in a limited sense to mean 'wise action such as behaviors, attitudes, methods, and thoughts that can be found in the process of formation and application of scientific knowledge'. The results of this study, i.e. three categories and six examples of WOS, can be summarized as follows. First category of WOS is 'wisdom as a scientific attitude'. The examples of this category are 'rational suspicion and open-minded attitude', and 'effort to find the best way in given situation'. Second category of WOS is 'wisdom as a method for problem solving'. The examples of this category are 'thinking with changing the conditions', and 'communication using the language of science'. Third category of WOS is 'wisdom as a reflection about science and human'. The examples of this category are 'understanding of the relationship between science and society', and 'perceiving the relationship between science and my life'. In conclusion, "Wisdom-oriented Science Education" as an alternative goal of future science education is suggested with its meanings and implications.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.329-347
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• 2017

Early research into category-based feature inference reported various phenomena in human thinking including typicality, diversity, similarity effects, etc. Later research discovered that participants' prior knowledge has an extensive influence on these sorts of reasoning. The current research tested the effects of causal knowledge on feature inference and conducted modeling on the results. Participants performed feature inference for categories consisted of four features where the features were connected either in common cause or common effect structure. The results showed typicality effects along with violations of causal Markov condition in common cause structure and causal discounting in common effect structure. To model the results, it was assumed that participants perform feature inference based on the difference between the probabilities of an exemplar with the target feature and an exemplar without the target feature (that is, $p(E_{F(X)}{\mid}Cat)-p(E_{F({\sim}X)}{\mid}Cat)$). Exemplar probabilities were computed based on causal model theory (Rehder, 2003) and applied to inference for target features. The results showed that the model predicts not only typicality effects but also violations of causal Markov condition and causal discounting observed in participants' data.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.273-291
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• 2019

This study aims to extend fundamental categories from Clause 11, "facet analysis" in International Standards for thesaurus(ISO 25964-1) by analyzing fundamental categories of Clause 11 and concept and their scope in a thesaurus of Clause 5. For to do this, the fundamental categories were established by adjusting partially and adding mental entities explicitly referencing the highest concepts(YAMATO which is the upper ontology of Mizoguchi, and ISO 2788) and existing fundamental categories(PMEST, FRBR group 3 entities, 13 categories in CRG). Also, established fundamental categories were reorganized and structured based on concreteness/abstraction of PMEST in Ranganathan and independence/dependence of YAMATO in Mizoguchi. And the upper categories were divided into independent and dependent entities. Under these entities 28 criteria are included in the independent ones and 2 criteria in the dependent ones. In the further study, the result of this study can be expected to reuse and refer as controlled vocabulary in the field like classification, taxonomies and thesauri where expected to utilize fundamental categories and as the high-level concept when constructing an ontology for information retrieval.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.683-692
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• 2010

This short article is concerned with a categorical time series obtained after clipping a heteroscedastic GARCH process. Estimation methods are discussed for the model parameters appearing both in the original process and in the resulting binary time series from a clipping (cf. Zhen and Basawa, 2009). Assuming AR-GARCH model for heteroscedastic time series, three data sets from Korean stock market are analyzed and illustrated with applications to calculating certain probabilities associated with the AR-GARCH process.

• Korean Journal of Cognitive Science
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• v.32 no.2
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• pp.55-73
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• 2021

Psychological essentialism states that people believe some categories to have hidden and defining essential features which cause other features of the category (Gelman, 2003; Hirschfeld, 1996; Medin & Ortony, 1989). Essentialist belief on categories questions the Roschian argument (Rosch, 1973, 1978) that categories merely consist of clusters of correlated features. Unlike family resemblance categories, essentialized categories are likely to have clear between-category boundaries and high within-category coherence (Gelman, 2003; Prentice & Miller, 2007). Two experiments were conducted to test the effects of essentialist belief on category representation (i.e., between-category boundary, within-category coherence). Participants learned family resemblance and essentialized categories in their assigned conditions and then performed categorization task (Expt. 1) and frequency estimation task of category exemplars (Expt. 2). The results showed, in essentialized categories, both boundary intensification and greater category coherence. Theses results are likely to have arisen due to increased cue and category validity in essentialized categories and suggest that essentialist belief influences macroscopic representation of category structure.

• Journal of Korean Library and Information Science Society
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• v.50 no.4
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• pp.381-403
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• 2019

Information on newspaper articles were categorized into different topics, and each categories within different topics were developed into a faceted taxonomies model which was combined with fundamental facets. After suggesting the plan to construct such a model, the research of actual faceted taxonomies were conducted. Faceted taxonomies divide information on news articles into different topics(such as politics, economies and others) and combine fundamental facets with categories(for example, politics can be sub-classified into general politics, administration, legal system, and others) and sub-categories. Each sub-categories can be further subdivided. In taxanomies, categories can have hierarchical relationships. Categories-Facets, for example, can be utilized to combine "arts" with "people", "action", "event", "time", "place" and others. And Sub-category of the classification of "arts" such as "art," "music," "dance" form hierarchical relationships with "arts" and, in turn, can be used for browsing and further inferences. Furthermore, combining category and facets results in hierarchical structure in order of fundamental facets. As for the pilot vocabulary construction, faceted taxonomies of 145 words from news paper articles on the topic of "arts" were constructed using all construction elements covered in this study.

• Spatial Information Research
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• v.19 no.4
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• pp.81-89
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• 2011

This paper presents a multivariate kriging algorithm that integrates categorical data as secondary data for spatial interpolation of sparsely sampled ground survey data. Instead of using constant mean values in each attribute of categorical data, disaggregated local mean values at target grid points are first estimated by area-to-point kriging and then are used as local mean values in simple kriging with local means. This algorithm is illustrated through a case study of spatial interpolation of a geochemical copper element with geological map data. Cross validation results indicates that the presented algorithm leads to significant respective improvement of 15% and 25% in prediction capability, compared with univariate ordinary kriging and conventional simple kriging with constant mean values. It is expected that the multivariate kriging algorithm applied in this study would be effectively applied for spatial interpolation with categorical data.

• Science of Emotion and Sensibility
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• v.24 no.1
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• pp.59-72
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• 2021

Concepts and categories offer the basis for inference pertaining to unobserved features. Prior research on category-based induction that used blank properties has suggested that similarity between categories and features explains feature inference (Rips, 1975; Osherson et al., 1990). However, it was shown by later research that prior knowledge had a large influence on category-based inference and cases were reported where similarity effects completely disappeared. Thus, this study tested category-based feature inference when features are connected in a causal chain and proposed a feature inference model that predicts participants' inference ratings. Each participant learned a category with four features connected in a causal chain and then performed feature inference tasks for an unobserved feature in various exemplars of the category. The results revealed nonindependence, that is, the features not only linked directly to the target feature but also to those screened-off by other feature nodes and affected feature inference (a violation of the causal Markov condition). Feature inference model of causal model theory (Sloman, 2005) explained nonindependence by predicting the effects of directly linked features and indirectly related features. Indirect features equally affected participants' inference regardless of causal distance, and the model predicted smaller effects regarding causally distant features.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.497-500
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• 2008

3차원 오브젝트의 위치 및 정보 획득을 위한 레이저레이더 시뮬레이션의 성능을 향상시키기 위해서는 시뮬레이션의 결과로 획득되는 공간의 범위와 해당 사물의 정보를 정확하고 빠르게 획득해야 한다. 본 연구에서는 이러한 레이더 시뮬레이션의 성능을 향상시키기 위해서 3차원 공간 데이터를 공간DB에 저장하고, 질의를 수행하여 해당 3차원 오브젝트의 정보를 효과적으로 추출해내는 방법론을 제시하였다. 이를 위해 본 연구에서는 시뮬레이션에서 사용되는 3차원 지형지물(지형, 건물 사물 등) 모델의 정보를 데이터모델링을 통해 토폴로지 형태를 갖도록 하였으며, 이를 공간DB에 저장하고, 레이저 신호와의 연산 쿼리를 시행하는 과정을 예시하였다. 이러한 과정을 구현하기 위하여 OGC 기반의 공간 데이터 타입, 함수, 인덱스들을 제공하는 PostgreSQL과 PostGIS를 사용하였다. 지형, 건물, 탱크 등 이렇게 세 가지의 범주의 사물로 나누어 각각을 공간DB로 구현, 쿼리를 실시하였다. 지형정보는 TIN을 사용하였고, 건물의 좌표 값들은 도화원도에서 추출하였으며, 탱크와 같은 사물은 VRML 모델의 좌표 값을 사용하였다.