• Proceedings of the Korean Society for Information Management Conference
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• 2014.08a
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• pp.95-98
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• 2014

본 연구는 지역명 범주에 속하는 용어들의 개념적 특성을 분석하고, 이를 토대로 다른 범주와의 관련도를 파악하여 지역명 범주 용어들을 중심으로 관계 속성들 사이의 논리적 연관성을 부여할 수 있는 모형 도출에 기반이 되는 기초 연구이다. 지역명 범주 용어 중 국가명에 한정하여 분석한 결과, 국가명 개념 속성 중심으로는 계층 구조 관계의 지역명 범주 용어들끼리 연관이 높으며, 전체 범주 용어들의 개념 속성 중심으로는 지역명 범주 용어가 지리적 위치로서의 의미로 주로 쓰이나, 행위의 주체 또는 객체의 의미나 시대의 개념으로도 많이 활용됨을 알 수 있었다. 국가명이 참조되는 개념 속성과 연관되어 활용되는 관계 속성의 경우의 일부는 참조하는 주요 개념 범주와 연관 관계를 토대로 논리적 의미 관계를 생각해볼 수 있는 것으로 나타났다.

• 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.

• Korean Journal of Cognitive Science
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• v.12 no.4
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• pp.29-39
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• 2001

Kaplan(2000) reported that instances were categorized more accurate in the aligned condition than in the non-aligned condition irrespective of similarity between instances[16]. This study investigated wether Kaplan(2000)\\`s results could be explained by stimulus types she used and alignment effects in categorization were due to selective attention to aligned attributes. In Experiment 1. I examined whether attribute alignment produced significant effects on similarity and categorization and aligned attributes were recalled more than non-aligned ones. Results showed that instances were rated more similar and categories were learned more rapidly in the aligned condition than in the non-aligned condition. It can be explained that categories are learned rapidly in the aligned condition because attribute alignment increases within-category similarity. But. the result that aligned attributes were recalled more than non-aliened ones in the attribute recall test implies that alignment effects in categorization can be independent of similarity between instances partially. In Experiment 2. I used equal numbed of attributes defining two categories and instructed subjects to pay their attention to categorization-relevant dimensions only. Results showed that dimension instruction facilitated category learning in the non-aligned condition only but categories were learned more rapidly in the aligned condition than in the non-aliened condition irrespective of instruction types. In conclusion. attribute alignment in categorization may facilitate paying selective attention to categorization-relevant attributes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.53-56
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• 2003

유사도 혹은 자료간의 거리 개념은 많은 기계학습 알고리즘에서 사용되고 있는 중요한 측정개념이다 하지만 입력되는 자료의 속성들중 순서가 정의되지 않은 범주형 속성이 포함되어 있는 경우, 자료간의 유사도나 거리 측정에 어려움이 따른다. 비거리 기반의 알고리즘들의 경우-C4.5, CART-거리의 측정없이 작동할 수 있지만, 거리기반의 알고리즘들의 경우 범주형 속성의 거리 정보 결여로 효과적으로 적용될 수 없는 문제점을 갖고 있다. 본 논문에서는 이러한 범주형 자료들간 거리 측정을 자료 집합의 특성을 충분히 고려한 방법을 제안한다. 이를 위해 자료 집합의 선험적인 정보를 필요로 한다. 이런 선험적 정보인 조건부 확률을 기반으로한 거리 측정방법을 제시하고 오류 피드백을 통해서 속성 간 거리 측정을 최적화 하려고 노력한다. 주어진 자료 집합에 대해 서로 다른 두 범주형 값이 목적 속성에 대해서 유사한 분포를 보인다면 이들 값들은 비교적 가까운 거리로 결정한다 이렇게 결정된 거리를 기반으로 학습 단계를 진행하며 이때 발생한 오류들에 대해 피드백 작업을 진행한다. UCI Machine Learning Repository의 자료들을 이용한 실험 결과를 통해 제안한 거리 측정 방법의 우수한 성능을 확인하였다.

• 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.

• Asia Marketing Journal
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• v.13 no.4
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• pp.1-27
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• 2012

Extending the findings of Rajagopal and Burnkrant (2009), this research examines the moderating role of consumers' goals (i.e., head category-relevant goal vs. modifier category-relevant goal) in the effects of two different interpretation strategies (i.e., relational interpretation vs. property interpretation) on product beliefs and attitudes toward hybrid products. In the current research, we make two predictions. First, we predict that both head category and modifier category beliefs will be higher under property interpretations than under relational interpretations in the modifier category-relevant goal priming conditions, whereas there will be no significant differences between each product category beliefs across the two interpretation conditions in the head category-relevant goal priming conditions. Second, we predict that attitudes toward hybrid products will be higher under property interpretations than under relational interpretations in the modifier category-relevant goal priming conditions, whereas there will be no significant differences between the attitudes toward hybrid products across the two interpretation conditions in the head category-relevant goal priming conditions. These predictions are tested and confirmed in two experiments. Finally, we discuss theoretical and practical implications of our findings and develop directions for future research.

• Journal of the Korean Home Economics Association
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• v.40 no.12
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• pp.63-83
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• 2002

본 연구는 미국 소비자들이 지각한 온라인 쇼핑속성에 대한 차원을 밝히고. 온라인 속성에 대한 중요성과 상품범주별 구매의도와의 관계를 밝혀 상품범주별 마케팅 전략과 인터넷 소비자 관리 및 교육 프로그램 개발에 기여하고자 하였다. 조사대상자는 가정에서 인터넷을 사용하고 있는 미국 소비자 303 명으로 구성되었으며, 질문지법에 의해 자료 수집되었다. 자료분석을 위해 탐색적 요인분석을 실행하였고, LISREL8에 의해 측정모델과 구조적 관계 모델을 동시에 검증하였다. 자료 분석결과를 요약하면 다음과 같다. 첫째, 소비자가 지각한 온라인 쇼핑에 대한 속성은 거래 및 비용, 사이트 디자인, 구매유인 프로그램, 상호 관계성의 4개 차원으로 분류되었다. 둘째, 온라인 상품은 구매의도에 따라 인지적 상품, 경험적 상품, 서비스 3개 범주로 분류되었다. 셋째, 지각된 온라인 쇼핑속성의 중요도와 각상품군 구매의도와의 구조적 관계모델을 추정한 결과,“거래 및 비용”은 3개의 상품군에 대한 구매의도에 모두 유의한 영향을 주었으며,“구매유인 프로그램”은 경험적 상품과 서비스에 대한 구매의도에 유의한 영향을 미쳤다. 따라서, 소비자들에게 중요하게 지각되는 인터넷 특정 속성 즉, 보완, 배달 및 비용을 초점으로한 상품범주별 차별화된 이점을 제시하여 효과적인 마케팅 전략을 수립해야 할 것이다. 또한, 전자 상거래와 관련 보완, 환불정책 등에 관한 소비자 교육과 보호법이 요구되고 있다.

• Korean Journal of Cognitive Science
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• v.33 no.3
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• pp.135-154
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• 2022

The purpose of this study was to confirm that the property generalization to social categories with low coherence is stronger when stress due to COVID-19 is perceived as high, compared to when stress is perceived as low. To this end, this study selected categories with high coherence(nun, soldier, flight attendant) and categories with low coherence(wedding planner, interpreter, florist), and recruited 336 participants to perform a category-based inductive generalization task(inferring how many properties repeatedly observed by some category members would appear across all category members), and measured their perceived COVID-19 stress. As a result, this study showed that when the cohesion of social categories is high, the effect of property generalization is stronger than when it is low, and the effect of property generalization is stronger in those who perceive stress due to Corona 19 higher than those who perceive it as low. In addition, this study confirmed that people who perceive COVID-19 stress strongly tend to generalize strongly to properties that are repeatedly observed in the low coherence category. This study is important in that it shows that there is a cognitive mechanism that is at the root of the phenomenon that stereotypes and prejudices deepen and discriminatory behaviors increase after the outbreak of COVID-19, such as COVID-19 stress and the resulting increase in attribute generalization tendency.

• Korean Journal of Cognitive Science
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• v.31 no.3
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• pp.77-102
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• 2020

Some progress has been made in the study of the category coherence effect, which states that the attributes of soldiers or nuns with similarities in dress and behavior, and easily distinguished from other categories, are likely to be generalized. However, few studies have examined the fundamental psychological mechanisms that underlie this category coherence effect, and this study aims to fill this gap. For this purpose, two experiments were conducted after selecting categories with high coherence (nuns, soldiers, and flight attendants) and those with low coherence (interpreters, wedding planners, and florists). In experiment 1, we observed that the members of a category were presumed to have certain reasons to prefer [property X] (presumption of reasons for preference), with this presumption becoming stronger when [property X] was observed repeatedly in high-coherence categories than in the case of low-coherence categories. Experiment 2 showed that for the high-coherence categories, the presumption of reasons for preference was stronger when [property X], rarely seen in everyday life (base rate of 30%), was observed, while the presumption of reasons for preference was weaker when [property Y] (base rate 70%), frequently seen in everyday life, was observed. In the low-coherence categories, the presumption of reasons for preference tended to be weak for both rare and frequent attributes. That is, there were significant effects of the two-way interaction between category coherence and base rate on the presumption of reasons for preference. This study has implications for psychological essentialism and stereotyping.

• 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.