• Korean Journal of Cognitive Science
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• v.33 no.2
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• pp.109-133
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• 2022

Episodic memory consists of a core event and the associated contexts. Although the role of the hippocampus and its neighboring regions in contextual representations during encoding has become increasingly evident, it remains unclear how these regions handle various context-specific information other than spatio-temporal contexts. Using high-resolution functional MRI, we explored the patterns of the medial temporal lobe (MTL) and cortical regions' involvement during the encoding of various types of contextual information (i.e., journalism principle 5W1H): "Who did it?," "Why did it happen?," "What happened?," "When did it happen?," "Where did it happen?," and "How did it happen?" Participants answered six different contextual questions while looking at simple experimental events consisting of two faces with one object on the screen. The MTL was divided to sub-regions by hierarchical clustering from resting-state data. General linear model analyses revealed a stronger activation of MTL sub-regions, the prefrontal lobe (PFC), and the inferior parietal lobule (IPL) during social (Who), behavioral (How), and intentional (Why) contextual processing when compared with spatio-temporal (Where/When) contextual processing. To further investigate the functional networks involved in contextual encoding dissociation, a multivariate pattern analysis was conducted with features selected as the task-based connectivity links between the hippocampal subfields and PFC/IPL. Each social, behavioral, and intentional contextual processing was individually and successfully classified from spatio-temporal contextual processing, respectively. Thus, specific contexts in episodic memory, namely social, behavior, and intention, involve distinct functional connectivity patterns that are distinct from those for spatio-temporal contextual memory.

• Korean Journal of Cognitive Science
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• v.18 no.4
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• pp.351-370
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• 2007

This study investigated whether top-down information influenced the filing-in of the blind spot. Two potential stimuli, which could fill in the blind spot, were presented at the location of the blind spot. When the two stimuli competed against each other to fill in the blind spot with equal probability, the depth-context of one of the stimuli was manipulated. We used an overlapping cue as our depth-context. Participants' task was to report the relative depth of a target presented in the blind spot. We found that the overlapping rue influenced the frequency of reported depth. These results, however, were nor observed in the off blind spot. These results suggest that top-down information such as an overlapping cue influences the filing-in of the blind spot.

• Journal of Elementary Mathematics Education in Korea
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• v.7 no.1
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• pp.23-44
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• 2003

The purpose of the study is to examine the phenomenon presented the process of problem solving activities of students with the mathematical context information accompanied problem based on Freudenthal's mathematizing theory and Realistic Mathematics Educations about cognitive and emotional aspects. In conclusion, taking a look at the results of study, open-ended contextual problem was had to offer in order to pull out various solutions. Teachers should help students develop their own methods, discuss their methods with others' and reinvent formal mathematics and its constructive process under the guidance of the teachers.

• Annual Conference of KIPS
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• 2020.05a
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• pp.587-590
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• 2020

스마트폰, GPS 장비, 위치 기반 소셜네트워크의 발달로 방대한 이동 경로 데이터 수집이 가능하게 됐다. 이를 통해 다양한 분야에서 GPS 데이터를 가지고 사람의 이동성을 분석하고 POI를 예측하는 기회가 많아졌다. 실생활에서 사람의 이동성은 다양한 상황에 영향을 받지만, 실제 GPS 데이터는 위치, 시간 정보의 수준이다. 따라서 다양한 상황을 내재하는 정보가 사람의 이동성 분석과 POI 예측에 필요하다. 본 논문에서는 POI의 순위, 사용자의 POI 활동, 카테고리 선호도 같은 맥락적 특징을 이용하여 이에 관련된 상황에 맞는 POI 시퀀스를 예측하는 Contextual LSTM 기반 딥러닝 기법을 제안한다. Contextual LSTM은 사람의 이동성에 영향을 주는 시퀀스의 맥락적 특징을 모델에 통합하기 위해 LSTM을 확장한다. 제안된 기법은 HITS 알고리즘과 여러 제약조건 기반으로 추출한 맥락적 특징별로 딥 러닝 모델에 통합하여 각각 POI 시퀀스를 검출했으며, 다양한 맥락적 특징에 대해서 공공 데이터와 수집한 데이터로 평가하였다.

• Annual Conference on Human and Language Technology
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• 1992.10a
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• pp.413-421
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• 1992

두 개의 실험을 통하여, 시간경과에 따른 한글 다의 단어의 의미처리과정을 알아보고자 하였다. 실험1에서는 의미를 편향시키는 맥락이 없는 상황에서 다의어의 의미처리를 알아보고자 하였는데, 결과는 사용빈도가 높은 의미의 활성화 촉진의 정도가, 빈도가 낮은 의미에 비하여 크고 오래 지속됨을 보여주었다. 실험 2에서는 다의어의 의미를 하나의 의미로 편향시키는 맥락을 사용하였는데, 맥락에 부합하는 의미의 반응시간이 부합하지 않는 의미에 비하여 빨랐다. 그리고 처음에는(SOA가 짧을 때) 일차 의미와 이차 의미의 활성화가 동시에 이루어지지만, 시간이 경과할수록 일차 의미의 활성화촉진은 이차의미에 비하여 크고 오랫동안 유지됨을 보여주었다.

• Korean Journal of Cognitive Science
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• v.22 no.2
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• pp.173-192
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• 2011

The visual system applies previously learned contextual knowledge to facilitate the perception and encoding of scenes. When extrapolation following the employment of contextual information occurs, it sometimes leads to scene memory distortion: people report as if they saw more than they actually had seen. This phenomenon is called the "boundary extension" effect (Intraub & Richardson, 1989). The present study aimed to clarify the effects of contextual information on boundary extension in a more systematic way. Based on the assumption that it is harder to extract contextual information from inverted scenes compared to intact scenes, we presented inverted scenes either during encoding or retrieval to manipulate the level of contextual information and compared the magnitude of boundary extension effect for upright versus inverted scenes. In a series of experiments, we found that scene inversion during encoding, but not during retrieval, significantly reduced boundary extension. Showing reduced memory distortion for inverted scenes, the current study directly demonstrated that access to contextual information is a critical component of scene extrapolation process.

• Proceedings of the Korean Biblia Society for Library and Information Science Conference
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• 2005.10a
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• pp.87-110
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• 2005

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.79-86
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• 2024

In this paper, a novel visual tracking method which can utilize the context of object regions is presented. Conventional methods have the inherent problem of treating all candidate regions independently, where the tracker could not successfully discriminate regions with similar appearances. This was due to lack of contextual modeling in a given scene, where all candidate object regions should be taken into consideration when choosing a single region. The goal of the proposed method is to encourage feature exchange between candidate regions to improve the discriminability between similar regions. It improves upon conventional methods that only consider a single region, and is implemented by employing the MLP-Mixer model for enhanced feature exchange between regions. By implementing channel-wise, inter-region interaction operation between candidate features, contextual information of regions can be embedded into the individual feature representations. To evaluate the performance of the proposed tracker, the large-scale LaSOT dataset is used, and the experimental results show a competitive AUC performance of 0.560 while running at a real-time speed of 65 fps.

• Communications of Mathematical Education
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• v.38 no.3
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• pp.401-423
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• 2024

This study analyzes information processing competency tasks included in high school mathematics textbooks according to the 2015 Revised Curriculum from the perspective of mathematical modeling. The findings reveal that most tasks were focused on mathematical concepts without incorporating real-life contexts, with a significant emphasis on the use of engineering tools among the sub-elements of information processing competencies. These results suggest potential difficulties for students in developing mathematical thinking skills necessary for practical problem-solving. This study underscores the need for the 2022 Revised Curriculum to enhance the connection with real-life problems and to design tasks that reflect a balanced integration of the various sub-elements of information processing competencies.