• Title/Summary/Keyword: Cognitive Information Processing

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Differences in Large-scale and Sliding-window-based Functional Networks of Reappraisal and Suppression

  • Jun, Suhnyoung;Lee, Seung-Koo;Han, Sanghoon
    • Science of Emotion and Sensibility
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    • v.21 no.3
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    • pp.83-102
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    • 2018
  • The process model of emotion regulation suggests that cognitive reappraisal and expressive suppression engage at different time points in the regulation process. Although multiple brain regions and networks have been identified for each strategy, no articles have explored changes in network characteristics or network connectivity over time. The present study examined (a) the whole-brain network and six other resting-state networks, (b) their modularity and global efficiency, which is an index of the efficiency of information exchange across the network, (c) the degree and betweenness centrality for 160 brain regions to identify the hub nodes with the most control over the entire network, and (d) the intra-network and inter-network functional connectivity (FC). Such investigations were performed using a traditional large-scale FC analysis and a relatively recent sliding window correlation analysis. The results showed that the right inferior orbitofrontal cortex was the hub region of the whole-brain network for both strategies. The present findings of temporally altering functional activity of the networks revealed that the default mode network (DMN) activated at the early stage of reappraisal, followed by the task-positive networks (cingulo-opercular network and fronto-parietal network), emotion-processing networks (the cerebellar network and DMN), and sensorimotor network (SMN) that activated at the early stage of suppression, followed by the greater recruitment of task-positive networks and their functional connection with the emotional response-related networks (SMN and occipital network). This is the first study that provides neuroimaging evidence supporting the process model of emotion regulation by revealing the temporally varying network efficiency and intra- and inter-network functional connections of reappraisal and suppression.

Consumer's Product Evaluation on the Experiential Attributes & Functional Attributes (체험적 속성과 기능적 속성에 대한 소비자 제품평가)

  • Min, Byung-Kwon;Jung, Yong-Gil
    • The Journal of the Korea Contents Association
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    • v.9 no.5
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    • pp.230-240
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    • 2009
  • This study proposes a theory of consumer experiences based on a cognitive science framework that serves as an alternative to the mainstream marketing paradigm of information processing and choice. The theory consists of three key theoretical constructs: experiential modules, primary vs. secondary experiences, and the hierarchy of experiential modules. Based on this theory, this study investigates the effect of experiential attributes and functional attributes on consumer's product evaluations, and the moderating role of consumer's knowledge. The main research findings are (1) the subjects react faster to sensory and affective stimuli(ex: experiential attributes) than they do to intellectual stimuli (ex: functional attributes), (2) the interaction modularity of attributes(functional vs. experiential) $\times$ tempo(normal vs. fast) $\times$ product knowledge(novice vs. expert) appear significantly with product evaluation as the dependent measure.

A study on environmental adaptation and expansion of intelligent agent (지능형 에이전트의 환경 적응성 및 확장성)

  • Baek, Hae-Jung;Park, Young-Tack
    • The KIPS Transactions:PartB
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    • v.10B no.7
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    • pp.795-802
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    • 2003
  • To live autonomously, intelligent agents such as robots or virtual characters need ability that recognizes given environment, and learns and chooses adaptive actions. So, we propose an action selection/learning mechanism in intelligent agents. The proposed mechanism employs a hybrid system which integrates a behavior-based method using the reinforcement learning and a cognitive-based method using the symbolic learning. The characteristics of our mechanism are as follows. First, because it learns adaptive actions about environment using reinforcement learning, our agents have flexibility about environmental changes. Second, because it learns environmental factors for the agent's goals using inductive machine learning and association rules, the agent learns and selects appropriate actions faster in given surrounding and more efficiently in extended surroundings. Third, in implementing the intelligent agents, we considers only the recognized states which are found by a state detector rather than by all states. Because this method consider only necessary states, we can reduce the space of memory. And because it represents and processes new states dynamically, we can cope with the change of environment spontaneously.

Improvement of OLSR Through MIMC's Decreased Overhead in MANET (모바일 애드 혹 네트워크 환경 하에서 멀티인터페이스 멀티채널의 오버헤드 감소를 통한 OLSR의 성능 개선)

  • Jang, Jae-young;Kim, Jung-ho
    • KIPS Transactions on Computer and Communication Systems
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    • v.5 no.3
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    • pp.55-70
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    • 2016
  • The most critical research issue in MANET environment is on supporting reliable communication between various devices. Various Multi-Hop Routing Protocol studies have proceeded. However, some problems you might have found when you use the existing link state routing technique are that it increases Control Message Overhead and it is unstable when node moves in CR circumstance which has transformation of using channel and MIMC circumstance which uses a number of interfaces. This essay offers a technique which is based on On-Demand Hello and the other technique which used Broadcast Interface of optimization as a solution to decrease Control Message Overhead. Also it proposes Quick Route Restoration technique which is utilized by GPS and MPR Selection technique which consider mobility as a solution of stable communication when node moves. Those offered Routing Protocol and OPNET based simulator result will be expected to be an excellent comparison in related research fields.

Development of an HTM-Based Parts Image Recognition System for Small Scale Manufacturing Industry (중소 제조업을 위한 HTM 기반의 부품 이미지 인식 시스템의 개발)

  • Bae, Sun-Gap;Lee, Dae-Han;Diao, Jian-Hua;Nan, Hai-Bao;Sung, Ki-Won;Bae, Jong-Min;Kang, Hyun-Syug
    • The KIPS Transactions:PartD
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    • v.16D no.4
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    • pp.613-620
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    • 2009
  • It is necessary to develop a system of judging whether or not the parts are defective easily at low cost, especially in a small scale factory which manufactures a large variety of products in small amounts. To develop such system, we require to recognize objects using human's cognitive ability under various circumstances. Human's high intelligence originates mostly from neocortex of human brain. The HTM theory, which is proposed by Jeff Hopkins, is one of the recent researches to model the operation principle of neocortex. In this paper we developed PRESM (Parts image REcognition System for small scale Manufacturing industry) system based on the HTM theory to judge badness of manufactured products. As a result of application to the real field of workplace environments we identified the superiority of our recognition system.

Comparison of Performance on Superordinate Word Tasks in Elderly and Young Adults (노년층과 청년층의 상위범주어 과제 수행력 비교)

  • Kim, Hyung Moo;Yoon, Ji Hye
    • 재활복지
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    • v.20 no.4
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    • pp.229-246
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    • 2016
  • The aim of this study is to conduct superordinate word selection task to compare their performance and reaction time, and superordinate word writing task to compare the differences in their performance and error pattern in 40 elderly adults and 43 young adults. As a result, first, in both tasks, elderly adults had a smaller number of correct responses. Second, elderly adults showed slower reaction time than young adults. Third, in superordinate word writing task, elderly adults showed more relevant errors than irrelevant errors. The reason elderly adults had a smaller number of correct responses in both tasks was that the links among the pieces of information in the semantic lexicon weakened or deteriorated due to normal aging. Slower reaction time was based on neurophysiological changes of the brain and cognitive processing speed. In addition, the relevant errors showed that they could access the lexicon for target words and produce explanation the relevant characteristics, even though they could not retrieve the target words.

Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses

  • Dong-Woo Ryu;ChungHwee Lee;Hyuk-je Lee;Yong S Shim;Yun Jeong Hong;Jung Hee Cho;Seonggyu Kim;Jong-Min Lee;Dong Won Yang
    • Dementia and Neurocognitive Disorders
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    • v.23 no.3
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    • pp.127-135
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    • 2024
  • Background and Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions: The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.

CNN-ViT Hybrid Aesthetic Evaluation Model Based on Quantification of Cognitive Features in Images (이미지의 인지적 특징 정량화를 통한 CNN-ViT 하이브리드 미학 평가 모델)

  • Soo-Eun Kim;Joon-Shik Lim
    • Journal of IKEEE
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    • v.28 no.3
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    • pp.352-359
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    • 2024
  • This paper proposes a CNN-ViT hybrid model that automatically evaluates the aesthetic quality of images by combining local and global features. In this approach, CNN is used to extract local features such as color and object placement, while ViT is employed to analyze the aesthetic value of the image by reflecting global features. Color composition is derived by extracting the primary colors from the input image, creating a color palette, and then passing it through the CNN. The Rule of Thirds is quantified by calculating how closely objects in the image are positioned near the thirds intersection points. These values provide the model with critical information about the color balance and spatial harmony of the image. The model then analyzes the relationship between these factors to predict scores that align closely with human judgment. Experimental results on the AADB image database show that the proposed model achieved a Spearman's Rank Correlation Coefficient (SRCC) of 0.716, indicating more consistent rank predictions, and a Pearson Correlation Coefficient (LCC) of 0.72, which is 2~4% higher than existing models.

A Study on Industries's Leading at the Stock Market in Korea - Gradual Diffusion of Information and Cross-Asset Return Predictability- (산업의 주식시장 선행성에 관한 실증분석 - 자산간 수익률 예측 가능성 -)

  • Kim Jong-Kwon
    • Proceedings of the Safety Management and Science Conference
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    • 2004.11a
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    • pp.355-380
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    • 2004
  • I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

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Clinical Study for YMG-1, 2's Effects on Learning and Memory Abilities (육미지황탕가감방-1, 2가 학습과 기억능력에 미치는 영향에 관한 임상연구)

  • Park Eun Hye;Chung Myung Suk;Park Chang Bum;Chi Sang Eun;Lee Young Hyurk;Bae Hyun Su;Shin Min Kyu;Kim Hyun taek;Hong Moo Chang
    • Journal of Physiology & Pathology in Korean Medicine
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    • v.16 no.5
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    • pp.976-988
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    • 2002
  • The aim of this study was to examine the memory and attention enhancement effect of YMG-1 and YMG-2, which are modified herbal extracts from Yukmijihwang-tang (YMJ). YMJ, composing six herbal medicine, has been used for restoring the normal functions of the body to consolidate the constitution, nourishing and invigorating the kidney functions for hundreds years in Asian countries. A series of studies reported that YMJ and its components enhance memory retention, protects neuronal cell from reactive oxygen attack and boost immune activities. Recently the microarray analysis suggested that YMG-1 protects neurodegeneration through modulating various neuron specific genes. A total of 55 subjects were divided into three groups according to the treatment of YMG-1 (n=20), YMG-2 (n=20) and control (C; n=15) groups. Before treatments, all of subjects were subjected to the assessments on neuropsychological tests of K-WAIS test, Rey-Kim memory test, and psychophysiological test of Event-Related Potential (ERP) during auditory oddball task and repeated word recognition task. They were repeatedly assessed with the same methods after drug treatment for 6 weeks. Although no significant effect of drug was found in Rey-Kim memory test, a significant interaction (P = .010, P < 0.05) between YMG-2 and C groups was identified in the scores digit span and block design, which are the subscales of K-WAIS. The very similar but marginal interaction (P = .064) between YMG-1 and C groups was found too. In ERP analysis, only YMG-1 group showed decreasing tendency of P300 latency during oddball task while the others tended to increase, and it caused significant interaction between session and group (p= .004). This result implies the enhancement of cognitive function in due to consideration of relationship between P300 latency and the speed of information processing. However, no evidence which could demonstrate the significant drug effect was found in neither amplitude or latency. These results come together suggest that YMG-1, 2 may enhance the attention, resulting in enhancement of memory processing. For elucidating detailed mechanism of YMG on learning and memory, the further studies are necessary.