• Journal of Digital Convergence
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• v.19 no.1
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• pp.355-360
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• 2021

In this study, the factors of confirmation bias that may occur in the initial user experience stage were analyzed using a honeycomb model by deriving user experience factors for each factor. In the initial user experience stage, confirmation bias occurs in the impression stage. At the processing stage of memory, sensory memory, working memory, and long-term memory, which stores and retrieves selective memory, were closely related. Confirmation bias was classified into visibility, correlation, memory, clarity, and universality in the usability part, and satisfaction, joy, and dissatisfaction were derived as emotional factors. As a result of the analysis with the honeycomb model, visuality, clarity, universality in the usability factor, and joy in the emotional factor had little effect on the confirmation bias, and satisfaction and dissatisfaction were analyzed as the main factors of the confirmation bias in the correlation, memory and emotional factors. This study is meaningful in that it can be usefully used as a reference material for companies that customize design patterns for the factor of confirmation bias.

• Korean Journal of Culture and Social Issue
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• v.26 no.3
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• pp.329-349
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• 2020

Confirmation bias is well known to be the cause of widespread misjudgment in the field of forensic decision-making. In this study, we examined the psychological mechanisms by which confirmation bias affects intentionality judgment in serious injury and death cases that combine the moral characteristics of the perpetrator and victim differently. As a result, participants perceived the case as a more typical criminal case when both the perpetrator and victim were bad people, and gave higher intention to perpetrators' actions in these typical crimes. In particular, it was found that people with a high degree of confirmation bias highly judge the intention of the offenders in a consistent way with the stereotype of criminal cases. However, in serious criminal cases, the moderate effect of confirmation bias has disappeared and only the effect of crime typicality has existed. Finally, we discussed implications of this study and ways to reduce bias in intentionality judgment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.169-178
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• 2019

This study examined the influence and role of college students' career preference type on the decision-making process of career selection, and also analyzed the effect on the confession bias in the process. The data was collected from June 20th to July 9th, 2017. The case analysis method was used for three times over 20 days by interviewing university students in the metropolitan area. The results of the analysis of the data from the study participants show that there are differences in the degree of individuals regardless of the preferred occupation type and career aptitude, but mostly there is a bias toward career and career. Confirmed biased thinking is a phenomenon that is shaped by the psychological and environmental factors of the research participants and that they try to fit themselves into a framework that reflects social awareness. The implication of this study is that the preference type affects the selection of the major, and that confirmation bias is a hindrance to proper employment and a job search. The preference for the individual job type should be properly established. Environment and opportunity should be provided.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.190-199
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• 2019

This quantitative study analyzed the influence of college students' preferred type of occupation on a confirmation bias and job anxiety during the process of making a career decision. The questionnaires were distributed to university students in Seoul and the metropolitan area for 500 weeks from July 10 2017 to August 8, 2017. Among them, 482 valid samples of data were analyzed by data coding and data cleaning usin SPSS 18.0 statistics and the AMOS 18.0 program. The main results of this study are that the type of business preference for an affirmative bias has a positive (+) direct influence (${\beta}=.374$) and the type of freedom has a positive direct influence (${\beta}=.326$) and a negative direct influence (${\beta}=-.274$). In the case of job anxiety, the influence of job type is more increased. The confirmation bias shows that the business type and freestyle type find cause in effort or achievement motive, while rect type is recognized as social environment and structural problem. In conclusion, there is a difference in the degree of confirmation bias and job insecurity. This study shows that college students' preferred occupation types can help them to understand the bias and anxiety that they have in preparing for the job and help to reduce job anxiety, and these findings are expected to be useful for career guidance.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.89-95
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• 2020

In the digital media environment, the communication of government agencies has changed in various forms and the expertise has been strengthened. However, there are still negative views on the performance of public communication by government agencies. Criticism by prejudice, rather than rational evaluation, can hinder the development of public communication. Therefore, this study intends to identify whether there is a confirmatory bias in evaluating the performance of public communication by government and to suggest alternatives. To this end, this study confirmed that there is a possibility of confirmatory bias in evaluating the performance of public advertising/PR by comparing the simultaneous and sequential environments. The results of this study are expected to contribute to sharing and expanding public values by presenting rational criticism and evaluation methods for public communication.

• Journal of The Korean Association For Science Education
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• v.36 no.6
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• pp.935-946
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• 2016

This study aims to explore cognitive biases relating the core competences of science and instructional strategy in reducing the level of cognitive biases. The literature review method was used to explore cognitive biases and science education experts discussed the relevance of cognitive biases to science education. Twenty nine cognitive biases were categorized into five groups (limiting rational causal inference, limiting diverse information search, limiting self-regulated learning, limiting self-directed decision making, and category-limited thinking). The cognitive biases in limiting rational causal inference group are teleological thinking, availability heuristic, illusory correlation, and clustering illusion. The cognitive biases in limiting diverse information search group are selective perception, experimenter bias, confirmation bias, mere thought effect, attentional bias, belief bias, pragmatic fallacy, functional fixedness, and framing effect. The cognitive biases in limiting self-regulated learning group are overconfidence bias, better-than-average bias, planning fallacy, fundamental attribution error, Dunning-Kruger effect, hindsight bias, and blind-spot bias. The cognitive biases in limiting self-directed decision-making group are acquiescence effect, bandwagon effect, group-think, appeal to authority bias, and information bias. Lastly, the cognitive biases in category-limited thinking group are psychological essentialism, stereotyping, anthropomorphism, and outgroup homogeneity bias. The instructional strategy to reduce the level of cognitive biases is disused based on the psychological characters of cognitive biases reviewed in this study and related science education methods.

• Smart Media Journal
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• v.13 no.2
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• pp.44-51
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• 2024

The construction of a stress measurement detaset plays a curcial role in various modern applications. In particular, for the efficient training of artificial intelligence models for stress measurement, it is essential to compare various biases and construct a quality-controlled dataset. In this paper, we propose the construction of a stress measurement dataset with quality management through the comparison of various biases. To achieve this, we introduce strss definitions and measurement tools, the process of building an artificial intelligence stress dataset, strategies to overcome biases for quality improvement, and considerations for stress data collection. Specifically, to manage dataset quality, we discuss various biases such as selection bias, measurement bias, causal bias, confirmation bias, and artificial intelligence bias that may arise during stress data collection. Through this paper, we aim to systematically understand considerations for stress data collection and various biases that may occur during the construction of a stress dataset, contributing to the construction of a dataset with guaranteed quality by overcoming these biases.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.205-212
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• 2016

This study attempts to empirically investigate if value strategy and momentum strategy could be improved by using past financial data such as ROE and PER in the Korean stock market. The study observes that both strategies which are refined by the portfolios consisting of companies with higher ROE/PER ratio show higher positive excessive returns than the traditional value strategy and momentum strategy. The study discusses that the excessive returns could be due to investors' behavioral biases such as conservatism, anchoring, confirmation, and herding by using convergent approach based on psychology theory. The results are not consistent with the efficient market hypothesis insisting investors' rational behavior.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.625-636
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• 2011

This paper proposes two parametric entropy estimators, the minimum variance unbiased estimator and the maximum likelihood estimator, for the lognormal distribution for a comparison of the properties of the two estimators. The variances of both estimators are derived. The influence of the bias of the maximum likelihood estimator on estimation is analytically revealed. The distributions of the proposed estimators obtained by the delta approximation method are also presented. Performance comparisons are made with the two estimators. The following observations are made from the results. The MSE efficacy of the minimum variance unbiased estimator appears consistently high and increases rapidly as the sample size and variance, n and ${\sigma}^2$, become simultaneously small. To conclude, the minimum variance unbiased estimator outperforms the maximum likelihood estimator.