• Journal of Information Science Theory and Practice
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• v.8 no.2
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• pp.18-31
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• 2020

Cognitive biases can influence human information behaviour and decisions made in information behaviour and use. This study aims to identify the biases involved in some aspects of information behaviour and the role they play in information behaviour and use. Twenty-five semi-structured face-to-face interviews were conducted in an exploratory qualitative study with graduate (MA and PhD) students who were at the stage of their dissertation/thesis research. Eisenberg & Berkowitz Big6^TM Skills for Information Literacy was adopted as a framework for interviews and the analysis was done using grounded theory coding method. The findings revealed the presence of twenty-eight biases in different stages of information behaviour, including availability bias (affects the preference for information seeking strategies), attentional bias (leads to biased attention to some information), anchoring effect (persuades users to anchor in special parts of information), confirmation bias (increases the tendency to use information that supports one's beliefs), and choice-supportive bias (results in confidence in information seeking processes). All stages of information seeking were influenced by some biases. Biases might result in a lack of clarity in defining the information needs, failure in looking for the right information, misinterpretation of information, and might also influence the way information is presented.

• 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.

• Nuclear Engineering and Technology
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• v.28 no.5
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• pp.467-481
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• 1996

An integrated framework of modeling the human operator cognitive behavior during nuclear power plant accident scenarios is presented. It incorporates both plant and operator models. The basic structure of the operator model is similar to that of existing cognitive models, however, this model differs from those existing ones largely in too aspects. First, using frame and membership function, the pattern matching behavior, which is identified as the dominant cognitive process of operators responding to an accident sequence, is explicitly implemented in this model. Second, the non-task-related human cognitive activities like effect of stress and cognitive biases such as confirmation bias and availability bias, are also considered. A computer code, OPEC is assembled to simulate this framework and is actually applied to an SGTR sequence, and the resultant simulated behaviors of operator are obtained.

• 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.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.