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Journal articles on the topic 'Cognitive bias'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Knobloch-Westerwick, Silvia, Cornelia Mothes, and Nick Polavin. "Confirmation Bias, Ingroup Bias, and Negativity Bias in Selective Exposure to Political Information." Communication Research 47, no. 1 (2017): 104–24. http://dx.doi.org/10.1177/0093650217719596.

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Selective reading of political online information was examined based on cognitive dissonance, social identity, and news values frameworks. Online reports were displayed to 156 Americans while selective exposure was tracked. The news articles that participants chose from were either conservative or liberal and also either positive or negative regarding American political policies. In addition, information processing styles (cognitive reflection and need-for-cognition) were measured. Results revealed confirmation and negativity biases, per cognitive dissonance and news values, but did not corrob

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2

Smith, Joan R. "Cognitive Bias." Journal of Perinatal & Neonatal Nursing 31, no. 4 (2017): 294–96. http://dx.doi.org/10.1097/jpn.0000000000000289.

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3

Chatfield, Tom. "Cognitive Bias." Think 22, no. 63 (2023): 53–58. http://dx.doi.org/10.1017/s1477175622000264.

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AbstractAre human beings irredeemably irrational? If so, why? In this article, I suggest that we need a broader appreciation of thought and reasoning to understand why people get things wrong. Although we can never escape cognitive bias, learning to recognize and understand it can help us push back against its dangers – and in particular to do so collectively and collaboratively.

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4

Howgego, Joshua. "Cognitive bias." New Scientist 228, no. 3051 (2015): 31–32. http://dx.doi.org/10.1016/s0262-4079(15)31757-7.

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5

Hertel, Paula T., and Andrew Mathews. "Cognitive Bias Modification." Perspectives on Psychological Science 6, no. 6 (2011): 521–36. http://dx.doi.org/10.1177/1745691611421205.

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Research conducted within the general paradigm of cognitive bias modification (CBM) reveals that emotional biases in attention, interpretation, and memory are not merely associated with emotional disorders but contribute to them. After briefly describing research on both emotional biases and their modification, the authors examine similarities between CBM paradigms and older experimental paradigms used in research on learning and memory. The techniques and goals of CBM research are compared with other approaches to understanding cognition–emotion interactions. From a functional perspective, th

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6

Norman, Geoff. "The Bias in researching cognitive bias." Advances in Health Sciences Education 19, no. 3 (2014): 291–95. http://dx.doi.org/10.1007/s10459-014-9517-5.

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7

Hertel, Paula T., Amaris Maydon, Julia Cottle, and Janna N. Vrijsen. "Cognitive Bias Modification." Clinical Psychological Science 5, no. 1 (2016): 122–30. http://dx.doi.org/10.1177/2167702616649366.

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Ruminative tendencies to think repetitively about negative events, like retrieval practice in laboratory experiments, should enhance long-term recall. To evaluate this claim, ruminators and nonruminators learned positive, negative, and neutral adjective–noun pairs. Following each of four study phases, “practice” participants attempted cued recall of nouns from positive or negative pairs; study-only participants performed a filler task. Half the pairs of each valence were tested after the four learning cycles, and all pairs were tested a week later. Large practice effects were found on both tes

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Haryati, Setyorini, T. Sihotang Ellen, and F. Sholikah. "Emotional Bias, Cognitive Bias and Herding Bias toward Investment Decision for Indonesian Investor." INTERNATIONAL JOURNAL OF SOCIAL SCIENCE HUMANITY & MANAGEMENT RESEARCH 2, no. 08 (2023): 785–92. https://doi.org/10.5281/zenodo.8228402.

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In making investment decisions, the role of emotions is very important because of investors irrational symptoms in decision making. This study aims to analyze the effect of emotional bias, cognitive bias, and herding bias on the investment decision making of investors in Indonesia. The research approach used is a quantitative approach. By using primary data from respondents spread throughout Indonesia, this study uses the partial least squares as a data analysis technique. The research hypothesis is that there is a positive influence of emotional bias, cognitive bias and herding bias in the pr

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Dacey, Mike. "Anthropomorphism as Cognitive Bias." Philosophy of Science 84, no. 5 (2017): 1152–64. http://dx.doi.org/10.1086/694039.

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10

Taylor, Ronald L. "Bias in Cognitive Assessment." Diagnostique 17, no. 1 (1991): 3–5. http://dx.doi.org/10.1177/153450849101700101.

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11

Reschly, Daniel J. "Bias in Cognitive Assessment." Diagnostique 17, no. 1 (1991): 86–90. http://dx.doi.org/10.1177/153450849101700108.

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12

Park, Christine S., Ljuba Stojiljkovic, Biljana Milicic, Brian F. Lin, and Itiel E. Dror. "Training Induces Cognitive Bias." Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare 9, no. 2 (2014): 85–93. http://dx.doi.org/10.1097/sih.0b013e3182a90304.

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13

Rathbone, P. "Role of cognitive bias." BMJ 342, may17 2 (2011): d3047. http://dx.doi.org/10.1136/bmj.d3047.

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14

Trout, J. D. "Paternalism and Cognitive Bias." Law and Philosophy 24, no. 4 (2005): 393–434. http://dx.doi.org/10.1007/s10982-004-8197-3.

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15

Philips, H. C. "Imagery and Likelihood Cognitive Bias in Pain." Behavioural and Cognitive Psychotherapy 43, no. 3 (2013): 270–84. http://dx.doi.org/10.1017/s1352465813000982.

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Background: Distressing intrusive images are frequently experienced by sufferers from chronic and acute pain. The images (Index images) are correlated with elevations in anxiety, threat, and a cognition that the imaged event might actually happen. The over-estimation that having a negative cognition about an adverse event will increase the probability of the negative event occurring - the likelihood bias - has been observed in a variety of psychological disorders. Preliminary research indicated this cognitive bias might occur in pain sufferers. Aims: To investigate the occurrence of a cognitiv

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16

Adepoju, Sheriff, and Mildred Adepoju. "Cognitive Biases:." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 4, no. 2 (2025): 44–54. https://doi.org/10.60087/jklst.v4.n2.004.

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Artificial Intelligence (AI) systems, while advancing software development, are often susceptible to cognitive biases that lead to unfair outcomes. This study explores the roles of confirmation bias, anchoring bias, and automation bias in influencing AI decision-making. These biases commonly emerge from unrepresentative datasets, algorithmic design flaws, and subjective human decisions. Through a qualitative methodology involving literature review and case analysis, the research identifies the origins and manifestations of cognitive bias in AI, particularly within domains like criminal justice

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17

Le, Luodan. "Media Frame and Audience Cognitive Bias: Assessing News Effectiveness from a Social Psychological Perspective." International Journal of Social Sciences and Public Administration 4, no. 2 (2024): 35–41. http://dx.doi.org/10.62051/ijsspa.v4n2.05.

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As an important channel of information dissemination, news media plays a key role in shaping public cognition and guiding public opinion. Media frame theory points out that news reports do not reflect reality objectively and neutrally, but construct reality by selecting, emphasizing and excluding specific information. This construction process not only affects the audience's understanding of the event, but can also lead to cognitive bias. Cognitive bias refers to the distortion or misunderstanding of reality caused by many factors such as prior knowledge, emotional tendency and social norms wh

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18

Afriani, Dila, and Halmawati Halmawati. "Pengaruh Cognitive Dissonance Bias, Overconfidence Bias Dan Herding Bias Terhadap Pengambilan Keputusan Investasi." JURNAL EKSPLORASI AKUNTANSI 1, no. 4 (2019): 1650–65. http://dx.doi.org/10.24036/jea.v1i4.168.

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This study aims to determine whether cognitive dissonance bias, overconfidence bias and herding bias have a significant effect on stock investment decision making partially and simultaneously. the sample in this study were students of the Faculty of Economics, Padang State University. The sampling technique was simple random sampling . A total of 133 questionnaires were returned in complete condition and processed. by using multiple linear regression techniques, the results show that cognitive dissonance bias and overconfidence bias do not affect stock investment decisions. Herding bias has a

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19

Suzuki, Akiko, Akiko Megumi, and Akira Yasumura. "Developmental Changes in Cognitive Bias." Psychology 12, no. 02 (2021): 293–303. http://dx.doi.org/10.4236/psych.2021.122019.

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20

Berkwitt, A., and M. Grossman. "Cognitive Bias in Inpatient Pediatrics." Hospital Pediatrics 4, no. 3 (2014): 190–93. http://dx.doi.org/10.1542/hpeds.2014-0002.

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21

O'Sullivan, ED, and SJ Schofield. "Cognitive bias in clinical medicine." Journal of the Royal College of Physicians of Edinburgh 48, no. 3 (2018): 225–32. http://dx.doi.org/10.4997/jrcpe.2018.306.

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22

Jolobe, Oscar MP. "Cognitive bias in laparoscopic cholecystectomy." Journal of the Royal College of Physicians of Edinburgh 49, no. 1 (2019): 92–94. http://dx.doi.org/10.4997/jrcpe.2019.119.

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23

TENG, Zhaojun, Yanling LIU, Yong LIU, and Rui ZHAI. "Cognitive Neuromechanisms of Optimism Bias." Advances in Psychological Science 22, no. 1 (2014): 57. http://dx.doi.org/10.3724/sp.j.1042.2014.00057.

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24

Harding, Emma J., Elizabeth S. Paul, and Michael Mendl. "Cognitive bias and affective state." Nature 427, no. 6972 (2004): 312. http://dx.doi.org/10.1038/427312a.

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25

Greene, Robert. "Research for Practitioners: Cognitive Bias." Compensation & Benefits Review 52, no. 1 (2019): 5–7. http://dx.doi.org/10.1177/0886368719892176.

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Research has shown multiple cognitive biases that affect compensation related decisions. This research brief lists and describes several, including the bias held by most of us that we are too intellignet to fall prey to biases. Implications for HR and compensation practitioners conclude this research brief.

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26

Natesan, Divya, Morgan Walker, and Shannon Clark. "Cognitive Bias in Usability Testing." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 5, no. 1 (2016): 86–88. http://dx.doi.org/10.1177/2327857916051015.

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Cognitive bias is the tendency to subconsciously change ones actions or thought process based on someone else’s comments or behavior. It is important to consider cognitive bias when performing a usability study because it can significantly alter or impair the validity of the results. While cognitive bias cannot always be entirely eliminated, identifying key cognitive biases to which usability study results are particularly susceptible is the first step to mitigating them. Once experimenters are alert to these subtle traps, they can consider approaches that sidestep the biases and greatly impro

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27

Wilcockson, Thomas D. W., Pothos Emmanuel M., and W. Miles Cox. "An online cognitive bias task." Behavioural Pharmacology 31, no. 1 (2020): 97–101. http://dx.doi.org/10.1097/fbp.0000000000000508.

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28

McNally, Richard J. "Cognitive Bias in Panic Disorder." Current Directions in Psychological Science 3, no. 4 (1994): 129–32. http://dx.doi.org/10.1111/1467-8721.ep10770595.

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29

Williamson, Donald A., Stephanie L. Muller, Deborah L. Reas, and Jean M. Thaw. "Cognitive Bias in Eating Disorders:." Behavior Modification 23, no. 4 (1999): 556–77. http://dx.doi.org/10.1177/0145445599234003.

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30

Campbell, Samuel G., Pat Croskerry, and David A. Petrie. "Cognitive bias in health leaders." Healthcare Management Forum 30, no. 5 (2017): 257–61. http://dx.doi.org/10.1177/0840470417716949.

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31

Jacobson, Gary P. "Cognitive Bias in Audiology Research." Journal of the American Academy of Audiology 31, no. 2 (2020): 86. https://doi.org/10.3766/jaaa.31.2.1.

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32

Stacy, Webb, and Jean MacMillan. "Cognitive bias in software engineering." Communications of the ACM 38, no. 6 (1995): 57–63. http://dx.doi.org/10.1145/203241.203256.

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33

Sterken, Rachel Katharine. "Generics, Content and Cognitive Bias." Analytic Philosophy 56, no. 1 (2015): 75–93. http://dx.doi.org/10.1111/phib.12056.

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34

Steel, Craig, Til Wykes, Anna Ruddle, Gina Smith, Dhruvi M. Shah, and Emily A. Holmes. "COGNITIVE BIAS MODIFICATION IN SCHIZOPHRENIA." Schizophrenia Research 102, no. 1-3 (2008): 128–29. http://dx.doi.org/10.1016/s0920-9964(08)70390-6.

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35

Elston, Dirk M. "Cognitive bias and medical errors." Journal of the American Academy of Dermatology 81, no. 6 (2019): 1249. http://dx.doi.org/10.1016/j.jaad.2019.06.1284.

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36

Maynes, Jeffrey. "Critical Thinking and Cognitive Bias." Informal Logic 35, no. 2 (2015): 183. http://dx.doi.org/10.22329/il.v35i2.4187.

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Teaching critical thinking skill is a central pedagogical aim in many courses. These skills, it is hoped, will be both portable (applicable in a wide range of contexts) and durable (not forgotten quickly). Yet, both of these virtues are challenged by pervasive and potent cognitive biases, such as motivated reasoning, false consensus bias and hindsight bias. In this paper, I argue that a focus on the development of metacognitive skill shows promise as a means to inculcate debiasing habits in students. Such habits will help students become more critical reasoners. I close with suggestions for im

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37

Ainsworth, Steve. "Cognitive bias: A deadly virus." Nurse Prescribing 13, no. 8 (2015): 373. http://dx.doi.org/10.12968/npre.2015.13.8.373.

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38

Song, Jae-Do, and Young-Hwan Ahn. "Cognitive Bias in Emissions Trading." Sustainability 11, no. 5 (2019): 1365. http://dx.doi.org/10.3390/su11051365.

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This study investigates whether cognitive biases such as the endowment effect and status quo bias occur in emissions trading. Such cognitive biases can serve as a barrier to trade. This study’s survey-based experiments, which include hypothetical emissions trading scenarios, show that the endowment effect does occur in emissions trading. The status quo bias occurs in only one of the three experiments. This study also investigates whether accumulation of experience can reduce cognitive bias as discovered preference hypothesis expects. The results indicate that practitioners who are supposed to

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39

Hales, Steven D., and Jennifer Adrienne Johnson. "Luck Attributions and Cognitive Bias." Metaphilosophy 45, no. 4-5 (2014): 509–28. http://dx.doi.org/10.1111/meta.12098.

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40

Hansen, Kim. "Cognitive bias in emergency medicine." Emergency Medicine Australasia 32, no. 5 (2020): 852–55. http://dx.doi.org/10.1111/1742-6723.13622.

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41

Koster, Ernst HW, and Kristof Hoorelbeke. "Cognitive bias modification for depression." Current Opinion in Psychology 4 (August 2015): 119–23. http://dx.doi.org/10.1016/j.copsyc.2014.11.012.

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42

Papadamos, Panagiotis, and Nina Gierasimczuk. "Cognitive Bias and Belief Revision." Electronic Proceedings in Theoretical Computer Science 379 (July 9, 2023): 441–54. http://dx.doi.org/10.4204/eptcs.379.34.

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43

Güver, Levin, and Markus Kneer. "Causation, Norms, and Cognitive Bias." Cognition 259 (June 2025): 106105. https://doi.org/10.1016/j.cognition.2025.106105.

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44

Zahro, Dalilah El, and Mohamad Nur Singgih. "Pengaruh Herding Bias, Overconfidence Bias, dan Cognitive Dissonance Bias terhadap Keputusan Investasi." Business Management Research 3, no. 2 (2024): 72–83. http://dx.doi.org/10.26905/bismar.v3i2.13772.

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The purpose of this study is to analyze investor behavior influenced by herding bias, overconfidence bias, and cognitive dissonance bias on investment decision making. Investors were given a questionnaire as part of the quantitative data collection for this study. This study used data from a sample of 102 investors who had all been active in the capital market for at least one year, had traded, and had invested at least twice. Several statistical analyses, including convergent and discriminant validity tests, composite reliability, and significance tests, were performed on the data collected f

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45

Noworyta, Karolina, Agata Cieslik, and Rafal Rygula. "Neuromolecular Underpinnings of Negative Cognitive Bias in Depression." Cells 10, no. 11 (2021): 3157. http://dx.doi.org/10.3390/cells10113157.

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This selective review aims to summarize the recent advances in understanding the neuromolecular underpinnings of biased cognition in depressive disorder. We begin by considering the cognitive correlates of depressed mood and the key brain systems implicated in its development. We then review the core findings across two domains of biased cognitive function in depression: pessimistic judgment bias and abnormal response to negative feedback. In considering their underlying substrates, we focus on the neurochemical mechanisms identified by genetic, molecular and pharmacological challenge studies.

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46

Yoshimura, Shinpei, Seiji Muranaka, and Asuka Sugita. "How socioeconomic status, cognitive bias, cognitive reappraisal affects depression?" Proceedings of the Annual Convention of the Japanese Psychological Association 84 (September 8, 2020): PD—106—PD—106. http://dx.doi.org/10.4992/pacjpa.84.0_pd-106.

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47

LI, CAI, HABIB UR REHMAN MAKHDOOM, and LIU YI. "AN EMPIRICAL STUDY ON THE GENDER DIFFERENCES FOR SELF-ORGANIZED ENTREPRENEURIAL BEHAVIOR BASED ON GROUNDED THEORY: A CASE FROM JIANGSU, CHINA." Journal of Developmental Entrepreneurship 24, no. 01 (2019): 1950006. http://dx.doi.org/10.1142/s1084946719500067.

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Based on grounded theory and combined with empirical investigation of self-organized entrepreneurial behavior, we explored the difference between male and female’s cognitive mode, cognitive bias and behavior influence. It is found that the cognitive pattern of self-organized female entrepreneurs is more narrow and obtrusive, but a few outstanding female entrepreneurs show outstanding performance. Self-organized male entrepreneurs show open and divergent structured cognition. On cognitive bias, women of ordinary self-organized entrepreneurs have a greater range of deviations. In terms of behavi

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48

Mineka, Susan, and Steven K. Sutton. "Cognitive Biases and the Emotional Disorders." Psychological Science 3, no. 1 (1992): 65–69. http://dx.doi.org/10.1111/j.1467-9280.1992.tb00260.x.

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Four types of cognitive bias involving selective processing of emotion-relevant information are discussed vis-à-vis their relevance for understanding emotion-cognition interactions and for understanding the etiology and maintenance of the emotional disorders. Anxiety, but not depression, appears to be associated with an attentional bias for threatening material. Depression, but not anxiety, appears to be associated with a memory bias for negative mood-congruent material. Phobias, anxiety, and depression all appear to be associated with mood-congruent judgmental biases. Finally, selective assoc

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49

Islam, Md Mafiqul. "Cognitive Frameworks for Mitigating Antiblack Bias: Advancing Ethical AI Design and Development." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 4, no. 1 (2024): 1–12. http://dx.doi.org/10.60087/jaigs.vol4.issue1.p12.

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This paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computation

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50

Islam, Md Mafiqul. "Cognitive Frameworks for Mitigating Antiblack Bias: Advancing Ethical AI Design and Development." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 4, no. 1 (2024): 1–12. http://dx.doi.org/10.60087/jaigs.v4i1.77.

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This paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computation

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