Varieties of Confirmation Bias
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Confirmation bias indicates a tendency to believe information, even if false, when it resonates with and mobilizes already present values, senses and beliefs.
“no debate da psicologia social sobre um “viés de confirmação” (KLAYMAN, 1995) -que indica uma tendência de crença em uma informação, mesmo que falsa, quando ela reverbera e mobiliza valores, sentidos e crenças já presentes.”
From O novo e o velho na organização da mídia no território brasileiro: contribuições para a leitura da conjuntura atual a partir de Milton Santos (2022) · cites this paper as KLAYMAN, 1995
Cites
The case for motivated reasoning.
Kunda, Z. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.108.3.480 · Cited by 9,022 · see citing works
Bem, D. J. (1972) · Advances in Experimental Social Psychology · doi:10.1016/s0065-2601(08)60024-6 · Cited by 5,241 · see citing works
Experimental Tests of the Endowment Effect and the Coase Theorem
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1990) · Journal of Political Economy · doi:10.1086/261737 · Cited by 3,194 · see citing works
Perception and the Representative Design of Psychological Experiments
Brunswik, E. (1956) · doi:10.1525/9780520350519 · Cited by 2,974 · see citing works
Confirmation, disconfirmation, and information in hypothesis testing
Klayman, J., & Ha, Y.-W. (1987) · Psychological Review · doi:10.1037/0033-295x.94.2.211 · Cited by 2,094 · see citing works
Self-citationCheng, P. W., & Holyoak, K. J. (1985) · Cognitive Psychology · doi:10.1016/0010-0285(85)90014-3 · Cited by 1,463 · see citing works
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HARKing: Hypothesizing after the results are known
Kerr, N. L. (1998) · Personality and Social Psychology Review · doi:10.1207/s15327957pspr0203_4 · Cited by 1,989 · see citing works
Corporate social responsibility and media coverage
Cahan, S. F., Chen, C., Chen, L., & Nguyen, N. H. (2015) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2015.07.004 · Cited by 365 · see citing works
Groups perform better than the best individuals on Letters-to-Numbers problems
Laughlin, P. R., Bonner, B. L., & Miner, A. G. (2002) · Organizational Behavior and Human Decision Processes · doi:10.1016/s0749-5978(02)00003-1 · Cited by 218 · see citing works
Thinking about diagnostic thinking: a 30-year perspective
Elstein, A. S. (2009) · Advances in Health Sciences Education · doi:10.1007/s10459-009-9184-0 · Cited by 205 · see citing works
Gambrill, E., & Shlonsky, A. (2000) · Children and Youth Services Review · doi:10.1016/s0190-7409(00)00123-7 · Cited by 187 · see citing works
What Is the Function of Confirmation Bias?
Peters, U. (2022) · Erkenntnis · doi:10.1007/s10670-020-00252-1 · Cited by 163 · see citing works
The Bias That Divides Us: The Science and Politics of Myside Thinking
Stanovich, K. E. (2021) · doi:10.7551/mitpress/13367.001.0001 · Cited by 152 · see citing works
Roles of medial prefrontal cortex and orbitofrontal cortex in self-evaluation
Beer, J. S., Lombardo, M. V., & Bhanji, J. P. (2010) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21359 · Cited by 104 · see citing works
Describing the Process of Adopting Nutrition and Fitness Apps: Behavior Stage Model Approach
König, L. M., Sproesser, G., Schupp, H. T., & Renner, B. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.8261 · Cited by 98 · see citing works
Survey on Machine Learning Biases and Mitigation Techniques
Siddique, S., Haque, M. A., George, R., Gupta, K. D., Gupta, D., & Faruk, M. J. H. (2023) · Digital · doi:10.3390/digital4010001 · Cited by 78 · see citing works
Known Unknowns: A Critical Determinant of Confidence and Calibration
Walters, D. J., Fernbach, P. M., Fox, C. R., & Sloman, S. A. (2017) · Management Science · doi:10.1287/mnsc.2016.2580 · Cited by 62 · see citing works
Approach-Induced Biases in Human Information Sampling
Hunt, L. T., Rutledge, R. B., Malalasekera, W. M. N., Kennerley, S. W., & Dolan, R. J. (2016) · PLOS Biology · doi:10.1371/journal.pbio.2000638 · Cited by 60 · see citing works
The perceived surveillance of conversations through smart devices
Frick, N. R. J., Wilms, K. L., Brachten, F., Hetjens, T., Stieglitz, S., & Ross, B. (2021) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2021.101046 · Cited by 48 · see citing works
Villarroel, C., Felton, M., & Garcia-Mila, M. (2016) · International Journal of Educational Research · doi:10.1016/j.ijer.2016.06.009 · Cited by 44 · see citing works
Generative artificial intelligence–mediated confirmation bias in health information seeking
Lopez‐Lopez, E., Abels, C. M., Holford, D., Herzog, S. M., & Lewandowsky, S. (2025) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15413 · Cited by 38 · see citing works
Opportunities and risks of artificial intelligence in patient portal messaging in primary care
Biro, J. M., Handley, J. L., Malcolm McCurry, J., Visconti, A., Weinfeld, J., Gregory Trafton, J., & Ratwani, R. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01586-2 · Cited by 35 · see citing works
The influence of prior belief on scientific thinking
Evans, J. S. B. T. (2002) · The Cognitive Basis of Science · doi:10.1017/cbo9780511613517.011 · Cited by 22 · see citing works
Be Friendly, Not Friends: How LLM Sycophancy Shapes User Trust
Sun, Y., & Wang, T. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791079 · Cited by 16
“Given growing concerns about unwarranted influence from conversational agents [2], shifting from an initial opposing stance to fully endorsing the user’s opinion, particularly without clear justification, risks being perceived as calculated manipulation.”
Feedback Loops With Language Models Drive In-Context Reward Hacking
Pan, A., Jones, E., Jagadeesan, M., & Steinhardt, J. (2024) · arXiv · doi:10.48550/arxiv.2402.06627 · Cited by 3 · see citing works
“Negative side effect: if users exhibit confirmation bias (Klayman, 1995), they may provide feedback which teaches LLMs to hallucinate articles that align with user viewpoints (Zhang et al., 2023), even if the articles contain misinformation.”
Background
- Authors
- Klayman, J.
- Corresponding author
- Klayman, Joshua
- ORCID
- Klayman, Joshua
- Venue
- Psychology of Learning and Motivation
- Publisher
- Elsevier
- Published
- 1995
- Pages
- 385-418
- ISSN
- 0079-7421 (ISSN-L)
- OpenAlex
- W1526704262
- Cited by
- 755
- References
- 6
- Citations by year
- 2026: 47 · 2025: 70 · 2024: 74 · 2023: 53 · 2022: 74 · 2021: 55 · 2020: 37 · 2019: 35 · 2018: 24 · 2017: 25 · 2016: 20 · 2015: 21 · 2014: 20 · 2013: 13 · 2012: 19
- FWCI
- 7.37
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- The Psychology of learning and motivation/The psychology of learning and motivation (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- confirmation bias, belief updating
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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