Bias & Heuristics

Resources

Abraham, J. (2023). Science, politics and the pharmaceutical industry: Controversy and bias in drug regulation. Routledge. https://www.taylorfrancis.com/books/mono/10.4324/9781003421405/science-politics-pharmaceutical-industry-john-abraham-0-john-abraham-university-reading

Ackerman, R., Bernstein, D. M., & Kumar, R. (2020). Metacognitive hindsight bias. Memory & Cognition, 48(5), 731–744. https://doi.org/10.3758/s13421-020-01012-w

Adkins, T., Lewis, R., & Lee, T. (2022). Heuristics contribute to sensorimotor decision-making under risk. Psychonomic Bulletin & Review, 29(1), 145-158. https://doi.org/10.3758/s13423-021-01986-x

Azzopardi, L., & Liu, J. (2024, March). Search under uncertainty: Cognitive biases and heuristics-Tutorial on modeling search interaction using behavioral economics. In Proceedings of the 2024 Conference on Human Information Interaction and Retrieval (pp. 427-430). https://doi.org/10.1145/3627508.363829

Bagheri, A., Pasande, M., Bello, K., Araabi, B. N., & Akhondi-Asl, A. (2024). Discovering the effective connectome of the brain with dynamic Bayesian DAG learning. NeuroImage, 120684. https://doi.org/10.1016/j.neuroimage.2024.120684

Balarezo, J. D., Foss, N. J., & Nielsen, B. B. (2024). Organizational learning: Understanding cognitive barriers and what organizations can do about them. Management Learning, 55(5), 741-768. https://doi.org/10.1177/13505076231210635

Bojke, L., Soares, M., Claxton, K., Colson, A., Fox, A., Jackson, C., ... & Taylor, A. (2021). Reviewing the evidence: heuristics and biases. In Developing a reference protocol for structured expert elicitation in health-care decision-making: a mixed-methods study. NIHR Journals Library.

Chater, N., Zhu, J. Q., Spicer, J., Sundh, J., León-Villagrá, P., & Sanborn, A. (2020). Probabilistic biases meet the Bayesian brain. Current Directions in Psychological Science, 29(5), 506-512. https://doi.org/10.1177%2F0963721420954801

Colas, J. T., O’Doherty, J. P., & Grafton, S. T. (2024). Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts. PLOS Computational Biology, 20(3), e1011950. https://doi.org/10.1371/journal.pcbi.1011950

Fadus, M. C., Ginsburg, K. R., Sobowale, K., Halliday-Boykins, C. A., Bryant, B. E., Gray, K. M., & Squeglia, L. M. (2020). Unconscious Bias and the Diagnosis of Disruptive Behavior Disorders and ADHD in African American and Hispanic Youth. Academic Psychiatry : The Journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry, 44(1), 95–102. https://doi.org/10.1007/s40596-019-01127-6

Fathollahi-Fard, A. M., Wong, K. Y., & Aljuaid, M. (2023). An efficient adaptive large neighborhood search algorithm based on heuristics and reformulations for the generalized quadratic assignment problem. Engineering Applications of Artificial Intelligence, 126, 106802. https://doi.org/10.1016/j.engappai.2023.106802

Gichoya, J. W., Thomas, K., Celi, L. A., Safdar, N., Banerjee, I., Banja, J. D., ... & Purkayastha, S. (2023). AI pitfalls and what not to do: mitigating bias in AI. The British Journal of Radiology, 96(1150), 20230023. https://doi.org/10.1259/bjr.20230023

Gopal, D. P., Chetty, U., O'Donnell, P., Gajria, C., & Blackadder-Weinstein, J. (2021). Implicit bias in healthcare: clinical practice, research and decision making. Future Healthcare Journal, 8(1), 40–48. https://doi.org/10.7861/fhj.2020-0233

Güldener, L., & Pollmann, S. (2024). Behavioral bias for exploration is associated with enhanced signaling in the lateral and medial frontopolar cortex. Journal of Cognitive Neuroscience, 36(6), 1156-1171. DOI: 10.1162/jocn_a_02132

Halpern, S. D., Truog, R. D., & Miller, F. G. (2020). Cognitive bias and public health policy during the COVID-19 pandemic. JAMA, 324(4), 337-338.https://doi.org/10.1001/jama.2020.11623

Hjeij, M., & Vilks, A. (2023). A brief history of heuristics: how did research on heuristics evolve?. Humanities and Social Sciences Communications, 10(1), 1-15. https://doi.org/10.1057/s41599-023-01542-z

Hunt, A. K., Wang, J., Alizadeh, A., & Pucelj, M. (2024). Advancing a theoretical framework for exploring heuristics and biases within HR decision-making contexts. Personnel Review. ISSN: 0048-3486

Ishfaq, M., Nazir, M. S., Qamar, M., & Usman, M. (2020). Cognitive bias and the extraversion personality shaping the behavior of investors. Frontiers in Psychology, 11, Article 556506. https://doi.org/10.3389/fpsyg.2020.556506

Juan, A. A., Keenan, P., Martí, R., McGarraghy, S., Panadero, J., Carroll, P., & Oliva, D. (2023). A review of the role of heuristics in stochastic optimisation: From metaheuristics to learn heuristics. Annals of Operations Research, 320(2), 831-861. https://doi.org/10.1007/s10479-021-04142-9

Kahneman, Daniel. 2003. “Experiences of Collaborative Research.” The American Psychologist 58 (9): 723–30. https://doi.org/10.1037/0003-066X.58.9.723.

Kappes, A., Harvey, A. H., Lohrenz, T., Montague, P. R., & Sharot, T. (2020). Confirmation bias in the utilization of others’ opinion strength. Nature Neuroscience, 23(1), 130-137. https://doi.org/10.1038/s41593-019-0549-2

Koninckx, P. R., Ussia, A., Stepanian, A., Saridogan, E., Malzoni, M., Miller, C. E., ... & Adamyan, L. (2025). The evidence-based medicine management of endometriosis should be updated for the limitations of trial evidence, the multivariability of decisions, collective experience, heuristics, and Bayesian thinking. Journal of Clinical Medicine, 14(1), 248. https://doi.org/10.3390/jcm14010248

Kuehl, L. K., Deuter, C. E., Nowacki, J., Ueberrueck, L., Wingenfeld, K., & Otte, C. (2021). Attentional bias in individuals with depression and adverse childhood experiences: influence of the noradrenergic system?. Psychopharmacology, 238(12), 3519-3531. https://doi.org/10.1007/s00213-021-05969-7

Kurjanska, M., & Milman, N. (2024). The more, the better? An active learning tool on unconscious bias in everyday decision-making. In Promoting Inclusion and Justice in University Teaching (pp. 76-89). Edward Elgar Publishing. https://doi.org/10.4337/9781035323456.00013

Lin, Y., Zhu, J. Q., & Sanborn, A. (2024). Bias in belief updating: Combining the Bayesian sampler with heuristics. In Proceedings of the Annual Meeting of the Cognitive Science Society (46). https://escholarship.org/uc/item/5142b5v1

Liu, B., Dow, P. K., Akroyd, C., & Sundaram, D. (2024). Enhancing decision making and mitigating cognitive biases: Design and implementation of a visualization education platform. https://authorconnect.aisnet.org/conferences/AMCIS2024/papers/1661

Liu, J., & Azzopardi, L. (2024, July). Search under uncertainty: Cognitive biases and heuristics: a tutorial on testing, mitigating and accounting for cognitive biases in search experiments. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 3013-3016). https://doi.org/10.1145/3626772.366138

Locke, J. M., Paradice, D., & Rainer, R. K. (2024). Mitigating bias through random activation function selection. Neural Computing and Applications, 36(6), 2983-2998. https://doi.org/10.1007/s00521-023-09178-5

Lockwood, P. L., van den Bos, W., & Dreher, J. C. (2024). Moral learning and decision-making across the lifespan. Annual Review of Psychology, 76. https://doi.org/10.1146/annurev-psych-021324-060611

Lois, G., Tsakas, E., Yuen, K., & Riedl, A. (2024). Tracking politically motivated reasoning in the brain: the role of mentalizing, value-encoding, and error detection networks. Social Cognitive and Affective Neuroscience, 19(1), nsae056. https://doi.org/10.1093/scan/nsae056

Love, P. E., Ika, L. A., & Pinto, J. K. (2024). Smart heuristics for decision-making in the ‘wild’: Navigating cost uncertainty in the construction of large-scale transport projects. Production Planning & Control, 35(16), 2286-2303. https://doi.org/10.1080/09537287.2023.2248942

Lv, X., Jia, Y., Brinthaupt, T. M., & Ren, X. (2024). Event-related potentials of belief-bias reasoning predict critical thinking. Journal of Educational Psychology. https://doi.org/10.1037/edu0000845

Machado, M., Assis, E. C., Souza, J. F., & Siqueira, S. W. M. (2024, May). A framework to support experimentation in the context of Cognitive Biases in Search as a Learning process. In Proceedings of the 20th Brazilian Symposium on Information Systems (pp. 1-9). https://doi.org/10.1145/3658271.3658310

Malatesta, D., & Siena, S. (2024). Observation and reporting: a teaching case on implicit bias and decision fallacies. Journal of Public Affairs Education, 1-17. https://doi.org/10.1145/3658271.3658310

Mandel, D. R., & Irwin, D. (2024). Beyond bias minimization: Improving intelligence with optimization and human augmentation. International Journal of Intelligence and CounterIntelligence, 37(2), 649-665. https://doi.org/10.1080/08850607.2023.2253120

Mavrogiorgos, K., Kiourtis, A., Mavrogiorgou, A., Menychtas, A., & Kyriazis, D. (2024). Bias in machine learning: A literature review. Applied Sciences, 14(19), 8860. https://doi.org/10.3390/app14198860

Mittermaier, M., Raza, M. M., & Kvedar, J. C. (2023). Bias in AI-based models for medical applications: challenges and mitigation strategies. NPJ Digital Medicine, 6(1), 113. https://doi.org/10.1038/s41746-023-00858-z

Mukherjee, A., & Chang, H. H. (2024). Heuristic reasoning in ai: Instrumental use and mimetic absorption. arXiv preprint arXiv:2403.09404. https://doi.org/10.48550/arXiv.2403.09404

Ng, I. K., Goh, W. G., & Lim, T. K. (2024). Beyond thinking fast and slow: a Bayesian intuitionist model of clinical reasoning in real-world practice. Diagnosis, (0). https://doi.org/10.1515/dx-2024-0169

Nguyen, H., Guo, C., & Homberg, J. R. (2020). Cognitive bias under adverse and rewarding conditions: A systematic review of rodent studies. Frontiers in Behavioral Neuroscience, 14, Article 14. https://doi.org/10.3389/fnbeh.2020.00014

Noworyta, K., Cieslik, A., & Rygula, R. (2021). Neuromolecular underpinnings of negative cognitive bias in depression. Cells, 10(11), Article 3157. https://doi.org/10.3390/cells10113157

Pan, J., Zhu, Z., Wang, J., Lin, A., & Caverlee, J. (2024, March). Countering mainstream bias via end-to-end adaptive local learning. In European Conference on Information Retrieval (pp. 75-89). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-56069-9_6

Peters, U. (2020) What Is the function of confirmation bias? Erkenntnis, 2020. https://doi.org/10.1007/s10670-020-00252-1

Rollwage, M., & Fleming, S. M. (2021). Confirmation bias is adaptive when coupled with efficient metacognition. Philosophical transactions of the Royal Society of London. Series B, Biological Sciences, 376(1822), Article 20200131. https://doi.org/10.1098/rstb.2020.0131

Saarinen, A., Jääskeläinen, I. P., Harjunen, V., Keltikangas-Järvinen, L., Jasinskaja-Lahti, I., & Ravaja, N. (2021). Neural basis of in-group bias and prejudices: A systematic meta-analysis. Neuroscience & Biobehavioral Reviews, 131, 1214-1227. https://doi.org/10.1016/j.neubiorev.2021.10.027

Sabaghypour, S., Farkhondeh Tale Navi, F., Kulkova, E., Abaduz, P., Zirak, N., & Nazari, M. A. (2024). The dark and bright side of the numbers: How emotions influence mental number line accuracy and bias. Cognition and Emotion, 38(5), 661-674. https://doi.org/10.1080/02699931.2023.2285834

Schirrmeister, E., Göhring, A. L., & Warnke, P. (2020). Psychological biases and heuristics in the context of foresight and scenario processes. Futures & Foresight Science, 2(2), Article e31. https://doi.org/10.1002/ffo2.31

Skagerlund, K., Forsblad, M., Slovic, P., & Västfjäll, D. (2020). The Affect Heuristic and Risk Perception - Stability Across Elicitation Methods and Individual Cognitive Abilities. Frontiers in Psychology, 11, Article 970. https://doi.org/10.3389/fpsyg.2020.00970

Suri, G., Slater, L. R., Ziaee, A., & Nguyen, M. (2024). Do large language models show decision heuristics similar to humans? A case study using GPT-3.5. Journal of Experimental Psychology: General. https://doi.org/10.1037/xge0001547

Surles, S., Noteboom, C., & El-Gayar, O. (2025). Human cognitive bias mitigation approaches to fairness within the machine learning value chain: A review and research agenda. https://hdl.handle.net/10125/109624

Tamburrini, G. (2024). The heuristics gap in AI ethics: impact on green AI policies and beyond. Journal of Responsible Technology, 100104. https://doi.org/10.1016/j.jrt.2024.100104

Tejani, A. S., Ng, Y. S., Xi, Y., & Rayan, J. C. (2024). Understanding and mitigating bias in imaging artificial intelligence. RadioGraphics, 44(5), e230067. https://doi.org/10.1148/rg.230067

Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., ... & Chaspari, T. (2023). A call to action on assessing and mitigating bias in artificial intelligence applications for mental health. Perspectives on Psychological Science, 18(5), 1062-1096. https://doi.org/10.1177/17456916221134490

Tversky, Amos, and Daniel Kahneman. 1974. “Judgment under Uncertainty: Heuristics and Biases.” Science 185 (4157): 1124–31. https://doi.org/10.1126/science.185.4157.1124.

Tversky, Amos, and Daniel Kahneman. 1981. “The Framing of Decisions and the Psychology of Choice.” Science 211 (4481): 453–58. https://doi.org/10.1126/science.7455683.

Tversky, Amos, and Daniel Kahneman. 1986. “Rational Choice and the Framing of Decisions.” The Journal of Business 59 (S4): S251–S278. https://doi.org/10.1086/296365.

Tversky, Amos, and Daniel Kahneman. 1992. “Advances in prospect theory: Cumulative representation of uncertainty.” Journal of Risk and Uncertainty 5 (4): 297–323. https://doi.org/10.1007/bf00122574.

van Brussel, S., Timmermans, M., Verkoeijen, P., & Paas, F. (2020). ‘Consider the opposite’–Effects of elaborative feedback and correct answer feedback on reducing confirmation bias–a pre-registered study. Contemporary Educational Psychology, 60, Article 101844. https://doi.org/10.1016/j.cedpsych.2020.101844

Wang, Y., Bao, W., Stupple, E. J., & Luo, J. (2024). Robust intuition? Exploring the difference in the strength of intuitions from perspective of attentional bias. Thinking & Reasoning, 30(1), 169-194. https://doi.org/10.1080/13546783.2023.2220972

Yang, S., Zhang, M., Xu, J., Wang, L., Li, Z., Zou, F., Wu, X., & Wang, Y. (2020). The electrophysiology correlation of the cognitive bias in anxiety under uncertainty. Scientific Reports, 10(1), Article 11354. https://doi.org/10.1038/s41598-020-68427-y

Zhang, J., Wei, L., Xu, Z., & Yao, Q. (2024, August). Heuristic learning with graph neural networks: A unified framework for link prediction. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4223-4231). https://doi.org/10.1145/3637528.367194

Zhao, Y., & Xie, B. (2020). Cognitive bias, entrepreneurial emotion, and entrepreneurship intention. Frontiers in Psychology, 11, Article 625. https://doi.org/10.3389/fpsyg.2020.00625

Other Resources

Kyi, T. L. (2020). This is your brain on stereotypes: How science is tackling unconscious bias. Kids Can Press Ltd.

Date of last update: 14-Dec-2022 CB

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