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So, What Are Cognitive Biases?

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Cognitive Biases in Visualizations

Abstract

Despite more than 40 years of research into the field and the increasing use of the term in the media, there is still some uncertainty and even mystery over cognitive biases. This chapter provides a background to the topic with the aim to clarify what is meant by cognitive biases. After introducing some uses and misuses of the term, examples of common biases are presented. This is followed by a brief history of the research in the area over the years which illustrates the continued debate on cognitive biases and decision-making. Work in the emerging field of cognitive biases in visualization, prior to this publication, is outlined which concerns both the interpretation of the visualization and the visualization tools, such as visual analytic systems. Finally, we discuss the challenging issue of debiasing - how to mitigate the undesirable impact of cognitive biases on judgments.

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Notes

  1. 1.

    A Google scholar search for “cognitive bias” reports 3000 in 2012 and 5480 in 2017.

  2. 2.

    The author’s own survey collected 288 distinct biases.

  3. 3.

    Full papers for DECISIVe 2014 are available [20].

References

  1. Ali N, Peebles D (2013) The effect of Gestalt laws of perceptual organization on the comprehension of three-variable bar and line graphs. Hum Factors 55(1):183–203

    Article  Google Scholar 

  2. Argenta C, Hale CR (2015) Analyzing variation of adaptive game-based training with event sequence alignment and clustering. In: Proceedings of the third annual conference on advances in cognitive systems poster collection, p 26

    Google Scholar 

  3. Arnott D (1998) A taxonomy of decision biases. Monash University, School of Information Management and Systems, Caulfield

    Google Scholar 

  4. Baron J (2008) Thinking and deciding, 4th ed

    Google Scholar 

  5. BBC (2014) Horizon: how we really make decisions. http://www.imdb.com/title/tt3577924/

  6. Brydges NM, Hall L (2017) A shortened protocol for assessing cognitive bias in rats. J Neurosci Methods 286:1–5

    Article  Google Scholar 

  7. Bush RM (2017) Serious play: an introduction to the sirius research program. SAGE Publications, Sage, CA: Los Angeles, CA

    Google Scholar 

  8. Business-Insider (2013) 57 cognitive biases that screw up how we think. http://www.businessinsider.com/cognitive-biases-2013-8

  9. Carter CR, Kaufmann L, Michel A (2007) Behavioral supply management: a taxonomy of judgment and decision-making biases. Int J Phys Distrib Logistics Manage 37(8):631–669

    Article  Google Scholar 

  10. Cho I, Wesslen R, Karduni A, Santhanam S, Shaikh S, Dou W (2017) The anchoring effect in decision-making with visual analytics. In: Visual analytics science and technology (VAST)

    Google Scholar 

  11. Cooper N, Da Silva A, Powell S (2016) Teaching clinical reasoning. ABC of clinical reasoning. Wiley Blackwell, Chichester, pp 44–50

    Google Scholar 

  12. Correll M, Gleicher M (2014) Error bars considered harmful: exploring alternate encodings for mean and error. IEEE Trans Visual Comput Graphics 20(12):2142–2151

    Article  Google Scholar 

  13. Croskerry P (2016) Our better angels and black boxes. BMJ Publishing Group Ltd and the British Association for Accident & Emergency Medicine

    Google Scholar 

  14. Croskerry P (2017) Cognitive and affective biases, and logical failures. Diagnosis: interpreting the shadows

    Google Scholar 

  15. Croskerry P, Singhal G, Mamede S (2013) Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ Qual Saf 2012

    Google Scholar 

  16. Daron JD, Lorenz S, Wolski P, Blamey RC, Jack C (2015) Interpreting climate data visualisations to inform adaptation decisions. Clim Risk Manage 10:17–26

    Article  Google Scholar 

  17. Dimara E, Bezerianos A, Dragicevic P (2017) The attraction effect in information visualization. IEEE Trans Visual Comput Graphics 23(1):471–480

    Article  Google Scholar 

  18. Dunbar NE, Miller CH, Adame BJ, Elizondo J, Wilson SN, Schartel SG, Lane B, Kauffman AA, Straub S, Burgon K, et al (2013) Mitigation of cognitive bias through the use of a serious game. In: Proceedings of the games learning society annual conference

    Google Scholar 

  19. Edwards W, Lindman H, Savage LJ (1963) Bayesian statistical inference for psychological research. Psychol Rev 70(3):193

    Article  Google Scholar 

  20. Ellis G (ed) (2014) DECISIVe 2014 : 1st workshop on dealing with cognitive biases in visualisations. IEEE VIS 2014, Paris, France. http://goo.gl/522HKh

  21. Ellis G, Dix A (2015) Decision making under uncertainty in visualisation? In: IEEE VIS2015. http://nbn-resolving.de/urn:nbn:de:bsz:352-0-305305

  22. Evans JSB (2008) Dual-processing accounts of reasoning, judgment, and social cognition. Annu Rev Psychol 59:255–278

    Article  Google Scholar 

  23. Fendley ME (2009) Human cognitive biases and heuristics in image analysis. PhD thesis, Wright State University

    Google Scholar 

  24. Fiedler K, von Sydow M (2015) Heuristics and biases: beyond Tversky and Kahnemans (1974) judgment under uncertainty. In: Cognitive psychology: Revisiting the classical studies, pp 146–161

    Google Scholar 

  25. George JF, Duffy K, Ahuja M (2000) Countering the anchoring and adjustment bias with decision support systems. Decis Support Syst 29(2):195–206

    Article  Google Scholar 

  26. Gigerenzer G (1996) On narrow norms and vague heuristics: A reply to Kahneman and Tversky

    Google Scholar 

  27. Gigerenzer G, Gaissmaier W (2011) Heuristic decision making. Annu Rev Psychol 62:451–482

    Article  Google Scholar 

  28. Gigerenzer G, Todd PM, ABC Research Group et al (1999) Simple heuristics that make us smart. Oxford University Press, Oxford

    Google Scholar 

  29. Gilovich T, Griffin D (2002) Introduction-heuristics and biases: then and now. Heuristics and biases: the psychology of intuitive judgment pp 1–18

    Google Scholar 

  30. Graber ML, Kissam S, Payne VL, Meyer AN, Sorensen A, Lenfestey N, Tant E, Henriksen K, LaBresh K, Singh H (2012) Cognitive interventions to reduce diagnostic error: a narrative review. BMJ Qual Saf

    Google Scholar 

  31. Green TM, Ribarsky W, Fisher B (2008) Visual analytics for complex concepts using a human cognition model. In: IEEE symposium on visual analytics science and technology, VAST’08, 2008. IEEE, New York, pp 91–98

    Google Scholar 

  32. Haselton MG, Bryant GA, Wilke A, Frederick DA, Galperin A, Frankenhuis WE, Moore T (2009) Adaptive rationality: an evolutionary perspective on cognitive bias. Soc Cogn 27(5):733–763

    Article  Google Scholar 

  33. Hernandez I, Preston JL (2013) Disfluency disrupts the confirmation bias. J Exp Soc Psychol 49(1):178–182

    Article  Google Scholar 

  34. Heuer RJ (1999) Psychology of intelligence analysis. United States Govt Printing Office.

    Google Scholar 

  35. Heuer RJ, Pherson RH (2010) Structured analytic techniques for intelligence analysis. Cq Press, Washington, D.C

    Google Scholar 

  36. Hilbert M (2012) Toward a synthesis of cognitive biases: how noisy information processing can bias human decision making. Psychol Bull 138(2):211

    Article  Google Scholar 

  37. Hogarth R (1987) Judgment and choice: the psychology of decision. Wiley, Chichester

    Google Scholar 

  38. IARPA (2013) Sirius program. https://www.iarpa.gov/index.php/research-programs/sirius

  39. Kahneman D (2011) Thinking, fast and slow. Macmillan, New York

    Google Scholar 

  40. Kahneman D, Frederick S (2002) Representativeness revisited: attribute substitution in intuitive judgment. Heuristics Biases Psychol Intuitive Judgment 49:81

    Google Scholar 

  41. Kahneman D, Tversky A (1996) On the reality of cognitive illusions. American Psychological Association

    Google Scholar 

  42. Keren G, Teigen KH (2004) Yet another look at the heuristics and biases approach. Blackwell handbook of judgment and decision making pp 89–109

    Google Scholar 

  43. Khan A, Breslav S, Glueck M, Hornbæk K (2015) Benefits of visualization in the mammography problem. Int J Hum-Comput Stud 83:94–113

    Article  Google Scholar 

  44. Kretz DR (2015) Strategies to reduce cognitive bias in intelligence analysis: can mild interventions improve analytic judgment? The University of Texas at Dallas

    Google Scholar 

  45. Kretz DR, Granderson CW (2013) An interdisciplinary approach to studying and improving terrorism analysis. In: 2013 IEEE international conference on intelligence and security informatics (ISI). IEEE, New York, pp 157–159

    Google Scholar 

  46. Kretz DR, Simpson B, Graham CJ (2012) A game-based experimental protocol for identifying and overcoming judgment biases in forensic decision analysis. In: 2012 IEEE conference on technologies for homeland Security (HST). IEEE, New York, pp 439–444

    Google Scholar 

  47. Manoogian J, Benson B (2017) Cognitive bias codex. https://betterhumans.coach.me/cognitive-bias-cheat-sheet-55a472476b18

  48. Meehl PE (1954) Clinical versus statistical prediction: a theoretical analysis and a review of the evidence

    Google Scholar 

  49. Micallef L, Dragicevic P, Fekete JD (2012) Assessing the effect of visualizations on Bayesian reasoning through crowdsourcing. IEEE Trans Visual Comput Graphics 18(12):2536–2545

    Article  Google Scholar 

  50. Miller S, Kirlik A, Kosorukoff A, Tsai J (2008) Supporting joint human-computer judgment under uncertainty. In: Proceedings of the human factors and ergonomics society annual meeting, vol 52. Sage, Los Angeles, pp 408–412

    Article  Google Scholar 

  51. Norman G (2014) The bias in researching cognitive bias. Adv Health Sci Educ 19(3):291–295

    Article  Google Scholar 

  52. Nussbaumer A, Verbert K, Hillemann EC, Bedek MA, Albert D (2016) A framework for cognitive bias detection and feedback in a visual analytics environment. In: 2016 European intelligence and security informatics conference (EISIC). IEEE, New York, pp 148–151

    Google Scholar 

  53. Peebles D (2008) The effect of emergent features on judgments of quantity in configural and separable displays. J Exp Psychol: Appl 14(2):85

    Google Scholar 

  54. Peebles D, Cheng PCH (2003) Modeling the effect of task and graphical representation on response latency in a graph reading task. Hum Factors 45(1):28–46

    Article  Google Scholar 

  55. Pinker S (1990) A theory of graph comprehension. Artificial intelligence and the future of testing pp 73–126

    Google Scholar 

  56. Pronin E, Lin DY, Ross L (2002) The bias blind spot: perceptions of bias in self versus others. Pers Soc Psychol Bull 28(3):369–381

    Article  Google Scholar 

  57. RECOBIA (2012) European Union RECOBIA project. http://www.recobia.eu

  58. Remus WE, Kottemann JE (1986) Toward intelligent decision support systems: an artificially intelligent statistician. MIS Q pp 403–418

    Google Scholar 

  59. Roelofs S, Boleij H, Nordquist RE, van der Staay FJ (2016) Making decisions under ambiguity: judgment bias tasks for assessing emotional state in animals. Frontiers Behav Neurosci 10:119

    Article  Google Scholar 

  60. Rudolph S, Savikhin A, Ebert DS (2009) Finvis: applied visual analytics for personal financial planning. In: IEEE symposium on visual analytics science and technology, 2009. VAST 2009. IEEE, New York, pp 195–202

    Google Scholar 

  61. Sacha D, Senaratne H, Kwon BC, Ellis G, Keim DA (2016) The role of uncertainty, awareness, and trust in visual analytics. IEEE Trans Visual Comput Graphics 22(1):240–249

    Article  Google Scholar 

  62. Schlüns H, Welling H, Federici JR, Lewejohann L (2017) The glass is not yet half empty: agitation but not Varroa treatment causes cognitive bias in honey bees. Anim Cogn 20(2):233–241

    Article  Google Scholar 

  63. Simon HA (1957) Models of man; social and rational. Wiley, New York

    MATH  Google Scholar 

  64. Soon CS, Brass M, Heinze HJ, Haynes JD (2008) Unconscious determinants of free decisions in the human brain. Nat Neurosci 11(5):543

    Article  Google Scholar 

  65. Stanovich KE, West RF (2000) Individual differences in reasoning: implications for the rationality debate? Behav Brain Sci 23(5):645–665

    Article  Google Scholar 

  66. Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185(4157):1124–1131

    Article  Google Scholar 

  67. Tversky B (1991) Distortions in memory for visual displays. Spatial instruments and spatial displays pp 61–75

    Google Scholar 

  68. Valdez AC, Ziefle M, Sedlmair M (2018) Priming and anchoring effects in visualization. IEEE Trans Visual Comput Graphics 24(1):584–594

    Article  Google Scholar 

  69. Verbeek E, Ferguson D, Lee C (2014) Are hungry sheep more pessimistic? the effects of food restriction on cognitive bias and the involvement of ghrelin in its regulation. Physiol Behav 123:67–75

    Article  Google Scholar 

  70. Virine L, Trumper M (2007) Project decisions: the art and science. Berrett-Koehler Publishers, Oakland

    Google Scholar 

  71. Wall E, Blaha LM, Franklin L, Endert A (2017) Warning, bias may occur: a proposed approach to detecting cognitive bias in interactive visual analytics. In: IEEE conference on visual analytics science and technology (VAST)

    Google Scholar 

  72. Wichman A, Keeling LJ, Forkman B (2012) Cognitive bias and anticipatory behaviour of laying hens housed in basic and enriched pens. Appl Anim Behav Sci 140(1):62–69

    Article  Google Scholar 

  73. Xiong C, van Weelden L, Franconeri S (2017) The curse of knowledge in visual data communication. In: Talk given at the information visualization research satellite event at vision sciences society annual meeting, St. Pete Beach, FL

    Google Scholar 

  74. Zacks J, Tversky B (1999) Bars and lines: a study of graphic communication. Memory Cogn 27(6):1073–1079

    Article  Google Scholar 

  75. Zhang J, Norman DA (1994) Representations in distributed cognitive tasks. Cogn Sci 18(1):87–122

    Article  Google Scholar 

  76. Zhang Y, Bellamy RK, Kellogg WA (2015) Designing information for remediating cognitive biases in decision-making. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems. ACM, New York, pp 2211–2220

    Google Scholar 

  77. Ziemkiewicz C, Kosara R (2010) Implied dynamics in information visualization. In: Proceedings of the international conference on advanced visual interfaces. ACM, New York, pp 215–222

    Google Scholar 

  78. Zuk T, Carpendale S (2007) Visualization of uncertainty and reasoning. In: International symposium on smart graphics. Springer, Berlin, pp 164–177

    Google Scholar 

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Authors and Affiliations

  1. Data Analysis and Visualization Group, University of Konstanz, Konstanz, Germany

    Geoffrey Ellis

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  1. Data Analysis and Visualisation Group, Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

    Geoffrey Ellis

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Ellis, G. (2018). So, What Are Cognitive Biases?. In: Ellis, G. (eds) Cognitive Biases in Visualizations. Springer, Cham. https://doi.org/10.1007/978-3-319-95831-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-95831-6_1

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