Innate Reasoning and Critical Incident Decision-Making

  • Robin BryantEmail author


The author describes the theory behind intuitive and analytical decision making during investigations. Forms of reasoning are described (including their limitations) together with a brief overview of what the fields of neuropsychology and evolutionary psychology might be able to contribute to our understanding. We often make decisions based on a serial assessment of information and we choose the first available workable option that appears to satisfy our requirements. Decisions during major incidents often have to be made in quick time by exercising swift judgement by choosing between options (including not to act). Inevitably, complex situations have to be simplified in the human mind with the number of options considered at any one time severely limited and inferences rapidly drawn. Heuristics are often employed to facilitate this, which whilst often effective are also linked with a number of well-known cognitive biases.


Reasoning Evolutionary psychology Heuristics Cognitive biases and decision making Drawing inferences in decision making 


  1. Brighton, H., & Gigerenzer, G. (2012, July–September). Homo Heuristicus: Less-Is-More Effects in Adaptive Cognition. Malaysian Journal of Medical Sciences, 19(4), 6–16.Google Scholar
  2. College of Policing. (2015). Critical Incident Management Introduction and Types of Critical Incidents [Online]. Available at
  3. Cosmides, L., & Tooby, J. (1992). Cognitive Adaptations for Social Exchange. In J. Barkow, L. Cosmides, & J. Tooby (Eds.), The Adapted Mind: Evolutionary Psychology and the Generation of Culture. Oxford: Oxford University Press.Google Scholar
  4. Cosmides, L., Barrett, H., & Tooby, J. (2010). Adaptive Specializations, Social Exchange, and the Evolution of Human Intelligence. In J. Avise & F. Ayala (Eds.), In the Light of Evolution IV: The Human Condition. Washington, DC: The National Academies Press. CrossRefGoogle Scholar
  5. Denzin, N., & Lincoln, Y. (Eds.). (2011). The Sage Handbook of Qualitative Research. London: Sage.Google Scholar
  6. Donoso, M., Collins, A., & Koechlin, E. (2014). Foundations of Human Reasoning in the Prefrontal Cortex. Science, 344(6191), 1481–1486.CrossRefGoogle Scholar
  7. Eastwood, J., Snook, B., & Luther, K. (2012). What People Want From Their Professionals: Attitudes Toward Decision-Making Strategies. Journal of Behavioural Decision Making, 25, 458–468.CrossRefGoogle Scholar
  8. Eyre, M., & Alison, L. (2007). To Decide or Not to Decide: Decision Making and Decision Avoidance in Critical Incidents. In D. Carson, B. Milne, F. Pakes, K. Shalev, & A. Shawyer (Eds.), Applying Psychology to Criminal Justice. Chichester: Wiley.Google Scholar
  9. Eyre, M., & Alison, L. (2010). Investigative Decision Making. In J. Brown & E. Campbell (Eds.), The Cambridge Handbook of Forensic Psychology. Cambridge: Cambridge University Press.Google Scholar
  10. Findley, K., & Scott, M. (2006). The Multiple Dimensions of Tunnel Vision in Criminal Cases. Wisconsin Law Review [Online]. Available at
  11. Garrett, M. (2014). Complexity of Our Brain. Psychology Today [Online]. Available at
  12. Gilovich, T., Griffin, D., & Kahneman, D. (Eds.). (2002). Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge: Cambridge University Press.Google Scholar
  13. Hammerstein, P., & Stevens, J. (2012). Six Reasons for Invoking Evolution in Decision Theory in Evolution and the Mechanisms of Decision Making (Ernst Strüngmann Forum Report, Vol. 11, pp. 1–17). Cambridge, MA: MIT Press.CrossRefGoogle Scholar
  14. Hoffrage, U., & Gigerenzer, G. (1998). Using Natural Frequencies to Improve Diagnostic Reasoning. Academic Medicine, 73, 538–540.CrossRefGoogle Scholar
  15. Hoffrage, U., Krauss, S., Martignon, L., & Gigerenzer, G. (2015, October). Natural Frequencies Improve Bayesian Reasoning in Simple and Complex Inference Tasks. Frontiers in Psychology, 6, Article 1473.Google Scholar
  16. Johnson, D., Blumstein, D., Fowler, J., & Haselton, M. (2013, August). The Evolution of Error: Error Management, Cognitive Constraints, and Adaptive Decision-Making Biases. Trends in Ecology & Evolution, 28(8), 474–481.CrossRefGoogle Scholar
  17. Kahneman, D., & Tversky, A. (Eds.). (2000). Choices, Values and Frames. Cambridge: Cambridge University Press.Google Scholar
  18. Kenrick, D., Norman, P., & Butner, J. (2003). Dynamical Evolutionary Psychology: Individual Decision Rules and Emergent Social Norms. Psychological Review, 110(1), 3–28.CrossRefGoogle Scholar
  19. Koehler, D. & Harvey, N. (Eds.). (2004). Blackwell Handbook of Judgment & Decision Making. Oxford: Blackwell.Google Scholar
  20. Lipshitz, R., & Ben Shaul, O. (1997). Schemata and Mental Models in Recognition-Primed Decision Making. In C. Zsambok & G. Klein (Eds.), Naturalistic Decision Making. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  21. Lipton, P. (1991). Inference to the Best Explanation. London: Routledge.Google Scholar
  22. Magnani, L. (2001). Abduction, Reason, and Science. Dordrecht: Kluwer.CrossRefGoogle Scholar
  23. Mata, R., Pachur, T., von Helversen, B., Rieskamp, J., & Schooler, L. (2012). Ecological Rationality: A Framework for Understanding and Aiding the Aging Decision Maker. Frontiers in Neuroscience, 6, 19.CrossRefGoogle Scholar
  24. Meder, B., & Gigerenzer, G. (2014). Statistical Thinking: No One Left Behind. In E. Chernoff & B. Sriraman (Eds.), Probabilistic Thinking, Advances in Mathematics Education. Dordrecht: Springer Science & Business Media.Google Scholar
  25. Mellers, B. (1996, March). From the President Society for Judgment and Decision Making. Newsletter, XV(1) [Online]. Available at
  26. Mousavi, S., & Gigerenzer, G. (2014, August). Risk, Uncertainty, and Heuristics. Journal of Business Research, 67(8), 1671–1678.CrossRefGoogle Scholar
  27. Orquin, J. L., & Kurzban, R. (2016). A Meta-analysis of Blood Glucose Effects on Human Decision Making. Psychological Bulletin, 142(5), 546–567.CrossRefGoogle Scholar
  28. Patokorpi, E. (2007). Logic of Sherlock Holmes in Technology Enhanced Learning. Educational Technology & Society, 10(1), 171–185.Google Scholar
  29. Pease, K., & Roach, J. (2017). How to Morph Experience into Evidence. In J. Knuttson & L. Tompson (Eds.), Advances in Evidence Based Policing (pp. 84–97). London: Routledge (ISBN 978-1-138 69873).CrossRefGoogle Scholar
  30. Pinker, S. (2003). How the Mind Works. London: Penguin Books.Google Scholar
  31. Roach, J., & Pease, K. (2013). Evolution and Crime. London: Routledge.CrossRefGoogle Scholar
  32. Salet, R., & Terpstra, J. (2013). Critical Review in Criminal Investigation: Evaluation of a Measure to Prevent Tunnel Vision. Policing, 8(1), 43–50.CrossRefGoogle Scholar
  33. Schum D., & Starace, S. (1994). The Evidential Foundations of Probabilistic Reasoning. Chichester: Wiley.Google Scholar
  34. Schurz, G. (2008). Patterns of Abduction. Syntheses, 164, 201–234.CrossRefGoogle Scholar
  35. Sharps, M. (2010). Processing Under Pressure: Stress, Memory and Decision-Making in Law Enforcement. Flushing, NY: Looseleaf Law Publications.Google Scholar
  36. Snook, B., & Cullen, R. (2009). Bounded Rationality and Criminal Investigations: Has Tunnel Vision Been Wrongfully Convicted? In K. Rossmo (Ed.), Criminal Investigative Failures. London: CRC Press.CrossRefGoogle Scholar
  37. Staller, M., & Zaiser, B. (2015). Developing Problem Solvers: New Perspectives on Pedagogical Practices in Police Use of Force Training. Journal of Law Enforcement, 4(3).Google Scholar
  38. Starckea, K., & Branda, M. (2012, April). Decision Making Under Stress: A Selective Review. Neuroscience & Biobehavioral Reviews, 36(4), 1228–1248.CrossRefGoogle Scholar
  39. Todd, P., & Gigerenzer, G. (1999). Simple Heuristics That Make Us Smart [Online]. Available at

Copyright information

© The Author(s) 2019

Authors and Affiliations

  1. 1.School of Law and Criminal JusticeCanterbury Christ Church UniversityCanterburyUK

Personalised recommendations