Evaluating everyday explanations

  • Jeffrey C. Zemla
  • Steven Sloman
  • Christos Bechlivanidis
  • David A. Lagnado
Brief Report

Abstract

People frequently rely on explanations provided by others to understand complex phenomena. A fair amount of attention has been devoted to the study of scientific explanation, and less on understanding how people evaluate naturalistic, everyday explanations. Using a corpus of diverse explanations from Reddit’s “Explain Like I’m Five” and other online sources, we assessed how well a variety of explanatory criteria predict judgments of explanation quality. We find that while some criteria previously identified as explanatory virtues do predict explanation quality in naturalistic settings, other criteria, such as simplicity, do not. Notably, we find that people have a preference for complex explanations that invoke more causal mechanisms to explain an effect. We propose that this preference for complexity is driven by a desire to identify enough causes to make the effect seem inevitable.

Keywords

Causal reasoning Knowledge Explanation 

Supplementary material

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References

  1. Ahn, W. K., & Bailenson, J. (1996). Causal attribution as a search for underlying mechanisms: an explanation of the conjunction fallacy and the discounting principle. Cognitive Psychology, 31(1), 82–123.CrossRefPubMedGoogle Scholar
  2. Aquinas, T. (1945) Basic Writings of St. Thomas Aquinas, trans. A.C. Pegis, New York: Random House.Google Scholar
  3. Bechlivanidis, C., Lagnado, D. A., Zemla, J. C., & Sloman, S. (2017). Concreteness and abstraction in everyday explanation. (In press).Google Scholar
  4. Bovens, L., & Olsson, E. J. (2000). Coherentism, reliability and Bayesian networks. Mind, 109(436), 685–719.CrossRefGoogle Scholar
  5. Byrne, M. D. (1995). The convergence of explanatory coherence and the story model: a case study in juror decision. In J. D. Moore & J. F. Lehman (Eds.), Proceedings of the Seventeenth Annual Meeting of the Cognitive Science Society (pp. 539–543). Mahwah, NJ: Erlbaum.Google Scholar
  6. Carruthers, P. (2006). The architecture of the mind. New York, NY: Oxford University Press.CrossRefGoogle Scholar
  7. Cimpian, A., & Salomon, E. (2014). The inherence heuristic: An intuitive means of making sense of the world, and a potential precursor to psychological essentialism. Behavioral and Brain Sciences, 37(05), 461–480.CrossRefPubMedGoogle Scholar
  8. Dray, W. H. (2000). Explanation in history. In J. H. Fetzer (Ed.), Science, Explanation, and Rationality: Aspects of the Philosophy of Carl G (pp. 217–242). Hempel, Oxford: Oxford University Press.Google Scholar
  9. Eriksson, K. (2012). The nonsense math effect. Judgment and Decision Making, 7(6), 746–749.Google Scholar
  10. Fernbach, P. M., Darlow, A., & Sloman, S. A. (2010). Neglect of alternative causes in predictive but not diagnostic reasoning. Psychological Science, 21(3), 329–336.CrossRefPubMedGoogle Scholar
  11. Flesch, R. (1948). A new readability yardstick. Journal of Applied Psychology, 32(3), 221–233.CrossRefPubMedGoogle Scholar
  12. Fricker, E. (2002). Trusting others in the sciences: A priori or empirical warrant? Studies in History and Philosophy of Science Part A, 33(2), 373–383.CrossRefGoogle Scholar
  13. Glymour, C. (2014). Probability and the Explanatory Virtues. The British Journal for the Philosophy of Science, axt051, 1–14.Google Scholar
  14. Harman, G. H. (1965). The inference to the best explanation. The Philosophical Review, 88–95.Google Scholar
  15. Hempel, C. G. (1965). Inductive-statistical explanation. In Aspects of scientific explanation (pp. 381–403). New York, NY: Free.Google Scholar
  16. Hirt, E. R., & Markman, K. D. (1995). Multiple explanation: A consider-an-alternative strategy for debiasing judgments. Journal of Personality and Social Psychology, 69(6), 1069–1086.CrossRefGoogle Scholar
  17. Johnson-Laird, P. N., Girotto, V., & Legrenzi, P. (2004). Reasoning from inconsistency to consistency. Psychological Review, 111(3), 640–661.CrossRefPubMedGoogle Scholar
  18. Keil, F. C. (2006). Explanation and understanding. Annual Review of Psychology, 57, 227–254.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Keil, F. C., Stein, C., Webb, L., Billings, V. D., & Rozenblit, L. (2008). Discerning the division of cognitive labor: An emerging understanding of how knowledge is clustered in other minds. Cognitive Science, 32(2), 259–300.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Kelemen, D., & Rosset, E. (2009). The human function compunction: Teleological explanation in adults. Cognition, 111(1), 138–143.CrossRefPubMedGoogle Scholar
  21. Khemlani, S. S., & Johnson-Laird, P. N. (2011). The need to explain. The Quarterly Journal of Experimental Psychology, 64(11), 2276–2288.CrossRefPubMedGoogle Scholar
  22. Khemlani, S. S., Sussman, A. B., & Oppenheimer, D. M. (2011). Harry Potter and the sorcerer's scope: Latent scope biases in explanatory reasoning. Memory & Cognition, 39(3), 527–535.CrossRefGoogle Scholar
  23. Kincaid, J. P., Fishburne Jr, R. P., Rogers, R. L., & Chissom, B. S. (1975). Derivation of new readability formulas (automated readability index, fog count and Flesch reading ease formula) for Navy enlisted personnel (No. RBR-8-75). Naval Technical Training Command Millington TN Research Branch.Google Scholar
  24. Kitcher, P. (1989). Explanatory unification and the causal structure of the world. Scientific Explanation, 13, 410–505.Google Scholar
  25. Kuhn, D. (1991). The skills of argument. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
  26. Lipton, P. (2004). Inference to the best explanation (2mdth ed.). Oxford: Oxford University Press.Google Scholar
  27. Lombrozo, T. (2007). Simplicity and probability in causal explanation. Cognitive Psychology, 55(3), 232–257.CrossRefPubMedGoogle Scholar
  28. Lombrozo, T. (2011). The instrumental value of explanations. Philosophy Compass, 6(8), 539–551.CrossRefGoogle Scholar
  29. Lombrozo, T., & Vasilyeva, N. (2017). Causal explanation. In M. Waldmann (Ed.), Oxford Handbook of Causal Reasoning. Oxford: Oxford University Press.Google Scholar
  30. Mackonis, A. (2013). Inference to the best explanation, coherence and other explanatory virtues. Synthese, 190(6), 975–995.CrossRefGoogle Scholar
  31. Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92(3), 289–316.CrossRefPubMedGoogle Scholar
  32. Oppenheimer, D. M. (2006). Consequences of erudite vernacular utilized irrespective of necessity: Problems with using long words needlessly. Applied Cognitive Psychology, 20(2), 139–156.CrossRefGoogle Scholar
  33. Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009). Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of Experimental Social Psychology, 45(4), 867–872.CrossRefGoogle Scholar
  34. Pacer, M., Williams, J., Xi, C., Lombrozo, T., & Griffiths, T. L. (2013). Evaluating computational models of explanation using human judgments. Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence. arXiv:1309.6855 [cs.AI]Google Scholar
  35. Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5(5), 411–419.Google Scholar
  36. Patterson, R., Operskalski, J. T., & Barbey, A. K. (2015). Motivated explanation. Frontiers in Human Neuroscience, 9, 1–15.CrossRefGoogle Scholar
  37. Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Francisco: Kaufmann.Google Scholar
  38. Pennington, N., & Hastie, R. (1986). Evidence evaluation in complex decision making. Journal of Personality and Social Psychology, 51(2), 242–258.CrossRefGoogle Scholar
  39. Pennington, N., & Hastie, R. (1988). Explanation-based decision making: Effects of memory structure on judgment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(3), 521–533.Google Scholar
  40. Read, S. J., & Marcus-Newhall, A. (1993). Explanatory coherence in social explanations: A parallel distributed processing account. Journal of Personality and Social Psychology, 65(3), 429–447.CrossRefGoogle Scholar
  41. Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton, NJ: Princeton University Press.Google Scholar
  42. Salmon, W. C. (2001). Reflections of a bashful Bayesian: a reply to Peter Lipton. In Explanation (pp. 121–136). Springer Netherlands.Google Scholar
  43. Schank, P., & Ranney, M. (1992). Assessing explanatory coherence: a new method for integrating verbal data with models of on-line belief revision. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society (pp. 599–604). Hillsdale, NJ: Erlbaum.Google Scholar
  44. Sloman, S. (2005). Causal models: How people think about the world and its alternatives. New York: Oxford University Press.CrossRefGoogle Scholar
  45. Sloman, S. A., & Lagnado, D. (2015). Causality in thought. Annual Review of Psychology, 66, 223–247.Google Scholar
  46. Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312.Google Scholar
  47. Strevens, M. (2007). Why explanations lie: Idealization in explanation. Unpublished Manuscript. Retrieved from http://www.strevens.org/research/expln/Idealization.pdf
  48. Strevens, M. (2008). Depth: An account of scientific explanation. Harvard University Press.Google Scholar
  49. Thagard, P. R. (1978). The best explanation: Criteria for theory choice. The Journal of Philosophy, 1978, 76–92.CrossRefGoogle Scholar
  50. Thagard, P. (1989). Explanatory coherence. Behavioral and Brain Sciences, 12, 435–502.CrossRefGoogle Scholar
  51. The World Bank, World Development Indicators (2015). Death rate, crude (per 1,000 people) [Data file]. Retrieved from http://data.worldbank.org/indicator/SP.DYN.CDRT.IN
  52. Vasilyeva, N., & Lombrozo, T. (2015). Explanations and causal judgments are differentially sensitive to covariation and mechanism information. In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P. Maglio (Eds.), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (pp. 2475–2480). Austin, TX: Cognitive Science Society.Google Scholar
  53. Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2008). The seductive allure of neuroscience explanations. Journal of Cognitive Neuroscience, 20(3), 470–477.CrossRefPubMedPubMedCentralGoogle Scholar
  54. Weisberg, D. S., Taylor, J. C., & Hopkins, E. J. (2015). Deconstructing the seductive allure of neuroscience explanations. Judgment and Decision Making, 10(5), 429–441.Google Scholar
  55. Woodward, J., & Hitchcock, C. (2003). Explanatory generalizations, part I: A counterfactual account. Noûs, 37(1), 1–24.CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  • Jeffrey C. Zemla
    • 1
  • Steven Sloman
    • 1
  • Christos Bechlivanidis
    • 2
  • David A. Lagnado
    • 2
  1. 1.Department of Cognitive, Linguistic, and Psychological SciencesBrown UniversityProvidenceUSA
  2. 2.University College LondonLondonUK

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