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Inference to the Best Explanation (IBE) Versus Explaining for the Best Inference (EBI)

Abstract

In pedagogical contexts and in everyday life, we often come to believe something because it would best explain the data. What is it about the explanatory endeavor that makes it essential to everyday learning and to scientific progress? There are at least two plausible answers. On one view, there is something special about having true explanations. This view is highly intuitive: it’s clear why true explanations might improve one’s epistemic position. However, there is another possibility—it could be that the process of seeking, generating, or evaluating explanations itself puts one in a better epistemic position, even when the outcome of the process is not a true explanation. In other words, it could be that accurate explanations are beneficial, or it could be that high-quality explaining is beneficial, where there is something about the activity of looking for an explanation that improves our epistemic standing. The main goal of this paper is to tease apart these two possibilities, both theoretically and empirically, which we align with “Inference to the Best Explanation” (IBE) and “Explaining for the Best Inference” (EBI), respectively. We also provide some initial support for EBI and identify promising directions for future research.

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Notes

  1. 1.

    For purposes of this paper, we treat “accurate,” “true,” and “right” explanations as roughly interchangeable. This begs certain questions—particularly as pertains to the debate about scientific realism and anti-realism—but these issues are not directly relevant to the distinction between IBE and EBI.

  2. 2.

    This use of “EBI” is distinct from the use expounded in Persson (2007).

  3. 3.

    This point was made to us by an anonymous reviewer.

  4. 4.

    Even on our expanded notion, EBI and IBE are not quite exhaustive of the way explanations could be of cognitive value—it is possible that the product of explanations could be valuable for some reason aside from their truth, while also not as a function of the explanatory reasoning process that led to them. For instance, one could believe that having any explanation, regardless of its truth, is what’s important. This view combines the focus on explanations from IBE with the tolerance for inaccuracy from EBI.

  5. 5.

    On the former account, IBE helps us by pointing us to the laws and initial and boundary conditions governing a system, and on the latter account, it helps us by pointing to assumptions and argument patterns that would unify our overall knowledge store. (For a review of those accounts, see Woodward 2014).

  6. 6.

    Mechanists rarely claim that mechanisms account for all causal relations, though.

  7. 7.

    To be clear, this taxonomy of causal accounts is not meant to be exhaustive; as the details of the accounts are not relevant for our analysis, a comprehensive review is unnecessary.

  8. 8.

    It is worth pointing out that the remarks about the unimportance of the act of explaining extend to any theory that takes the explanation as the only valuable product of the explaining act. In other words, everything we have said about IBE in the narrow sense applies to the broader class of explanation-acquisition discussed in Sect. 1.

  9. 9.

    Achinstein and Wilkenfeld’s accounts are still open to the possibility that the best explanations are the best as a result of having some particular explanatory virtues (on Wilkenfeld’s 2013, representation-centric approach, natural candidates would be the accuracy and fecundity of the representational content of the explanation). Thus, their accounts do not preclude evaluating explanations in terms of their more traditional virtues, but do allow for an additional class of considerations.

  10. 10.

    Of course, hitting on false explanations won’t always be beneficial; conditions that have a net epistemic benefit when explanations are false may be rare, even if they are important in providing evidence for EBI. For instance, we know that in some cases explaining can actually impair learning, and that this appears to be in part because people perseverate in making judgments on the basis of false explanations. See for example Williams et al. (2013) on how prompts to explain in uncooperative worlds can actually impede learning.

  11. 11.

    One anonymous reviewer pointed out to us that several thinkers (e.g., Papineau 1993 Sect. 5.11) have argued that this sort of rule circularity is perfectly reasonable. If this is right, then there is no problem for EBI either, and so much the better. However, our own inclination is that such seemingly question-begging responses should be adopted only as a last resort (though perhaps “last resort” accurately reflects our present place in the dialectic).

  12. 12.

    Briefly: such justification cannot advert solely to the a priori, since our conclusion has to do with the actual contingent arrangement of the world, but it cannot be based on prior experience, since the validity of extending lessons learned from prior experience to unseen cases is the very thing in question.

  13. 13.

    We thank an anonymous reviewer for drawing our attention to this comparison.

References

  1. Achinstein, P. (1983). The nature of explanation. New York: Oxford University Press.

    Google Scholar 

  2. Bonawitz, E. B., & Lombrozo, T. (2012). Occam’s rattle: Children’s use of simplicity and probability to constrain inference. Developmental Psychology, 48, 1156.

    Article  Google Scholar 

  3. Bromberger, S. (1966). Why-questions. In R. G. Colodny (Ed.), Mind and cosmos: Essays in contemporary science and philosophy (pp. 86–110). Pittsburg: University of Pittsburg Press.

    Google Scholar 

  4. Chi, M. T., de Leeuw, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.

    Google Scholar 

  5. Chi, M. T., Roy, M., & Hausmann, R. G. (2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive Science, 32, 301–341.

    Article  Google Scholar 

  6. Chi, M. T., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25, 471–533.

    Article  Google Scholar 

  7. Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243.

    Article  Google Scholar 

  8. Craver, C. F., & Ohio Library and Information Network. (2007). Explaining the brain. Oxford: Clarendon Press.

    Book  Google Scholar 

  9. Duncan, R. G., & Gotwals, A. W. (2015). A tale of two progressions: On the benefits of careful comparisons. Science Education, 99, 410–416.

    Article  Google Scholar 

  10. Duncan, R. G., Rogat, A. D., & Yarden, A. (2009). A learning progression for deepening students’ understandings of modern genetics across the 5th–10th grades. Journal of Research in Science Teaching, 46, 655–674.

    Article  Google Scholar 

  11. Duschl, R. A. (1990). Restructuring science education: The importance of theories and their development. New York: Teachers College Press.

    Google Scholar 

  12. Edwards, B. J., Williams, J. J., & Lombrozo, T. (2013). Effects of explanation and comparison on category learning. In Proceedings of the 35th annual conference of the cognitive science society. Austin, TX: Cognitive Science Society.

  13. Eggert, S., & Bögeholz, S. (2010). Students’ use of decision-making strategies with regard to socioscientific issues: An application of the rasch partial credit model. Science Education, 94, 230–258.

    Google Scholar 

  14. Friedman, M. (1974). Explanation and scientific understanding. The Journal of Philosophy, 71, 5–19.

    Article  Google Scholar 

  15. Garfinkel, A. (1980). Forms of explanation. New Haven: Yale University Press.

    Google Scholar 

  16. Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52(1), 45.

    Article  Google Scholar 

  17. Gopnik, A. (1998). Explanation as orgasm. Minds and Machines, 8(1), 101–118.

    Article  Google Scholar 

  18. Gotwals, A. W., & Songer, N. B. (2010). Reasoning up and down a food chain: Using an assessment framework to investigate students’ middle knowledge. Science Education, 94, 259–281.

  19. Hammer, D., & Sikorski, T. (2015). Implications of complexity for research on learning progressions. Science Education, 99, 424–431.

    Article  Google Scholar 

  20. Harman, G. H. (1965). The inference to the best explanation. The Philosophical Review, 74(1), 88–95.

    Article  Google Scholar 

  21. Hastie, R., & Pennington, N. (2000). Explanation-based decision making. In T. Connolly, H. R. Arkes & K. R. Hammond (Eds.), Judgment and decision making: An interdisciplinary reader (2nd ed., pp. 212–228). New York: Cambridge University Press.

    Google Scholar 

  22. Hume, D. (2000). An enquiry concerning human understanding: A critical edition. Oxford: Oxford University Press.

    Google Scholar 

  23. Jefferys, W. H., & Berger, J. O. (1992). Ockham’s razor and Bayesian analysis. American Scientist, 80, 64–72.

  24. Kelly, K. T. (2007). How simplicity helps you find the truth without pointing at it. In V. Harazinov, M. Friend, & N. Goethe (Eds.), Induction, algorithmic learning theory, and philosophy (pp. 111–143) Berlin: Springer.

  25. Legare, C., & Lombrozo, T. (2014). The selective benefits of explanation on learning in early childhood. Journal of Experimental Child Psychology, 126, 198–212.

    Article  Google Scholar 

  26. Lehrer, R., & Schauble, L. (2006). Scientific thinking and science literacy. In K. A. Renninger, I. E. Sigel, W. Damon & R. M. Lerner (Eds.), Handbook of child psychology (Vol. 4, pp. 153–196). Hoboken: Wiley.

  27. Lehrer, R., & Schauble, L. (2015). Learning progressions: The whole world is NOT a stage. Science Education, 99, 432–437.

    Article  Google Scholar 

  28. Lipton, P. (2004). Inference to the best explanation. London: Routledge.

    Google Scholar 

  29. Lombrozo, T. (2006). The structure and function of explanations. Trends in Cognitive Sciences, 10, 464–470.

    Article  Google Scholar 

  30. Lombrozo, T. (2007). Simplicity and probability in causal explanation. Cognitive Psychology, 55, 232–257.

    Article  Google Scholar 

  31. Lombrozo, T. (2012). Explanation and abductive inference. In K. J. Holyoak & R. G. Morrison (Eds.), Oxford Handbook of Thinking and Reasoning, (pp. 260–276). Oxford: Oxford University Press.

  32. McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38(1), 1–30.

    Article  Google Scholar 

  33. Papineau, D. (1993). Philosophical naturalism. Oxford: Blackwell.

  34. Pennington, N., & Hastie, R. (1986). Evidence evaluation in complex decision making. Journal of Personality and Social Psychology, 51, 242.

    Article  Google Scholar 

  35. 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.

    Article  Google Scholar 

  36. Pennington, N., & Hastie, R. (1992). Explaining the evidence: Tests of the story model for juror decision making. Journal of Personality and Social Psychology, 62(2), 189.

    Article  Google Scholar 

  37. Persson, J. (2007). IBE and EBI. In J. Persson & P. Ylikoski (Eds.), Rethinking explanation (pp. 137–147). Berlin: Springer.

    Chapter  Google Scholar 

  38. Quine, W. V. O. (1951). Two dogmas of empiricism. Philosophical Review, 60(1), 20–43.

    Article  Google Scholar 

  39. Renkl, A., & Atkinson, R. K. (2002). Learning from examples: Fostering self-explanations in computer-based learning environments. Interactive Learning Environments, 10(2), 105–119.

    Article  Google Scholar 

  40. Rozenblit, L., & Keil, F. (2002). The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science, 26, 521–562.

    Article  Google Scholar 

  41. Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton, NJ: Princeton University Press.

    Google Scholar 

  42. Schank, R. C. (2011). Teaching minds: How cognitive science can save our schools. New York: Teachers College Press.

    Google Scholar 

  43. Sobel, D. M., Yoachim, C. M., Gopnik, A., Meltzoff, A. N., & Blumenthal, E. J. (2007). The blicket within: Preschoolers’ inferences about insides and causes. Journal of Cognition and Development, 8(2), 159–182.

    Article  Google Scholar 

  44. Van Fraassen, B. C. (1980). The scientific image. Oxford; New York: Clarendon Press; Oxford University Press.

    Book  Google Scholar 

  45. Walker, C. M., Lombrozo, T., Legare, C. H., & Gopnik, A. (2014). Explaining prompts children to privilege inductively rich properties. Cognition, 133, 343–357.

    Article  Google Scholar 

  46. Wilkenfeld, D. A. (2013). Understanding as representation manipulability. Synthese, 190(6), 997–1016.

    Article  Google Scholar 

  47. Wilkenfeld, D. A. (2014). Functional explaining: A new approach to the philosophy of explanation. Synthese, 191(14), 3367–3391.

    Article  Google Scholar 

  48. Williams, J. J., & Lombrozo, T. (2010). The role of explanation in discovery and generalization: Evidence from category learning. Cognitive Science, 34, 776–806.

    Article  Google Scholar 

  49. Williams, J. J., & Lombrozo, T. (2013). Explanation and prior knowledge interact to guide learning. Cognitive Psychology, 66(1), 55–84.

    Article  Google Scholar 

  50. Williams, J. J., Lombrozo, T., & Rehder, B. (2013). The hazards of explanation: Overgeneralization in the face of exceptions. Journal of Experimental Psychology: General, 142(4), 1006.

    Article  Google Scholar 

  51. Wittgenstein, L., & Anscombe, G. E. M. (2001). Philosophical investigations: The german text, with a revised English translation [Philosophische Untersuchungen. English and German] (3rd ed.). Oxford: Blackwell.

    Google Scholar 

  52. Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press.

    Google Scholar 

  53. Woodward, J. (2014). Scientific explanation. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. Stanford: Stanford University

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Acknowledgments

We would like to thank the University of California, Berkeley, the John Templeton Foundation Varieties of Understanding project, the McDonnell Scholar Award, and NSF Grant DRL-1056712 (to Tania Lombrozo) for support during the writing of this paper.

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Correspondence to Daniel A. Wilkenfeld.

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Wilkenfeld, D.A., Lombrozo, T. Inference to the Best Explanation (IBE) Versus Explaining for the Best Inference (EBI). Sci & Educ 24, 1059–1077 (2015). https://doi.org/10.1007/s11191-015-9784-4

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Keywords

  • Good Explanation
  • True Belief
  • Internal Component
  • Cognitive Benefit
  • Causal Account