Synthese

, Volume 194, Issue 4, pp 1303–1322 | Cite as

Probabilistic coherence measures: a psychological study of coherence assessment

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Abstract

Over the years several non-equivalent probabilistic measures of coherence have been discussed in the philosophical literature. In this paper we examine these measures with respect to their empirical adequacy. Using test cases from the coherence literature as vignettes for psychological experiments we investigate whether the measures can predict the subjective coherence assessments of the participants. It turns out that the participants’ coherence assessments are best described by Roche’s (Insights from philosophy, jurisprudence and artificial intelligence, 2013) coherence measure based on Douven and Meijs’ (Synthese 156:405–425, 2007) average mutual support approach and the conditional probability.

Keywords

Bayesian coherentism Probabilistic coherence measures  Probabilistic support measures Test cases Experimental philosophy 

References

  1. Akiba, K. (2000). Shogenji’s probabilistic measure of coherence is incoherent. Analysis, 60, 356–359.CrossRefGoogle Scholar
  2. BonJour, L. (1985). The structure of empirical knowledge. Cambridge: Harvard University Press.Google Scholar
  3. Bovens, L., & Hartmann, S. (2003). Bayesian epistemology. Oxford: Oxford University Press.Google Scholar
  4. Carnap, R. (1950). Logical foundations of probability. Chicago: University of Chicago Press.Google Scholar
  5. Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367–405.CrossRefGoogle Scholar
  6. Cialdini, R. B., Trost, M. R., & Newsom, J. T. (1995). Preference for consistency: The development of a valid measure and the discovery of surprising behavioral implications. Journal of Personality and Social Psychology, 69, 318–328.CrossRefGoogle Scholar
  7. Crupi, V., Tentori, K., & Gonzales, M. (2007). On Bayesian measures of evidential support: Theoretical and empirical issues. Philosophy of Science, 74, 229–252.CrossRefGoogle Scholar
  8. Douven, I., & Meijs, W. (2007). Measuring coherence. Synthese, 156, 405–425.CrossRefGoogle Scholar
  9. Festa, R. (2012). For unto every one that hath shall be given. Matthew properties for incremental confirmation. Synthese, 184, 89–100.CrossRefGoogle Scholar
  10. Finch, H. A. (1960). Confirming power of observations metricized for decisions among hypotheses. Philosophy of Science, 27, 293–307.CrossRefGoogle Scholar
  11. Fitelson, B. (2004). Two technical corrections to my coherence measure. http://fitelson.org/coherence2.
  12. Fitelson, B. (2003). A probabilistic theory of coherence. Analysis, 63, 194–199.CrossRefGoogle Scholar
  13. Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19, 25–42.CrossRefGoogle Scholar
  14. Gaifman, H. (1979). Subjective probability, natural predicates and Hempel’s ravens. Erkenntnis, 21, 105–147.Google Scholar
  15. Glass, D. H. (2002). Coherence, explanation, and Bayesian networks. In O’Neill, M., Sutcliffe, R. F. E., Ryan, C., Eaton, M., & Griffith, N. J. L. (Eds.), Artificial intelligence and cognitive science. 13th Irish conference, AICS 2002, Limerick, Ireland, September 2002 (pp. 177–182). Berlin: Springer.Google Scholar
  16. Glass, D. H. (2005). Problems with priors in probabilistic measures of coherence. Erkenntnis, 63, 375–385.CrossRefGoogle Scholar
  17. Good, I. J. (1984). The best explicatum for weight of evidence. Journal of Statistical Computation and Simulation, 19, 294–299.CrossRefGoogle Scholar
  18. Greiner, B. (2004). An online recruitment system for economic experiments. In K. Kremer & V. Macho (Eds.), Forschung und wissenschaftliches Rechnen 2003, GWDG Bericht 63 (pp. 79–93). Goettingen: Ges. fuer Wiss. Datenverarbeitung.Google Scholar
  19. Harris, A., & Hahn, U. (2009). Bayesian rationality in evaluating multiple testimonies: Incorporating the role of coherence. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(5), 1366–1373.Google Scholar
  20. Jeffreys, H. (1961). Theory of probability. Oxford: Oxford University Press.Google Scholar
  21. Kemeny, J., & Oppenheim, P. (1952). Degrees of factual support. Philosophy of Science, 1952, 307–324.CrossRefGoogle Scholar
  22. Keynes, J. (1921). A treatise on probability. London: Macmillan.Google Scholar
  23. Kolmogorov, A. (1956). Foundations of the theory of probability. New York: AMS Chelsea Publishing.Google Scholar
  24. Koscholke, J. (2015). Evaluating test cases for probabilistic measures of coherence. Erkenntnis. doi:10.1007/s10670-015-9734-1.
  25. Kuipers, T. A. F. (2000). From instrumentalism to constructive realism. Dordrecht: Reidel.CrossRefGoogle Scholar
  26. Levi, I. (1962). Corroboration and rules of acceptance. British Journal for the Philosophy of Science, 13, 307–313.Google Scholar
  27. Meijs, W. (2005). Probabilistic measures of coherence. PhD thesis, Erasmus University, Rotterdam.Google Scholar
  28. Meijs, W. (2006). Coherence as generalized logical equivalence. Erkenntnis, 64, 231–252.CrossRefGoogle Scholar
  29. Meijs, W., & Douven, I. (2007). On the alleged impossibility of coherence. Synthese, 157(3), 347–360.CrossRefGoogle Scholar
  30. Mortimer, H. (1988). The logic of induction. Paramus: Prentice Hall.Google Scholar
  31. Nozick, R. (1981). Philosophical explanations. Oxford: Clarendon.Google Scholar
  32. Olsson, E. J. (2002). What is the problem of coherence and truth? The Journal of Philosophy, 94, 246–272.CrossRefGoogle Scholar
  33. Olsson, E. J. (2005). Against coherence: Truth, probability and justification. Oxford: Oxford University Press.CrossRefGoogle Scholar
  34. Pfeiffer, P. (1990). Probability for applications. New York: Springer.CrossRefGoogle Scholar
  35. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team (2013). nlme: Linear and nonlinear mixed effects models. http://CRAN.R-project.org/package=nlme.
  36. Popper, K. R. (1954). Degree of confirmation. British Journal for the Philosophy of Science, 5, 143–149.CrossRefGoogle Scholar
  37. R Core Team (2015). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
  38. Rescher, N. (1958). Theory of evidence. Philosophy of Science, 25, 83–94.CrossRefGoogle Scholar
  39. Rescher, N. (1973). The coherence theory of truth. Oxford: Oxford University Press.Google Scholar
  40. Rips, L. J. (2001). Two kinds of reasoning. Psychological Science, 12, 129–134.CrossRefGoogle Scholar
  41. Roche, W. (2013). Coherence and probability: A probabilistic account of coherence. In M. Araszkiewicz & J. Savelka (Eds.), Coherence: Insights from philosophy, jurisprudence and artificial intelligence (pp. 59–91). Dordrecht: Springer.CrossRefGoogle Scholar
  42. Schippers, M. (2014). Probabilistic measures of coherence: From adequacy constraints towards pluralism. Synthese, 191(16), 3821–3845.CrossRefGoogle Scholar
  43. Schippers, M., & Siebel, M. (2015). Inconsistency as a touchstone for coherence measures. Theoria, 30, 11–41.CrossRefGoogle Scholar
  44. Schupbach, J. N. (2011). New hope for Shogenji’s coherence measure. British Journal for the Philosophy of Science, 62(1), 125–142.CrossRefGoogle Scholar
  45. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.CrossRefGoogle Scholar
  46. Shogenji, T. (1999). Is coherence truth conducive? Analysis, 59, 338–345.CrossRefGoogle Scholar
  47. Shogenji, T. (2012). The degree of epistemic justification and the conjunction fallacy. Synthese, 184, 29–48.CrossRefGoogle Scholar
  48. Siebel, M. (2004). On Fitelson’s measure of coherence. Analysis, 64, 189–190.CrossRefGoogle Scholar
  49. Siebel, M., & Wolff, W. (2008). Equivalent testimonies as a touchstone of coherence measures. Synthese, 161, 167–182.CrossRefGoogle Scholar
  50. Wagenmakers, E. J. (2007). A practical solution to the pervasive problems of \(p\) values. Psychonomic Bulletin & Review, 14, 779–804.CrossRefGoogle Scholar
  51. Weller, J. A., Dieckmann, N. F., Tusler, M., Mertz, C. K., Burns, W. J., & Peters, E. (2013). Development and testing of an abbreviated numeracy scale: A Rasch analysis approach. Journal of Behavioral Decision Making, 26, 198–212.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Philosophy DepartmentUniversity of OldenburgOldenburgGermany
  2. 2.Institut für PsychologieFernUniversität in HagenHagenGermany

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