Minds and Machines

, Volume 8, Issue 1, pp 39–60

Learning Causes: Psychological Explanations of Causal Explanation1

  • Clark Glymour
Article

Abstract

I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, incorrectly let large causes ‘overshadow’ small causes, misrepresents the most likely, and warranted, causal explanation available to the subjects, in the light of which their responses were normative; (iii) showing how a recent psychological theory (due to P. Cheng) of human judgment of causal power can be considerably generalized: and (iv) suggesting a range of possible experiments comparing human and computer abilities to extract causal information from associations.

cause causation directed graphs explanation judgment under certainty 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahn, W. and Bailenson, J. (1996), ‘Causal Attribution as a Search for Underlying Mechanisms: An Explanation of the Conjunction Fallacy and the Discounting Principle’, Cognitive Psychology.Google Scholar
  2. Ahn, W., Kalish, C.W., Medin, D.L. and Gelman, S.A. (1995), ‘The Role of Covariation Versus Mechanism Information in Causal Attribution’, Cognition 54, pp. 299–352.Google Scholar
  3. Allan, L.G. (1980), ‘A Note on Measurements of Contingency Between Two Binary Variables in Judgment Tasks’. Bulletin of the Psychonomic Society 15, pp. 147–149.Google Scholar
  4. Allan, L.G. and Jenkins, H.M. (1983), ‘The Effect of Representations of Binary Variables on Judgment of Influence’. Learning and Motivation, 14, pp. 381–405.Google Scholar
  5. Anderson, J.R. and Sheu, C-F. (1995), ‘Causal Inferences as Perceptual Judgments’. Memory & Cognition 23, pp. 510–524.Google Scholar
  6. Baker, A.G., Mercier, P., Valle-Tourangeau, F., Frank, R. and Pan, M. (1993), ‘Selective Associations and Causality Judgments: The Presence of a Strong Causal Factor May Reduce Judgments of a Weaker One’, Journal of Experimental Psychology: Learning, Memory, and Cognition 19, pp. 414–432.Google Scholar
  7. Cheng, P.W. (1997), ‘From Covariation to Causation: A Causal Power Theory’. Psychological Review 104, pp. 367–405.Google Scholar
  8. Cheng, P.W. and Novick, L.R. (1992), ‘Covariation in Natural Causal Induction’. Psychological Review, 99, pp. 365–382.Google Scholar
  9. Glymour, C. and Cheng, P. W. Causal Mechanism and Probability: A Normative Approach’, in M. Oaksford and N. Chater (eds.), Rational Models of Cognition. Oxford, U.K.: Oxford University Press (in press).Google Scholar
  10. Harré, R. and Madden, E.H. (1975), Causal Powers: A Theory of Natural Necessity, Totowa, New Jersey: Rowman & Littlefield.Google Scholar
  11. Hashem, A.I. and Cooper, G.F. (1996), ‘Human Causal Discovery From Observational Data’. Proceedings of the 1996 symposium of the American Medical Information Association.Google Scholar
  12. Jacoby, L., Yonelinas, A. and Jennings, J., (1997), The Relation Between Conscious and Unconscious (Automatic) Influences: A Declaration of Independence, in J. Cohen and J. Schooler (eds.), Scientific Approaches to Consciousness., Mahwah, N.J., Lawrence Erlbaum Associates, pp. 13–47.Google Scholar
  13. Jenkins, H. and Ward, W. (1965), ‘Judgment of Contingency Between Responses and Outcomes’. Psychological Monographs 7, pp. 1–17.Google Scholar
  14. Pearl, J. (1988), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, California: Morgan Kaufmann.Google Scholar
  15. Pearl, J. (1995), ‘Causal Diagrams for Empirical Research’, Biomtrika 82(4), pp. 669–709.Google Scholar
  16. Price, P.C. and Yates, J.F. (1993), ‘Judgmental Overshadowing: Further Evidence of Cue Interaction in Contingency Judgment’. Memory & Cognition 21, pp. 561–572.Google Scholar
  17. Rescorla, R.A. (1968), ‘Probability of Shock in the Presence and Absence of CS in Fear Conditioning’. Journal of Comparative and Physiological Psychology 66, pp. 1–5.Google Scholar
  18. Shanks, D.R. (1995), ‘Is human learning rational?’ Quarterly Journal of Experimental Psychology, 48A, pp. 257–279.Google Scholar
  19. Shultz, T.R. (1982), ‘Rules of Causal Attribution’. Monographs of the Society for Research in Child Development, 47,(1).Google Scholar
  20. Spellman, B.A. (1996a), ‘Acting as Intuitive Scientists: Contingency Judgments are Made While Controlling for Alternative Potential Causes’. Psychological Science, 7, pp. 337–342.Google Scholar
  21. Spellman, B.A. (1996b), Conditionalizing causality, in D.R. Shanks, K.J. Holyoak, D.L. Medin (eds.), The Psychology of Learning and Motivation, vol 34: Causal learning (pp. 167–207). San Diego: Academic Press.Google Scholar
  22. Spirtes, P., Glymour, C. and Scheines, R. (1993), Causation, Prediction and Search, New York: Springer.Google Scholar
  23. Turner, M. (1987), Death is the Mother of Beauty: Mind, Metaphor, Criticism. Chicago: University of Chicago Press.Google Scholar
  24. Waldmann, M.R. and Holyoak, K.J. (1992), ‘Predictive and Diagnostic Learning Within Causal Models: Asymmetries in Cue Competition’. Journal of Experimental Psychology: General 121, pp. 222–236.Google Scholar
  25. White, P.A. (1989), ‘A Theory of Causal Processing’, British Journal of Psychology, 80, pp. 431–454.Google Scholar
  26. White, P.A. (1995), ‘Use of Prior Beliefs in the Assignment of Causal Roles: Causal Powers Versus Regularity-based Accounts’. Memory & Cognition, 23, pp. 243–254.Google Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Clark Glymour
    • 1
  1. 1.University of California at San Diego and Carnegie Mellon UniversityUSA

Personalised recommendations