Assessing Interactive Causal Influence

Psychological Review

Volumn 111, Issue 2, 2004, Pages 455-485

  Novick, Laura R ^a,c   Cheng, Patricia W ^b  

^a VANDERBILT UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c VANDERBILT UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; HUMAN; MOTIVATION; PSYCHOLOGICAL MODEL; PSYCHOLOGICAL THEORY; STATISTICAL MODEL;

HUMANS; MODELS, PSYCHOLOGICAL; MODELS, STATISTICAL; MOTIVATION; PSYCHOLOGICAL THEORY;

EID: 1942539330     PISSN: 0033295X     EISSN: None     Source Type: Journal    
DOI: 10.1037/0033-295X.111.2.455     Document Type: Article

References (58)

1. Ahn, W., Kalish, C. W., Medin, D. L., & Gelman, S. A. (1995). The role of covariation versus mechanism information in causal attribution. Cognition, 54, 299-352.
2. Anderson, J. R., & Sheu, C.-F. (1995). Causal inferences as perceptual judgments. Memory & Cognition, 23, 510-524.
3. Bliss, C. I. (1939). The toxicity of poisons applied jointly. Annals of Applied Biology, 26, 585-615.
4. Carmichael, C., & Hayes, B. K. (2001). Prior knowledge and exemplar encoding in children's concept acquisition. Child Development, 72, 1071-1090.
5. Cartwright, N. (1989). Nature's capacities and their measurement. Oxford, England: Clarendon Press.
6. Chapman, G. B., & Robbins, S. J. (1990). Cue interaction in human contingency judgment. Memory & Cognition, 18, 537-545.
7. Cheng, P. W. (1993). Separating causal laws from casual facts: Pressing the limits of statistical relevance. In D. L. Medin (Ed.), The psychology of learning and motivation (pp. 215-264). New York: Academic Press.
8. Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367-405.
9. Cheng, P. W. (2000). Causality in the mind: Estimating contextual and conjunctive causal power. In F. Keil & R. Wilson (Eds.), Cognition and Explanation (pp. 227-253). Cambridge, MA: MIT Press.
10. Cheng, P. W., & Holyoak, K. J. (1995). Complex adaptive systems as intuitive statisticians: Causality, contingency, and prediction. In J.-A. Meyer & H. Roitblat (Eds.), Comparative approaches to cognition (pp. 271-302). Cambridge, MA: MIT Press.
11. Cheng, P. W., & Novick, L. R. (1990). A probabilistic contrast model of causal induction. Journal of Personality and Social Psychology, 58, 545-567.
12. Cheng, P. W., & Novick, L. R. (1992). Covariation in natural causal induction. Psychological Review, 99, 365-382.
13. Clifford, D., & Cheng, P. W. (2000). Tests of a causal-power theory of conjunctive causation. Unpublished manuscript, University of California at Los Angeles.
14. Danks, D. (2003). Equilibria of the Rescorla-Wagner model. Journal of Mathematical Psychology, 47, 109-121.
15. Feller, W. (1957). An introduction to probability theory and its applications (2nd edition). New York: Wiley.
16. Fienberg, S. E. (1980). The analysis of cross-classified categorical data (2nd edition). Cambridge, MA: MIT Press.
17. Forsterling, F. (1989). Models of covariation and attribution: How do they relate to the analogy of analysis of variance? Journal of Personality and Social Psychology, 57, 615-625.
18. Freedman, D., Pisani, R., & Purves, R. (1998). Statistics (3rd edition). New York: Norton.
19. Gart, J. J., & Zweifel, J. R. (1967). On the bias of various estimators of the logit and its variance with application to quantal bioassay. Biometrika, 54, 181-187.
20. Glymour, C. (1998). Psychological and normative theories of causal power and the probabilities of causes, In G. F. Cooper & S. Moral (Eds.), Uncertainty in artificial intelligence (pp. 166-172). San Francisco, CA: Kaufmann.
21. Glymour, C. (2000). Bayes nets as psychological models. In F. Keil & R. Wilson (Eds.), Explanation and cognition (pp. 169-198). Cambridge, MA: MIT Press.
22. Glymour, C. (2001). The mind's arrows: Bayes nets and graphical models in psychology. Cambridge, MA: MIT Press.
23. Glymour, C., & Cheng, P. W. (1998). Causal mechanism and probability: A normative approach. In M. Oaksford & N. Chater (Eds.), Rational models of cognition (pp. 295-313). Oxford, England: Oxford University Press.
24. Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111, 3-32.
25. Haldane, J. B. S. (1955). The estimation and significance of the logarithm of a ratio of frequencies. Annals of Human Genetics, 20, 309-311.
26. Hays, W. L. (1994). Statistics (5th edition). New York: Holt, Rinehart & Winston.
27. Hewstone, M. R. C., & Jaspars, J. M. F. (1987). Covariation and causal attribution: A logical model of the intuitive analysis of variance. Journal of Personality and Social Psychology, 53, 663-672.
28. Hilton, D. J., & Slugoski, B. R. (1986). Knowledge-based causal attribution: The abnormal conditions focus model. Psychological Review, 93, 75-88.
29. Hume, D. (1987). A treatise of human nature (2nd edition), Oxford, England: Clarendon Press. (Original work published 1739)
30. Jenkins, H. M., & Ward, W. C. (1965). Judgment of contingency between responses and outcomes. Psychological Monographs: General and Applied, 79 (1, Whole No. 594), 17.
31. Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty. Cambridge, England: Cambridge University Press.
32. Kant, I. (1965). Critique of pure reason (N. K. Smith, Trans.). London: Macmillan. (Original work published 1781).
33. Kelley, H. H. (1967). Attribution theory in social psychology. In D. Levine (Ed.), Nebraska symposium on motivation (Vol. 15, pp. 192-238). Lincoln: University of Nebraska Press.
34. Kelley, H. H. (1972). Causal schemata and the attribution process. Morristown, NJ: General Learning Press.
35. Khoury, M. J., Flanders, W. D., Greenland, S., & Adams, M. J. (1989). On the measurement of susceptibility in epidemiologic studies. American Journal of Epidemiology, 129, 183-190.
36. Lien, Y., & Cheng, P. W. (2000). Distinguishing genuine from spurious causes: A coherence hypothesis. Cognitive Psychology, 40, 87-137.
37. Mackie, J. L. (1974). The cement of the universe: A study of causation, Oxford, England: Clarendon Press.
38. Marr, D. (1982). Vision. San Francisco: Freeman.
39. Meehl, P. E. (1954). Clinical versus statistical prediction: A theoretical analysis and a look at the evidence. Minneapolis: University of Minnesota Press.
40. Melz, E. R., Cheng, P. W., Holyoak, K. J., & Waldmann, M. R. (1993). Cue competition in human categorization: Contingency or the Rescorla-Wagner learning rule? Comments on Shanks (1991). Journal of Experimental Psychology: Learning, Memory, and Cognition, 19, 1398-1410.
41. Nisbett, R. E., & Ross, L. (1980). Human inference: Strategies and shortcomings of social judgment. Englewood Cliffs, NJ: Prentice-Hall.
42. Novick, L. R., Fratianne, A., & Cheng, P. W. (1992). Knowledge-based assumptions in causal attribution. Social Cognition, 10, 299-333.
43. Parducci, A. (1965). Category judgment: A range-frequency model. Psychological Review, 72, 407-418.
44. Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge, England: Cambridge University Press.
45. Peterson, C. R., & Beach, L. R. (1967). Man as an intuitive statistician. Psychological Bulletin, 68, 29-46.
46. Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current theory and research (pp. 64-99). New York: Appleton-Century-Crofts.
47. Rothman, K. J., & Greenland, S. (1998). Modern epidemiology. Philadelphia, PA: Lippincott-Raven.
48. Schustack, M. W., & Sternberg, R. J. (1981). Evaluation of evidence in causal inference. Journal of Experimental Psychology: General, 110, 101-120.
49. Sheps, M. C. (1958). Shall we count the living or the dead? The New England Journal of Medicine, 259, 1210-1214.
50. Shultz, T. R. (1982). Rules of causal attribution. Monographs of the Society for Research in Child Development, 47 (No. 1).
51. Spirtes, P., Glymour, C., & Schemes, R. (2000). Causation, prediction and search (2nd edition). Boston: MIT Press.
52. Tenenbaum, J. B., & Griffiths, T. L. (2001). Structure learning in human causal induction. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems 13 (pp. 59-65). Cambridge, MA: MIT Press.
53. Tenenbaum, J. B., & Griffiths, T. L. (2003). Theory-based causal inference. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems 15 (pp. 43-50). Cambridge, MA: MIT Press.
54. Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review, 90, 293-315.
55. Wasserman, E. A., Elek, S. M., Chatlosh, D. L., & Baker, A. G. (1993). Rating causal relations: Role of probability in judgments of response-outcome contingency. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19, 174-188.
56. White, P. A. (1998). Causal judgement: Use of different types of contingency information as confirmatory and disconfirmatory. European Journal of Cognitive Psychology, 10, 131-170.
57. Wickens, T. D. (1989). Multiway contingency tables analysis for the social sciences. Hillsdale, NJ: Erlbaum.
58. Wu, M., & Cheng, P. W. (1999). Why causation need not follow from statistical association: Boundary conditions for the evaluation of generative and preventive causal powers. Psychological Science, 10, 92-97.