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  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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인과 마르코프 가정의 심리적 실재성에 대한 연구

The Psychologial Validity of Markov Assumption

한국심리학회지: 인지 및 생물 / The Korean Journal of Cognitive and Biological Psychology, (P)1226-9654; (E)2733-466X
2013, v.25 no.1, pp.93-116
https://doi.org/10.22172/cogbio.2013.25.1.006
박주화 (성균관대학교)

초록

사람들이 예측을 할 때 어떤 정보를 이용할까? 인과 베이즈 모형은 인과 구조의 제약인 마크로프 원리에 의해 인과 예측이 이루어진다고 주장한다. 하지만, 선행 연구들은 사람들이 마르코프 원리가 제안하는 차단 규칙을 일관되게 위반하고 있음을 보고하고 있으며, 이는 인과 베이즈 모형의 심리적 실재성을 위협하는 것이다. 본 연구는 사람들이 어떻게 인과추론을 하는지 3가지 가설, 불충분 인과모형 가설, 모순가설(Walsh & Sloman, 2004), 그리고 기저 메커니즘 가설(Rehder & Burnett, 2005)을 검증하였다. 실험 1은 인과 충분성이 보장된 인과 구조에서도 차단 규칙은 위반됨을 보여주었으며, 이는 모순 가설(Walsh & Sloman, 2004)을 지지하는 결과이다. 실험 2는 실험 1에서 발생 가능한 혼입 변인을 통제하였다. 하지만 그 결과는 실험 1과 동일하였다. 실험 1과 실험 2의 결과가 인과 베이즈 모형에 시사하는 바를 논의하였다.

keywords
Markov assumption, Causal Bayes net, Contradiction, Causal inference, Screening-off rule, 인과 베이즈 모형, 마르코프 가정, 모순 가설, 인과 추론, 차단 규칙

Abstract

What kind of information do people use to make predictions? The principle that causal Bayes nets suggest is that people follow structural constraints like the Markov principle. Previous studies have cast doubt on the psychological validity of the screening-off principle. I tested three hypotheses about how people used causal information to make predictions. Experiment 1 revealed that people violated the screening-off rule even when a causal structure was causally sufficient supporting the contradiction hypothesis (Walsh and Sloman, 2004). Experiment 2 controlled possible compounding in Experiment 1 but still reported the violation. Possible implications to the causal Bayes net formalism were discussed.

keywords
Markov assumption, Causal Bayes net, Contradiction, Causal inference, Screening-off rule, 인과 베이즈 모형, 마르코프 가정, 모순 가설, 인과 추론, 차단 규칙

참고문헌

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(3), 299-352.

2.

Chaigneau, S.E., Barsalou, L.W., & Sloman, S. (2004). Assessing the causal structure of function. Journal of Experimental Psychology: General, 133, 601-625.

3.

Eberhardt, F., Glymour, C., Scheines, R. (2005). N-1 experiments suffice to determine the causal relations among n variables. Technical report, Carnegie Mellon University

4.

Fernbach, P. M. & Sloman, S. A. (2009). Causal learning with local computations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 678-693.

5.

Glymour, C. (2001). The mind’s arrows: Bayes nets and graphical causal models in psychology. Cambridge, MA: Bradford Books.

6.

Gopnik, A., & Schulz, L. (2004). Mechanisms of theory formation in young children. Trends in Cognitive Science, 8, 371-377.

7.

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, 1-30.

8.

Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 354–384.

9.

Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. 661-716.

10.

Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2008). Bayesian models of cognition. In R. Sun (Ed.), Cambridge handbook of computational cognitive modeling (pp. 59-100). Cambridge, MA: Cambridge University Press.

11.

Hausman, D. M., & Woodward, J. (1999). Independence, invariance and the causal Markov condition. British Journal for the Philosophy of Science, 50, 521-583.

12.

Keil, F. C. (2003). Folkscience: Coarse interpretations of a complex reality. Trends in Cognitive Science, 7, 368-373.

13.

Lagnado, D., & Sloman, S. A. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 856-876.

14.

Lagnado, D. A., Waldmann, M. R., Hagmayer, Y., & Sloman, S. A. (2007). Beyond covariation: Cues to causal structure. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 86-100). Oxford, England: Oxford University Press.

15.

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

16.

Mayrhofer, R., Goodman, N. D., Waldmann, M. R., & Tenenbaum, J. B. (2008). Structured correlation from the causal background. In In B. C. Love, K. McRae, V. M. Sloutsky,(Eds.) Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society (pp. 303-308). Austin, TX: Cognitive Science Society.

17.

Mayrhofer, R., Hagmayer, Y., & Waldmann, M., (2010) Agents and causes: A Bayesian error attribution model of causal reasoning. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 925-930). Austin, TX: Cognitive Science Society.

18.

Park, J. H & Sloman, S. A (under review). Structure is not Enough.

19.

Pearl, J. (2000). Causality: Models, Reasoning and Inference. Cambridge: Cambridge University Press.

20.

Rehder, B. (2003). A causal-model theory of conceptual representation and categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1141-59.

21.

Rehder, B. (2009). Causal-based property generalization. Cognitive Science 33, 301-344.

22.

Rehder, B. (in prepration). Nonindependence in Human Causal Reasoning.

23.

Rehder, B., & Burnett, R. C. (2005).Feature inference and the causal structure of object categories. Cognitive Psychology, 50, 264-314.

24.

Reichenbach, H. (1956). The direction of time. Berkeley, CA: University of California Press.

25.

Sloman, S. A. (2005). Causal models; how people think about the world and its alternatives. New York: Oxford University Press.

26.

Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search. New York: Springer-Verlag.

27.

Waldmann, M. R., & Hagmayer, Y. (2005). Seeing versus doing: Two modes of accessing causal knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 216-227.

28.

Walsh, C. R., & Sloman, S. A. (2004). Revising causal beliefs. In K. Forbus, D. Gentner, & T. Regier (Eds.), Proceedings of the 26th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

29.

Walsh, C. & Sloman, S. A. (2008). Updating beliefs withcausal models: Violations of screening off. Gluck, M. A., Anderson, J. R , & Kosslyn, S. M. (Eds.). Memory and Mind: A Festschrift for Gordon H. Bower. New Jersey: Lawrence Erlbaum Associates.

한국심리학회지: 인지 및 생물