- 권한신청
- P-ISSN1226-9654
- E-ISSN2733-466X
- KCI
인과 구조 작동 방식이 인과 추론의 정확성에 미치는 효과
The Effect of a Functional form of Causal relation on Accurate Causal Reasoning
도승이 (성균관대학교)
초록
개인과 기업 그리고 국가 정책의 성공은 판단과 예측의 정확성에 달려있다고 해도 과언이 아니다. 하지만 사람들은 판단과 예측을 할 때 주어진 정보 외에 추가적인 정보를 탐색하지 않으며, 심지어 주어진 정보조차 무시한다. 판단과 예측의 정확성을 향상시키기 위해서는 이러한 정보 무시 경향성의 인지적 과정에 대한 이해가 필수적이다. 본 연구는 고등 인지 과정의 새로운 규범적 모델로 떠오르고 있는 인과 베이즈 모형(causal Bayes nets)의 틀에서 정보 무시 현상의 인지적 과정을 연구하였다. 실험 1에서는 인과 베이즈 모형이 적용되기 위한 세 가지 경계 조건인 인과 구조와 파라미터의 충분성 및 인과 구조 작동 방식의 명확성을 만족시키는 상황을 구성한 후 인과 추론을 실시하였다. 선행 연구와는 다르게 실험 1에서는 정보 무시 현상이 관찰되지 않았다. 실험 2는 인과 구조가 작동하는 방식을 변경하였다. 실험 2는 인과 구조가 자발적으로 작동한 실험 1과는 달리 인과 구조가 실험 참여자의 개입에 의해서만 작동하였다. 실험 2에서는 정보 무시 현상이 관찰되었다. 이는 선행 연구에서 보고된 정보 무시 현상이 인과 구조 또는 관련 변인들이 작동하는 방식에 대한 인출이 되지 않아서 발생했을 가능성을 시사한다. 본 연구는 또한 인과 관계 표상에 대한 인과 베이즈 모형의 가정이 실제 사람들의 표상과 다를 가능성이 있음을 시사한다. 본 연구는 인과 구조가 작동하는 방식에 대한 인지적 이해를 바탕으로 판단과 예측의 정확성을 향상시키는 후속 연구가 필요함을 보여 주였다.
- keywords
- The causal Bayes nets, neglect of relevant information, predictions, 정보 무시 현상, 인과 베이즈 모형, 인과 구조 작동 방식, 예측 추론
Abstract
The success of an individual, a business and even a country depends on the accuracy of judgments and predictions about the future. Reasoners, however, often fail to search for additional pieces of information to make an accurate prediction. Moreover, they tend to neglect a relevant piece of information even if it is available. Thus, it is critical to understand the cognitive process behind the neglect phenomenon in order to increase the accuracy of predictions. The present study aimed to investigate the process that underlies neglect of relevant information based on a newly emerging normative model, the causal Bayes nets. In Experiment 1, we constructed a situation where the three boundary conditions of the causal Bayes nets were satisfied: the sufficiency of a causal structure, parameters, and the clarity of the functional form of causal structure. The results revealed that participants did not neglect a relevant piece of information when they made a predictive inference. Experiment 2 was identical to Experiment 1, except for the functional form of causal structure. The causal structure in Experiment 1 was activated by itself while the structure in Experiment 2 was activated only when it was intervened. With the intervention-based causal structure, participants showed neglect of a relevant piece of information to make a predictive inference. Our results suggest that neglect of a functional form of events in question is responsible for the failure to consider relevant information. Our results also suggest that the assumptions that the causal Bayes nets make about representations of causal relations may not be consistent with the actual representations of reasoners. Our results suggest that a future study is necessary to investigate how understanding of the functional form of causal structures influences prediction accuracy.
- keywords
- The causal Bayes nets, neglect of relevant information, predictions, 정보 무시 현상, 인과 베이즈 모형, 인과 구조 작동 방식, 예측 추론
참고문헌
윤민지 (2014). 예측 추론과 진단 추론의 정보처리 특성. 성균관대학교 석사학위 논문
한겨레신문 (2014. 11. 17). ‘지진’ 예측 실패한 과학자는 유죄, 무죄?
경향신문 (2014. 10. 20). 고속도로 교통량 예측 실패… 통행료로 건설비 원금도 못 갚아.
Alter, A. L., Oppenheimer, D. M., Epley, N., & Eyre, R. N. (2007). Overcoming intuition: Metacognitive difficulty activates analytic reasoning. Journal of Experimental Psychology: General, 136, 569-576.
Anderson, J. (1990). The adaptive character of thought. Hillsdale, NJ: Erlbaum.
Barbey, A. K., & Sloman, S. A. (2007). Base-rate respect: From statistical formats to cognitive structures. Behavioral and Brain Sciences, 30, 287-298.
Baron, J. (2000). Thinking and deciding. Cambridge University Press.
Bonini, N., Tentori, K., & Osherson, D. (2004). A different conjunction fallacy. Mind & Language, 19(2), 199-210.
Cartwright, N. (2004). Causation: One word, many things. Philosophy of Science, 71, 805-81.
Chater, N., & Oaksford, M. (1999). Ten years of the rational analysis of cognition. Trends in Cognitive Sciences, 3, 57-65.
Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104, 367-405.
Cummins, D. D. (2014). The impact of disablers on predictive inference. Journal of experimental psychology: learning, memory, and cognition, 40(6), 1638-1655.
Dawes, R. M. (1979). The robust beauty of improper linear models. American Psychologist, 34, 571-582.
Doherty, M. E., Chadwick, R., Caravan, H., Barr, D., & Mynatt, C. R. (1996). On people’s understanding of the diagnostic implications of probabilistic data. Memory & Cognition, 24, 644-654.
Doherty, M. E., Mynatt, C. R., Tweeney, R. D., & Schiavo, M. D. (1979). On pseudodiagnosticity. Acta Psychologica, 43, 111- 121.
Fernbach, P. M., & Sloman, S. A. (2009). Causal Learning with Local Computations, Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(3), 678-693.
Fernbach, P. M., & Rehder, B. (2013). Cognitive shortcuts in causal inference. Argument & Computation, 4(1), 64-88.
Fernbach, P. M., Darlow, A., & Sloman, S. A. (2010). Neglect of Alternative Causes in Predictive But not Diagnostic Reasoning, Psychological Science, 21, 329-336.
Fernbach, P. M., Darlow, A., & Sloman, S. A. (2011a). Asymmetries in Predictive and Diagnostic Reasoning, Journal of Experimental Psychology: General, 140, 168-185.
Fernbach, P. M., Darlow, A., & Sloman, S. A. (2011b). When Good Evidence Goes Bad: TheWeak Evidence Effect in Judgment and Decision-Making. Cognition, 119, 459-467.
Fischhoff, B., Slovic, P., & Lichtenstein, S. (1977). Knowing with certainty: The appropriateness of extreme confidence. Journal of Experimental Psychology: Human perception and performance, 3(4), 552-564.
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological review, 103(4), 650-669.
Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62, 451-482.
Gigerenzer, G., Hoffrage, U., & Kleinbölting, H. (1991). Probabilistic mental models: a Brunswikian theory of confidence. Psychological review, 98(4), 506.
Gilovich, T., & Griffin, D. (2002). Heuristics and biases: Then and now. In T. Gilovich, D. W. Griffin, & D. Kahneman (Eds.). Heuristics and biases: The psychology of intuitive judgment (pp. 230-249). Cambridge, England: Cambridge University Press.
Glymour, C. (1998). Learning causes: Psychological explanations of causal explanation. Minds and Machines, 8, 39-60.
Griffiths, T. L., & Tenebaum, J. B. (2005). Structure and Strength in Causal Induction, Cognitive Psychology, 51, 334-384.
Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-Based Causal Induction, Psychological Review, 116, 661-716.
Hadjichristidis, C., Sloman, S. A., & Over, D. E. (2009). Categorical induction from uncertain premises: Jeffrey’s (doesn’t) rule. Manuscript submitted for publication.
Hattori, M., & Oaksford, M. (2007). Adaptive non interventional heuristics for covariation detection in causal induction: Model comparison and rational analysis. Cognitive Science, 31(5), 765-814.
Hastie, R., & Dawes, R. M. (Eds.). (2010). Rational choice in an uncertain world: The psychology of judgment and decision making. Sage.
Hausman, D. M., & Woodward, J. (1999). Independence, invariance and the causal Markov condition. British Journal for the Philosophy of Science, 50, 521-583.
Hertwig, R., & Gigerenzer, G. (1999). Theconjunction fallacy'revisited: how intelligent inferences look like reasoning errors. Journal of Behavioral Decision Making, 12, 275-306.
Holyoak, K. J., & Cheng, P. W. (2011), Causal Learning and Inference as a Rational Process, Annual Review of Psychology, 62, 135-163.
Kanheman, D., Slovic, P., & Tversky, A. (1982). Judgement under uncertainty: Heuristics and biases.Judgment. Cambridge University Press.
Klayman, J., & Ha, Y. W. (1987). Confirmation, disconfirmation, and information in hypothesis testing. Psychological Review, 94, 211-228.
Koriat, A., Lichtenstein, S., & Fischhoff, B. (1980). Reasons for confidence. Journal of Experimental Psychology: Human learning and memory, 6(2), 107-118.
Krynski, T. R., & Tenenbaum, J. B. (2007). The role of causality in judgment under uncertainty. Journal of Experimental Psychology: General, 136(3), 430-450.
Kushnir, T., Gopnik, A., Lucas, C., and Schulz, L. (2010). Inferring hidden causal structure. Cognitive Science, 34(1), 148-160.
Lagnado, D., & Sloman, S. A. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 856-876.
Lagnado, D. A., & Sloman, S. A. (2006). Time as a guide to cause. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(3), 451-460.
Lord, C. G., Ross, L., & Lepper, M. R. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37, 2098-2109.
Lucas, C. G., & Griffiths, T. L. (2010). Learning the form of causal relationships using hierarchical Bayesian models. Cognitive Science, 34(1), 113-147.
Luhmann, C., & Ahn, W. (2007). BUCKLE: A model of unobserved cause learning. Psychological Review, 92(3), 657-677.
Mayrhofer, R., & Waldmann, M. R. (2014). Agents and causes: Dispositional intuitions as a guide to causal structure. Cognitive science, 1-31.
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.
Meder, B., Mayrhofer, R., & Waldmann, M. R. (2014). Structure induction in diagnostic causal reasoning. Psychological review, 121(3), 277-301.
Meder, B., Hagmayer, Y., & Waldmann, M. R. (2008). Inferring interventional predictions from observational learning data. Psychonomic Bulletin & Review, 15(1), 75-80.
Meder, B., Hagmayer, Y., & Waldmann, M. R. (2009). The role of learning data in causal reasoning about observations and interventions. Memory & Cognition, 37, 249-264.
Oskamp, S. (1965). Overconfidence in case-study judgments. Journal of consulting psychology, 29(3), 261-265
Park, J., & Sloman, S. A. (2013). Mechanistic beliefs determine adherence to the Markov property in causal reasoning. Cognitive Psychology, 67, 186-216.
Park, J., & Sloman, S. A. (2014). Causal explanation in the face of contradiction. Memory & Cognition, 42(5), 806-820.
Pearl, J. (2000). Causality: models, reasoning and inference. Cambridge: MIT press.
Ross, B. H., & Murphy, G. L. (1996). Category- based predictions: Influence of uncertainty and feature associations. Journal of Experimental Psychology: Learning, Memory, 22, 736-745.
Scheines, R., Spirtes, P., Glymour, C., & Meek, C. (1994). TETRAD II. Hillsdale. NJ: Erlbaum.
Shaklee, H., & Fischhoff, B. (1982). Strategies of Information Search in Causal Analysis. Memory & Cognition, 10, 520-530.
Skowronski, J. J., & Carlston, D. E. (1989). Negativity and extremity biases in impression formation: A review of explanations. Psychological bulletin, 105(1), 131-142.
Sloman, S. (2009). Causal models: How people think about the world and its alternatives. Oxford University Press.
Sloman, S. A., Lagnado D. (2015). Causality in Thought. Annual Review of Psychology, 66(3), 1-25.
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search. New York: Springer-Verlag.
Tversky, A., & Kahneman, D. (1980). Causal schemata in judgments under uncertainty. In M. Fishbein (Ed.), Progress in social psychology (pp.49-72). Hillsdale, NJ: Erlbaum.
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.
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.
Wason, P. C. (1960). On the failure to eliminate hypotheses in a conceptual task. Quarterly journal of experimental psychology, 12(3), 129- 140.
Waldmann, M. R. (2007). Combining versus analyzing multiple causes: How domain assumptions and task context affect integration rules. Cognitive Science, 31, 233-256.
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