힘 확률 대비 이론에 기반을 둔 인과 추론 연구 = Causal reasoning studies with a focus on the Power Probabilistic Contrast Theory

국문 초록 (Abstract)

인과 추론은 심리학에서는 물론 최근 베이스 접근법을 취하는 인지과학자들에 의해서도 활발히 연구되고 있다. 본 연구는 인과추론에 대한 대표적 심리학 이론인 힘-확률대비이론(a power probabilistic contrast theory of causality)을 중심으로 인과 추론의 최근 동향을 개관하고자 한다. 힘-확률대비이론에서는, 원인은 결과를 일으키거나 억제하는 힘(power)인데, 이 힘은 특정한 조건하에서 통계적 상관을 통해 파악될 수 있다고 가정한다. 본 논문에서는 이 이론에 대한 초기의 경험적 지지 증거를 먼저 살펴본 다음, 베이스 접근에 기반을 둔 이론과의 쟁점을 명확히 하고, 원인은 맥락에 무관하게 동일하게 작동한다는 인과적 불변성 가정(causal invariance hypothesis)을 중심으로 한 보다 최근의 연구 결과를 소개하고자 한다. 이 연구들은 종래의 통계적 접근법으로는 잘 설명되지 않는 결과를 제시함으로써, 철학, 통계학, 그리고 인공 지능 등과 같은 인접 분야에 인과성에 대한 힘 이론을 진지하게 고려할 것을 촉구하고 있다.

다국어 초록 (Multilingual Abstract)

Causal reasoning is actively studied not only by psychologists but, in recent years, also by cognitive scientists taking the Bayesian approach. This paper seeks to provide an overview of the recent trends in causal reasoning research with a focus on the power probabilistic contrast theory of causality, a major psychological theory on causal inference. The power probabilistic contrast theory (PPCT) assumes that a cause is a power that initiates or inhibits the result. This power is purported be understood through statistical correlation under certain conditions. The paper examines the supporting empirical evidence in the development of PPCT. Also, introduced are the theoretical dispute between the PPCT and the model based on Bayesian approach, and the current developments and implications of research on causal invariance hypothesis, which states that cause operates identically regardless of the context. Recent studies have produced experimental results that cannot be readily explained by existing empirical approach. Therefore, these results call for serious examination of the power theory of causality by researchers in neighboring fields such as philosophy, statistics, and artificial intelligence.

목차 (Table of Contents)

• 들어가며
• 학습에 대한 두 접근법 : 연합에서 인지로
• 인과적 학습에 대한 심리학적 모형들
• 인과 귀납에 대한 베이스 접근법
• 인과적 불변성을 중심으로 한 연구들
• 힘-확률대비 이론의 의의와 시사점
• 나가며
• 참고문헌
• Abstract

참고문헌 (Reference)

• 1 박주용, "인과 추리: 철학적 배경과 힘-확률대비 이론을 중심으로." 18 : 37-48, 2000
• 2 Hume, D., "인간 오성의 탐구" 고려원 1987
• 3 Hume, D., "오성에 관하여" 서광서 1975
• 4 Kant, I., "순수이성 비판" 아카넷 1781
• 5 Wu, M., "Why causation need not follow from statistical association: Boundary conditions for the evaluation of generative and preventive causal powers" 10 : 92-97, 1999
• 6 Rehder, B., "When similarity and causality compete in category-based property generalization" 34 : 3-16, 2006
• 7 Liljeholm, M., "When is a cause the “same”? Coherent generalization across contexts" 18 : 1014-1021, 2007
• 8 Spelke, E. S., "Thought without language" Oxford University Press 1988
• 9 Griffiths, T. L., "Theory-based causal induction" 116 : 661-716, 2009
• 10 Lustig, R. H., "The toxic truth about sugar" 482 : 27-29, 2012
• 11 Mitchell, C. J., "The propositional nature of human associative learning" 32 : 183-198, 2009
• 12 Kelley, H. H., "The processes of causal attribution" 28 : 107-128, 1973
• 13 Chater, N., "The probabilistic mind" Oxford University Press 2008
• 14 Carroll, C. D., "The induction of hidden causes: Causal mediation and violations of independent causal influence" Cognitive Science Society 913-918, 2010
• 15 Mackie, J. L., "The cement of the universe: A study of causation" Clarendon Press 1974
• 16 Glymour, C., "The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology" MIT Press 2001
• 17 Maes, E., "The Elusive Nature of the Blocking Effect: 15 Failures to Replicate" 145 : e49-e71, 2016
• 18 Collins, D. J., "Summation in causal learning: Elemental processing or configural generalization?" 59 : 1524-1534, 2006
• 19 Griffiths, T. L., "Structure and strength in causal induction" 51 : 285-386, 2005
• 20 Shanks, D. R., "Psychology of learning and motivation, Vol. 34" Academic Press 265-311, 1996
• 21 Leslie, Alan M., "Pretense and Representation: The Origin of ‘Theory of Mind’" 94 : 412-426, 1987
• 22 Waldmann, M. R., "Predictive and diagnostic learning within causal models:Asymmetries in cue competition" 121 : 222-236, 1992
• 23 Cheng, P. W., "Oxford Handbook of thinking and reasoning" Oxford University Press 2012
• 24 Beckers, T., "Outcome additivity and outcome maximality influence cue competition in human causal learning" 31 : 238-249, 2005
• 25 Perales, J. C., "Models of covariation-based causal judgment: a review and synthesis" 14 : 577-596, 2007
• 26 Kamin, L. J., "Miami symposium on the prediction of behavior: Aversive stimulation" University of Miami Press 9-31, 1968
• 27 Cheng, P. W., "Logical consistency and objectivity in causal learning" Cognitive Science Society 2034-2039, 2013
• 28 Lucas C. G., "Learning the form of causal relationships using hierarchical Bayesian models" 34 : 113-147, 2010
• 29 Shanks, D. R., "Learning : From association to cognition" 61 : 273-301, 2010
• 30 Lober, K., "Is causal induction based on causal power? Critique of Cheng (1997)" 107 : 195-212, 2000
• 31 Meder, B., "Inferring interventional predictions from observational learning data" 15 : 75-80, 2008
• 32 Griffiths, T. L., "Handbook of computational psychology" Cambridge University Press 285-386, 2008
• 33 Buehner, M., "From covariation to causation: A test of the assumption of causal power" 29 : 1119-1140, 2003
• 34 Cheng, P. W., "From covariation to causation: A causal power theory" 104 : 367-405, 1997
• 35 Soto, F. A., "Explaining compound generalization in associative and causal Learning through rational principles of dimensional generalization" 121 : 526-558, 2014
• 36 Danks, D., "Equilibria of the Rescorla–Wagner model" 47 : 109-121, 2003
• 37 Rottman, B. M., "Do people reason rationally about causally related events? Markov violations, weak inferences, and failures of explaining away" 87 : 88-134, 2016
• 38 Lien, Y., "Distinguishing genuine from spurious causes: A coherence hypothesis" 40 : 87-137, 2000
• 39 Rescorla, R. A., "Classical Conditioning II: Current Theory and Research" Appleton-Century-Crofts 64-99, 1972
• 40 Sloman, S., "Causal models" Oxford University Press 2005
• 41 Gopnik, A., "Causal learning: Psychology, Philosophy, and computation" Oxford University Press 2007
• 42 Gopnik, A., "Causal learning mechanisms in very young children: Two, three, and four-year-olds infer causal relations from patterns of variation and covariation" 37 : 620-629, 2001
• 43 Holyoak, J. K., "Causal learning and inference as a rational process: The new synthesis" 62 : 135-163, 2011
• 44 Lu, H., "Bayesian generic priors for causal learning" 115 : 955-982, 2008
• 45 Novick, L. R., "Assessing interactive causal influence. Psychological Review" 111 : 455-485, 2004
• 46 Lu, H., "A Bayesian theory of sequential causal learning and abstract transfer" 40 : 404-430, 2016