取样大小对因果强度推理的影响研究

心理科学 ›› 2013, Vol. 36 ›› Issue (3): 716-721.

取样大小对因果强度推理的影响研究

刘雁伶¹,胡竹菁²

1. 江西师范大学
2. 江西师范大学心理学院

收稿日期:2011-12-13 修回日期:2012-08-13 出版日期:2013-05-20 发布日期:2013-05-24
通讯作者: 刘雁伶

The influence of sample size on causal-strength judgments

YanLing LIU,

Received:2011-12-13 Revised:2012-08-13 Online:2013-05-20 Published:2013-05-24
Contact: YanLing LIU

摘要/Abstract

摘要： 摘要：使用纸笔测验探讨表格集中呈现信息条件下取样大小对单一因果关系强度推理的影响，并比较五种模型ΔP、效力PC、SS效力、Support和χ2的预测与实验数据的相关。结果显示：（1）取样大小对不同的问题有不同的影响：高取样在ΔP=0时导致了低估计值，在0<|ΔP|=PPC时没有效果，在|ΔP|

关键词: 关键词：因果推理, 取样大小, Support模型, SS效力模型.

Abstract: Abstract: The paper conducted a experiment to investigate weather causal judgment ovaries with sample size, and compare the predictions of five models, ΔP、Power-PC、SS、Support and χ2. Contingency were presented with table format, results show that:（a）The Sample size factor have different effect on different contingency : High sample lead to the low estimate in ΔP = 0 , have no effect in 0< |ΔP | = Power-PC , while lead to high estimate in |ΔP|

Key words: Key words: causal inference, sample size, Support-model, SS-model.

刘雁伶胡竹菁. 取样大小对因果强度推理的影响研究[J]. 心理科学, 2013, 36(3): 716-721.

YanLing LIU. The influence of sample size on causal-strength judgments[J]. Psychological Science, 2013, 36(3): 716-721.

参考文献

参考文献
1：王墨耘、傅小兰（a）。因果力比较范式下对效力PC 理论的检验，心理学报，2004，36 (2) ：160—167;
2：王墨耘、傅小兰（b）。用不同外部表征方式集中呈现信息条件下的因果力判断，心理学报，2004，36 (3) ：298～306;
3：Anderson, J. R., & Sheu, C. F. (1995). Causal inferences as perceptual judgments. Memory & Cognition, 23: 510–524;
4：Buhner M.J, Cheng P.W. Clliford D.(2003). From covariation to causation : a test of the assumption of causal power. Journal of Experimental Psychology : Learning , Memory and Cognition , 29 : 1119～1140;
5：Cheng , P. W. (1997). From covariation to causation : a causal power theory. Psychological Review ,104 :367 - 405;
6：Glymour, C. (1998). Learning causes: Psychological explanations of causal explanation. Minds and Machines, 8, 39–60;
7：Greville,W.J., Buehner,M.J.(2010). Temporal Predictability Facilitates Causal Learning. Journal of Experimental Psychology : General , 139 : 756–771;
8：Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51: 334–384;
9：Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116: 661–716;
10：Jenkins, H., & Ward, W. (1965). Judgment of contingencies between responses and outcomes. Psychological Monographs, 7: 1–17;
11：Luhmann,C.C., Ahn,W.K. (2011). Expextations and Intrerpretations During Causal Learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37: 568–587;
12：Liljeholm, M., Cheng, P.W. (2009). The influence of virtual sample size on confidence and causal-strength judgments.Journal of Experimental Psychology: Learning, Memory, and Cognition, 35: 157–172;
13：Lober, K., & Shanks, D. R. (2000). Is causal induction based on causal power? Critique of Cheng (1997). Psychological Review, 107, 195–212;
14：Lu, H., Yuille, A., Liljeholm, M., Cheng, P.W., & Holyoak, K.J. (2008). Bayesian generic priors for causal learning. Psychological Review. 115: 955–984;
15：Mandel D R , Lehman D R.(1998). Integration of contingency information in judgments of cause , covariation , and probability. Journal of Experimental Psychology : General, 127 :269–285;
16：Perales, J. C., & Shanks, D. R. (2007). Models of covariation-based causal judgment: A review and synthesis. Psychonomic Bulletiin & Review, 14:577–596;
17：White, P. A. (2003). Making causal judgments from the proportion of confirming instances: The pCI rule. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29: 710–727;
18：White, P. A. (2004). Causal judgement from contingency information: A systematic test of the pCI rule. Memory and Cognition, 32, 353–368;
19：White. (2009). Accounting for occurrences: An explanation for some novel tendencies in causal judgment from contingency information. Memory and Cognition, 37, 500–513;