认知与发展心理学研究室知识库

简单因果归纳是指人们对单一因果关系中的原因产生或者预防结果的能力的估计，在人们的生活实践中具有重要的意义。目前对简单因果归纳存在很多理论解释和问题争议。本研究针对己往研究中的重结果预测忽视推理过程说明的倾向，从对简单因果归纳的计算函数描述与算法过程描述相结合的角度，系统地探讨比较了在不同原因种类下，在因果信息不同的外部表征方式下，和在不同的因果力估计方式下简单因果归纳的模式、主要影响因素和认知过程机制。研究主要发现:
对于产生能力的估计，在逐个呈现样例的程序下，被试的因果力估计一般符合权重△P模型;而在集中呈现信息的程序下，被试的因果力估计一般符合△P规则。
对于预防能力的估计，在逐个呈现样例的程序下，被试的因果力估计部分符合权重△P模型，部分符合效力PC理论;而在集中呈现信息的程序下，被试的因果力估计部分符合△P规则，部分符合效力PC理论。
在因果力比较中，因果作用难度信息的知觉突出性影响被试是否考虑难度信息来做因果力比较，被试一般并不考虑结果基率所蕴涵的难度信息，但是会考虑不同程度结果目标状态的难度差异来比较因果力。
因果信息不同的外部表征方式通过制约任务一记忆负荷要求的高低，而影响因果力估计的过程机制。逐个呈现样例程序要求的记忆负荷和统计计算难度大，从而导致对因果四格信息的权重差异，出现因果力估计系统偏离△尸的现象;而集中呈现信息程序要求的一记忆负荷小，便于统计计算，被试能综合考虑因果四格信息，并使用明确的统计计算规则来做估计。
在通常的实验室情景下，因果力估计的过程机制看来主要是统计计算而非联想。
人们简单因果归纳主要受下列诸多具体因素的影响: △P, P(E|C)或P(~E|C)、因果信息的外部表征方式、任务一记忆负荷要求、被试加工努力程度、对因果四格信息的权重、原因种类、估计方式、因果作用的难度、情景、知识、指导语和个体差异等因素。人们简单的因果归纳受如此多的具体实际因素的影响，因而具有复杂多样性，难以用统一的模式加以描述和概括，一目前没有任何一个理论模掣能统一地解释本研究发现的复杂多样的实验现象。

Simple Causal Induction is human estimation of powers of causes to generate or prevent effects. There are so many theories and controversies in accounts for simple causal induction. To address the problem that the previous relevant studies emphasize descriptions of results of causal estimates but disregard descriptions of reasoning process, by the combination of computational and algorithmic descriptions of simple causal induction, the present study systematically explored the patterns, the main affecting factors and the cognitive mechanismof simple causal induction in the different kinds of causes, in the different external representations of causal information, and in the different estimating modes.
The present study showed:
In generative causes, causal estimates generally conform to the weighted DP Model in the trial-by-trial presentation procedure, but conform to the DP rule in the concentrative presentation procedure.
In preventive causes, causal estimates partially conform to the weighted 0P Model and partially conform to the power PC theory in the trial-by-trial presentation procedure, but partially conform to the DP rule and partially conform to the power PC theory in the concentrative presentation procedure.
In comparing causal powers, the perceptual salience of the difficulty of causal action affects whether participants take account of the difficulty to comparing causal powers. Participants disregard the difficulty of causal action implies by the base rate of the effect, whereas take account of difficulties in different degrees of goal states of effects.
Different loads of memory required by different external representational modes of causal information cause different weight patterns for causal four cells and different patterns of causal estimates. High loads of memory in the trial-by-trial presentation procedure cause participants' weight differences in causal four cells so as to cause the systematical deviation of causal estimates from 0P. However, in the concentrative presentation procedure without high load of memory, participants use causal four cells equally to make causal estimates by statistic computation.
In usual experimental situations, the mechanism of causal estimation seems to be statistic computation rather than association.
Estimations of causal powers are affected by the following so many factors: △P, P(E|C) or P(~E|C), external representational modes of causal information, memory load, weights for causal four cells, degrees of processing effort, kinds of causes, estimating modes, difficulties of causal action, situations of tasks, knowledge, instructions, individual differences, etc., that they are so complex and diverse that it is impossible to generalize them into a uniform pattern.