McClelland James L, Thompson Richard M
Department of Psychology, Carnegie Mellon University, Pittsburgh , USA.
Dev Sci. 2007 May;10(3):333-56. doi: 10.1111/j.1467-7687.2007.00586.x.
A connectionist model of causal attribution is presented, emphasizing the use of domain-general principles of processing and learning previously employed in models of semantic cognition. The model categorizes objects dependent upon their observed 'causal properties' and is capable of making several types of inferences that 4-year-old children have been shown to be capable of. The model gives rise to approximate conformity to normative models of causal inference and gives approximate estimates of the probability that an object presented in an ambiguous situation actually possesses a particular causal power, based on background knowledge and recent observations. It accounts for data from three sets of experimental studies of the causal inferencing abilities of young children. The model provides a base for further efforts to delineate the intuitive mechanisms of causal inference employed by children and adults, without appealing to inherent principles or mechanisms specialized for causal as opposed to other forms of reasoning.
提出了一种因果归因的联结主义模型，强调运用先前在语义认知模型中使用的领域通用的处理和学习原则。该模型根据观察到的“因果属性”对物体进行分类，并且能够做出已证明4岁儿童能够做出的几种类型的推理。该模型产生与因果推理规范模型的近似一致性，并根据背景知识和近期观察结果，对在模糊情境中呈现的物体实际具有特定因果能力的概率给出近似估计。它解释了来自三组关于幼儿因果推理能力的实验研究的数据。该模型为进一步努力描绘儿童和成人所采用的因果推理直观机制奠定了基础，而无需诉诸专门用于因果推理而非其他推理形式的内在原则或机制。
