Predicting causality ascriptions from background knowledge: model and experimental validation
Predicting causality ascriptions from background knowledge: model and experimental validation
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DOI：
10.1016/j.ijar.2007.07.003
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发表时间：
2008-08-01
影响因子：
3.9
通讯作者：
Prade, Henri
中科院分区：
文献类型：
--
作者：
Bonnefon, Jean-Francois;Neves, Rui Da Silva;Prade, Henri
定义了一个模型，基于关于世界正常进程的背景知识，预测代理人对链中两个事件之间的因果关系(以及相关的促进和理由概念)的归属。背景知识用非单调的结果关系表示。这使得该模型能够处理信息较差的情况，其中背景知识不够准确以例如结构方程来表示。讨论了因果关系归属的试探性性质，并确定了它们成立的条件(对异常因素的偏好、传递性、与逻辑蕴涵的一致性以及关于析取和合取的稳定性)。据报道，经验数据支持我们基本定义的心理学合理性。(C)2007 Elsevier Inc.保留所有权利。
A model is defined that predicts an agent's ascriptions of causality (and related notions of facilitation and justification) between two events in a chain, based on background knowledge about the normal course of the world. Background knowledge is represented by non-monotonic consequence relations. This enables the model to handle situations of poor information, where background knowledge is not accurate enough to be represented in, e.g., structural equations. Tentative properties of causality ascriptions are discussed, and the conditions under which they hold are identified (preference for abnormal factors, transitivity, coherence with logical entailment, and stability with respect to disjunction and conjunction). Empirical data are reported to support the psychological plausibility of our basic definitions. (C) 2007 Elsevier Inc. All rights reserved.