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首页> 外文期刊>Cognitive Science >Adaptive Non-Interventional Heuristics for Covariation Detection in Causal Induction: Model Comparison and Rational Analysis
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Adaptive Non-Interventional Heuristics for Covariation Detection in Causal Induction: Model Comparison and Rational Analysis

机译:因果归纳检测中的自适应非介入启发式协变量检测:模型比较与理性分析

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摘要

In this article, 41 models of covariation detection from 2 × 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi-coefficient under an extreme rarity assumption, which has been shown to be an important factor in covariation detection (McKenzie & Mikkelsen, 2007) and data selection (Hattori, 2002; Oaksford & Chater, 1994, 2003). The results were supportive of the new model. To investigate its explanatory adequacy, a rational analysis using two computer simulations was conducted. These simulations revealed the environmental conditions and the memory restrictions under which the new model best approximates the normative model of covariation detection in these tasks. They thus demonstrated the adaptive rationality of the new model.
机译:在本文中,针对2×2列联表的41种协方差检测模型,根据文献中的过去数据和新实验的数据进行了评估。在极端稀有假设下,基于规范phi系数的极限情况,也包括了一个新模型,这已被证明是协变检测(McKenzie&Mikkelsen,2007)和数据选择(Hattori,2002; 2002)的重要因素。 Oaksford&Chater,1994年,2003年)。结果支持了新模型。为了调查其解释的充分性,使用两个计算机模拟进行了理性分析。这些模拟揭示了环境条件和内存限制,在这些条件下,新模型可以最好地近似于这些任务中协变检测的规范模型。因此,他们证明了新模型的适应性合理性。

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