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Cultural Consensus Theory: Comparing different concepts of cultural truth

机译:文化共识理论:比较文化真理的不同概念

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摘要

Cultural Consensus Theory (CCT) is a model-based approach to aggregating the responses of informants (respondents) to questions (items) about some domain of their shared cultural knowledge. The purpose of CCT is to allow a researcher to discover consensus knowledge in cultural groups. This paper compares and contrasts two CCT models for items requiring a dichotomous, true/false answer. The first model is the General Condorcet Model (GCM). A special case of the GCM is already in wide use, especially in cultural anthropology, and this paper generalizes that version and provides new mathematical and statistical results for it. The character of the GCM is that of a general signal detection model, where the item-trial type (signal or noise) as well as the respondents' hit and false alarm rates, are latent rather than observable. The second model, the Latent Truth Model (LTM), is a new model that allows cultural truth to assume continuous values in the unit interval rather than the two-valued truth assumption of the GCM. Both models are compared analytically, and hierarchical Bayesian inference for each is developed. A posterior predictive model check is established for both models that bears directly on the assumption that there is a single consensus truth. In addition, the similarities and differences between the models are illustrated both with mathematical and statistical results, as well as by analyzing real and simulated data sets, and a second posterior predictive check that tends to differentiate the models is also provided.
机译:文化共识理论(CCT)是一种基于模型的方法,用于汇总线人(受访者)对有关其共享文化知识的某些领域的问题(项目)的回答。 CCT的目的是允许研究人员发现文化群体中的共识性知识。本文针对需要二分式,正确/错误答案的项目,对两种CCT模型进行了比较和对比。第一个模型是通用Condorcet模型(GCM)。 GCM的一种特殊情况已被广泛使用,尤其是在文化人类学中,本文对此进行了概括,并为其提供了新的数学和统计结果。 GCM的特征是通用信号检测模型的特征,其中项目试验类型(信号或噪声)以及受访者的命中率和误报率是潜在的,而不是可观察的。第二种模型是潜在真理模型(LTM),它是一种新模型,它允许文化真理在单位间隔中采用连续值,而不是GCM的二值真理假设。通过分析比较两个模型,并为每个模型开发了层次贝叶斯推断。为这两个模型建立了后验预测模型检查,该模型直接假设存在一个共识真相。此外,还通过数学和统计结果以及通过分析实际和模拟数据集来说明模型之间的异同,并且还提供了倾向于区分模型的第二种后验预测检查。

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