مقایسهٔ روش‌ها

	Minimally Counterintuitive Recall	Moralizing Gods Database Analysis
حوزه	Religious Studies	Religious Studies
خانواده	Process / pipeline	Regression model
سال پیدایش	2001	2015
پدیدآور	Pascal Boyer & Charles Ramble	Peter Turchin and the Seshat: Global History Databank team
نوع	Memory-recall experiment for religious-concept transmission	Cross-cultural quantitative database analysis
منبع بنیادین	Boyer, P., & Ramble, C. (2001). Cognitive templates for religious concepts: cross-cultural evidence for recall of counter-intuitive representations. Cognitive Science, 25(4), 535-564.	Turchin, P., Brennan, R., Currie, T., et al. (2015). Seshat: The Global History Databank. Cliodynamics: The Journal of Quantitative History and Cultural Evolution, 6(1), 77-107.
نام‌های دیگر	MCI Recall Paradigm, Counterintuitiveness Recall Experiment, Cognitive Optimum Recall Test, Boyer-Ramble Recall Paradigm	Big Gods Database Analysis, Seshat Moralizing-Gods Analysis, Cross-Cultural Big Gods Modeling, Moralizing High Gods Coding
مرتبط	3	3
خلاصه	The minimally counterintuitive (MCI) recall paradigm is an experimental method in the cognitive science of religion that explains why some supernatural concepts spread and persist while others do not. Pascal Boyer and Charles Ramble's 2001 cross-cultural studies, conducted in France, Gabon, and Nepal, tested the hypothesis that concepts which breach a small number of intuitive ontological expectations - a statue that hears prayers, a person who passes through walls - are remembered better than wholly intuitive concepts and better than bizarre concepts that violate too many expectations at once. By embedding intuitive, minimally counterintuitive, and maximally counterintuitive items in narratives and measuring free recall after a delay, the method locates a 'cognitive optimum' of counterintuitiveness that favours cultural transmission, providing an empirical, memory-based account of the recurrent features of religious representations.	Moralizing gods database analysis is a cross-cultural quantitative method that codes the presence of moralizing or 'big' supernatural enforcers and measures of social complexity across many historical polities over time, then models their relationship. The exemplary infrastructure is the Seshat: Global History Databank, introduced by Peter Turchin and colleagues in 2015, which records hundreds of polities on standardized variables - population, territory, hierarchy, infrastructure, information systems, and religious features - with explicit sources and uncertainty codes. A high-profile 2019 Nature paper using Seshat data argued that complex societies tend to precede moralizing gods; that paper was retracted in 2021 over its treatment of missing data. The method is therefore best understood not as a settled finding but as a databank-driven analytical pipeline whose results depend critically on coding decisions, missing-data handling, and modeling of temporal and phylogenetic dependence.