Kolvoort Ivar R, Temme Nina, van Maanen Leendert
Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
Institute for Logic, Language, and Computation, University of Amsterdam, Amsterdam, The Netherlands.
Open Mind (Camb). 2023 Jun 15;7:318-349. doi: 10.1162/opmi_a_00080. eCollection 2023.
One consistent finding in the causal reasoning literature is that causal judgments are rather variable. In particular, distributions of probabilistic causal judgments tend not to be normal and are often not centered on the normative response. As an explanation for these response distributions, we propose that people engage in 'mutation sampling' when confronted with a causal query and integrate this information with prior information about that query. The Mutation Sampler model (Davis & Rehder, 2020) posits that we approximate probabilities using a sampling process, explaining the average responses of participants on a wide variety of tasks. Careful analysis, however, shows that its predicted response distributions do not match empirical distributions. We develop the Bayesian Mutation Sampler (BMS) which extends the original model by incorporating the use of generic prior distributions. We fit the BMS to experimental data and find that, in addition to average responses, the BMS explains multiple distributional phenomena including the moderate conservatism of the bulk of responses, the lack of extreme responses, and spikes of responses at 50%.
因果推理文献中一个一致的发现是，因果判断相当多变。特别是，概率因果判断的分布往往不是正态的，而且通常不以规范反应为中心。作为对这些反应分布的一种解释，我们提出，人们在面对因果查询时会进行“变异抽样”，并将这些信息与关于该查询的先验信息相结合。变异抽样模型（戴维斯和雷德，2020）假定我们使用抽样过程来近似概率，这解释了参与者在各种任务上的平均反应。然而，仔细分析表明，其预测的反应分布与实证分布不匹配。我们开发了贝叶斯变异抽样模型（BMS），通过纳入通用先验分布的使用对原始模型进行了扩展。我们将BMS应用于实验数据，发现除了平均反应外，BMS还解释了多种分布现象，包括大部分反应的适度保守性、缺乏极端反应以及50%处的反应峰值。
