People often take nondiagnostic information into account when revising their beliefs. A probability judgment decreases due to nondiagnostic information represents the well-established “dilution effect” observed in many domains. Surprisingly, the opposite of the dilution effect called the “confirmation effect” has also been observed frequently. The present work provides a unified cognitive model that allows both effects to be explained simultaneously. The suggested similarity-updating model incorporates two psychological components: first, a similarity-based judgment inspired by categorization research, and second, a weighting-and-adding process with an adjustment following a similarity-based confirmation mechanism. Four experimental studies demonstrate the model’s predictive accuracy for probability judgments and belief revision. The participants received a sample of information from one of two options and had to judge from which option the information came. The similarity-updating model predicts that the probability judgment is a function of the similarity of the sample to the options. When one is presented with a new sample, the previous probability judgment is updated with a second probability judgment by taking a weighted average of the two and adjusting the result according to a similarity-based confirmation. The model describes people’s probability judgments well and outcompetes a Bayesian cognitive model and an alternative probability-theory-plus-noise model. The similarity-updating model accounts for several qualitative findings, namely, dilution effects, confirmation effects, order effects, and the finding that probability judgments are invariant to sample size. In sum, the similarity-updating model provides a plausible account of human probability judgment and belief revision. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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概率判断和信念修正的相似性更新模型。

人们在修改他们的信念时通常会考虑非诊断性信息。由于非诊断信息而导致的概率判断下降代表了在许多领域中观察到的公认的“稀释效应”。令人惊讶的是，被称为“确认效应”的稀释效应的反面也经常被观察到。目前的工作提供了一个统一的认知模型，可以同时解释这两种效应。建议的相似性更新模型包含两个心理成分：第一，受分类研究启发的基于相似性的判断，第二，根据基于相似性的确认机制进行调整的加权和添加过程。四项实验研究证明了该模型对概率判断和信念修正的预测准确性。参与者从两个选项之一收到信息样本，并且必须判断信息来自哪个选项。相似度更新模型预测概率判断是样本与选项相似度的函数。当一个新样本出现时，先前的概率判断会更新为第二个概率判断，方法是取两者的加权平均值并根据基于相似性的确认调整结果。该模型很好地描述了人们的概率判断，并且胜过贝叶斯认知模型和另一种概率论加噪声模型。相似性更新模型解释了几个定性发现，即稀释效应、确认效应、顺序效应以及概率判断对样本量不变的发现。总之，相似性更新模型为人类概率判断和信念修正提供了一个合理的解释。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）