Abstract In traditional multinomial processing tree (MPT) models for aggregate frequency data, parameters have usually been validated by means of experimental manipulations, thereby testing selective effects of discrete independent variables on specific model parameters. More recently, hierarchical MPT models which account for parameter heterogeneity between participants have been introduced. These models offer a new possibility of parameter validation by analyzing selective covariations of interindividual differences in MPT model parameters with continuous covariates. The new approach enables researchers to test parameter validity in terms of functional dissociations, including convergent validity and discriminant validity in a nomological network. Here, we apply the novel approach to a multidimensional source-monitoring model in the domain of stereotype formation based on pseudocontingency inference. Using hierarchical Bayesian MPT models, we test the validity of source-guessing parameters as indicators of specific source evaluations on the individual level. First, analyzing experimental data on stereotype formation ( N = 130 ), we replicated earlier findings of biased source-guessing parameters while taking parameter heterogeneity across participants into account. Second, we investigated the specificity of covariations between conditional guessing parameters and continuous direct measures of source evaluations. Interindividual differences in direct evaluations predicted interindividual differences in specific source-guessing parameters, thus validating their substantive interpretation. Third, in an exploratory analysis, we examined relations of memory parameters and guessing parameters with cognitive performance measures from a standardized cognitive assessment battery.

中文翻译：

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通过具有连续协变量的功能分离在分层 MPT 模型中进行参数验证：在应急推理中的应用

摘要 在用于聚合频率数据的传统多项式处理树（MPT）模型中，通常通过实验操作来验证参数，从而测试离散自变量对特定模型参数的选择性影响。最近，引入了解释参与者之间参数异质性的分层 MPT 模型。这些模型通过分析具有连续协变量的 MPT 模型参数的个体间差异的选择性协变，提供了参数验证的新可能性。新方法使研究人员能够根据功能分离来测试参数有效性，包括规则网络中的收敛有效性和判别有效性。这里，我们将新方法应用于基于伪偶然性推理的刻板印象形成领域的多维源监控模型。使用分层贝叶斯 MPT 模型，我们测试了源猜测参数作为个体层面上特定源评估指标的有效性。首先，分析关于刻板印象形成的实验数据（N = 130），我们复制了有偏见的来源猜测参数的早期发现，同时考虑了参与者之间的参数异质性。其次，我们研究了条件猜测参数和源评估的连续直接测量之间协变的特异性。直接评估中的个体差异预测了特定来源猜测参数的个体差异，从而验证了它们的实质性解释。第三，