Parameter validation in hierarchical MPT models by functional dissociation with continuous covariates: An application to contingency inference

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.