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

Bott, Franziska M.; Heck, Daniel W.; Meiser, Thorsten

doi:10.1016/j.jmp.2020.102388

Cited by 8 publications

(8 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This parameterization in terms of difference between group-level parameters has several advantages. First, the explicit modeling of the difference between both conditions allows us to directly implement potential theoretical assumptions and hypotheses about this difference via the prior distribution (Lee & Wagenmakers, 2013) and add potential predictors for the group difference (e.g., Bott et al, 2020; Schubert et al, 2019). Second and more importantly, Boehm et al (2018) showed that the two-step approach of running t tests on individual posterior estimates can lead to incorrect conclusions and is biased toward the alternative hypothesis.…”

Section: Discussionmentioning

confidence: 99%

“…This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. prior distribution (Lee & Wagenmakers, 2013) and add potential predictors for the group difference (e.g., Bott et al, 2020;Schubert et al, 2019). Second and more importantly, Boehm et al (2018) showed that the two-step approach of running t tests on individual posterior estimates can lead to incorrect conclusions and is biased toward the alternative hypothesis.…”

Section: Comparing α Between Conditionsmentioning

confidence: 99%

See 1 more Smart Citation

Measuring the mixture of rule-based and exemplar-based processes in judgment: A hierarchical Bayesian approach.

Izydorczyk¹,

Bröder²

2023

Decision

View full text Add to dashboard Cite

Based on theoretical and empirical considerations, Bröder et al. (2017) proposed the RulEx-J model to quantify the relative contribution of rule-and exemplar-based processes in numerical judgments. In their original article, a least-squares (LS) optimization procedure was used to estimate the model parameters. Despite general evidence for the validity of the model, the authors suggested that a strong bias in favoring the rule module could arise when there is noise in the data. In this article, we present a hierarchical Bayesian implementation of the RulEx-J model with the goal to rectify this problem. In a series of simulation studies, we demonstrate the ability of the hierarchical Bayesian RulEx-J model to recover parameters accurately and to be more robust against noise in the data, compared to an LS estimation routine. One further advantage of the hierarchical Bayesian approach is the direct implementation of hypotheses about group differences in the model structure. A validation experiment as well as reanalyses of two experiments from different labs demonstrate the usefulness of the approach for testing hypotheses about processing differences. Further applications for judgment research are discussed.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Comparing α Between Conditionsmentioning

confidence: 99%

Measuring the mixture of rule-based and exemplar-based processes in judgment: A hierarchical Bayesian approach.

Izydorczyk¹,

Bröder²

2023

Decision

View full text Add to dashboard Cite

show abstract

“…Regarding correlation between parameters computed using the MPT model and traits measured by the questionnaires, only slight correlation was found. This result indicates that parameters estimated using the current MPT model must be interpreted carefully because of the lack of external validity using questionnaires ( Bott et al, 2020 ). The results might be observed because of fact that all the facial expressions used for the current study were posed expressions instead of genuine displays.…”

Section: Discussionmentioning

confidence: 99%

Computational Process of Sharing Emotion: An Authentic Information Perspective

et al. 2022

View full text Add to dashboard Cite

Although results of many psychology studies have shown that sharing emotion achieves dyadic interaction, no report has explained a study of the transmission of authentic information from emotional expressions that can strengthen perceivers. For this study, we used computational modeling, which is a multinomial processing tree, for formal quantification of the process of sharing emotion that emphasizes the perception of authentic information for expressers’ feeling states from facial expressions. Results indicated that the ability to perceive authentic information of feeling states from a happy expression has a higher probability than the probability of judging authentic information from anger expressions. Next, happy facial expressions can activate both emotional elicitation and sharing emotion in perceivers, where emotional elicitation alone is working rather than sharing emotion for angry facial expressions. Third, parameters to detect anger experiences were found to be correlated positively with those of happiness. No robust correlation was found between the parameters extracted from this experiment task and questionnaire-measured emotional contagion, empathy, and social anxiety. Results of this study revealed the possibility that a new computational approach contributes to description of emotion sharing processes.

show abstract

“…This approach resembles the classic multi-trait multi-method approach of Campbell and Fiske (1959). On the one hand, the correlation between MPT parameters and theoretically related, external criteria should be high to indicate convergent validity (Bott et al, 2020). On the other hand, the correlation between MPT parameters and theoretically unrelated or irrelevant external criteria should be close to zero to indicate discriminant validity.…”

Section: Model Validationmentioning

confidence: 99%

How to Develop, Test, and Extend Multinomial Processing Tree Models: A Tutorial

Schmidt¹,

Erdfelder²,

Heck³

2022

Preprint

View full text Add to dashboard Cite

Many psychological theories assume that observable responses are determined by multiple latent processes. Multinomial processing tree (MPT) models are a class of cognitive models for discrete responses that allow researchers to disentangle and measure such processes. Before applying MPT models to specific psychological theories, it is necessary to tailor a model to specific experimental designs. In this tutorial, we explain how to develop, fit, and test MPT models using the classical pair-clustering model as a running example. The first part covers the required data structures, model equations, identifiability, model validation, maximum-likelihood estimation, hypothesis tests, and power analyses using the software multiTree. The second part introduces hierarchical MPT modeling which allows researchers to account for individual differences and to estimate the correlations of latent processes among each other and with additional covariates using the TreeBUGS package in R. All examples including data and annotated analysis scripts are provided at the Open Science Framework (https://osf.io24pbm/).

show abstract

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

Cited by 8 publications

References 41 publications

Measuring the mixture of rule-based and exemplar-based processes in judgment: A hierarchical Bayesian approach.

Measuring the mixture of rule-based and exemplar-based processes in judgment: A hierarchical Bayesian approach.

Computational Process of Sharing Emotion: An Authentic Information Perspective

How to Develop, Test, and Extend Multinomial Processing Tree Models: A Tutorial

Contact Info

Product

Resources

About