@article{rey-mermetNeitherMeasurementErrorinpress,

title = {Neither Measurement Error nor Speed-Accuracy Trade-Offs Explain the Difficulty of Establishing Attentional Control as a Psychometric Construct: Evidence from a Latent-Variable Analysis Using Diffusion Modeling},

author = {Alodie Rey-Mermet and Henrik Singmann and Klaus Oberauer},

url = {https://osf.io/3h26y_v2/download/, preprint},

doi = {10.31234/osf.io/3h26y_v2},

year  = {2026},

date = {2026-04-02},

urldate = {2026-04-02},

journal = {Psychonomic Bulletin & Review},

abstract = {Attentional control refers to the ability to maintain and implement a goal and goal-relevant information when facing distraction. So far, previous research has failed to substantiate strong evidence for a psychometric construct of attentional control. This could result from two methodological shortcomings: (a) the neglect of individual differences in speed-accuracy trade-offs when only speed or accuracy is used as dependent variable, and (b) the difficulty of isolating attentional control from measurement error. To overcome both issues, we combined hierarchical-Bayesian Wiener diffusion modeling with structural equation modeling. We re-analyzed six datasets, which included data from three to eight attentional-control tasks, and data from young and older adults. Overall, the results showed that measures of attentional control failed to correlate with each other and failed to load on a latent variable. Therefore, limiting the impact of differences in speed-accuracy trade-offs and of measurement error does not solve the difficulty of establishing attentional control as a psychometric construct. These findings strengthen the case against a psychometric construct of attentional control.},

keywords = {Diffusion model, executive functions, hierarchical-Bayesian modeling, individual differences},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{Singmann2024,

title = {Evaluating the Robustness of Parameter Estimates in Cognitive Models: A Meta-Analytic Review of Multinomial Processing Tree Models Across the Multiverse of Estimation Methods},

author = {Henrik Singmann and Daniel W Heck and Marius Barth and Edgar Erdfelder and Nina R Arnold and Frederik Aust and Jimmy Calanchini and Fabian E Gümüsdagli and Sebastian S Horn and David Kellen and Karl C. Klauer and Dora Matzke and Franziska Meissner and Martha Michalkiewicz and Marie Luisa Schaper and Christoph Stahl and Beatrice G. Kuhlmann and Julia Groß},

url = {https://psycnet.apa.org/fulltext/2024-99104-001.pdf, journal PDF

http://singmann.org/download/publications/MPT-multiverse.pdf, accepted manuscript

https://osf.io/preprints/psyarxiv/sd4xp, preprint on OSF},

doi = {10.1037/bul0000434},

year  = {2024},

date = {2024-08-28},

urldate = {2024-08-28},

journal = {Psychological Bulletin},

volume = {150},

issue = {8},

pages = {965-1003},

publisher = {PsyArXiv},

keywords = {hierarchical-Bayesian modeling, mathematical modeling, measurement models, Meta Analysis, MPT models, Statistics - Computation},

pubstate = {published},

tppubtype = {article}

}

@article{Baumann2021,

title = {A Linear Threshold Model for Optimal Stopping Behavior},

author = {Chrisitiane Baumann and Henrik Singmann and Samuel Gershman and Bettina von Helversen},

url = {http://singmann.org/download/publications/Baumann_Optimal_Stopping_PNAS_preprint.pdf, preprint},

year  = {2020},

date = {2020-06-09},

journal = {Proceedings of the National Academy of Sciences},

volume = {117},

number = {23},

pages = {12750-12755},

keywords = {Decision Making, hierarchical-Bayesian modeling, mathematical modeling, measurement models},

pubstate = {published},

tppubtype = {article}

}

@article{Gronau2020b,

title = {bridgesampling: An R Package for Estimating Normalizing Constants},

author = {Quentin F Gronau and Henrik Singmann and Eric-Jan Wagenmakers},

url = {https://www.jstatsoft.org/index.php/jss/article/view/v092i10/v92i10.pdf, published version

https://arxiv.org/pdf/1710.08162.pdf, preprint

http://arxiv.org/abs/1710.08162, on ArXiV},

doi = {10.18637/jss.v092.i10},

year  = {2020},

date = {2020-02-27},

urldate = {2018-09-25},

journal = {Journal of Statistical Software},

volume = {92},

number = {10},

abstract = {Statistical procedures such as Bayes factor model selection and Bayesian model averaging require the computation of normalizing constants (e.g., marginal likelihoods). These normalizing constants are notoriously difficult to obtain, as they usually involve high-dimensional integrals that cannot be solved analytically. Here we introduce an R package that uses bridge sampling (Meng & Wong, 1996; Meng & Schilling, 2002) to estimate normalizing constants in a generic and easy-to-use fashion. For models implemented in Stan, the estimation procedure is automatic. We illustrate the functionality of the package with three examples.},

keywords = {hierarchical-Bayesian modeling, R, Software, Statistics - Computation},

pubstate = {published},

tppubtype = {article}

}

@article{Trippas2018,

title = {Characterizing Belief Bias in Syllogistic Reasoning: A Hierarchical-Bayesian Meta-Analysis of ROC Data},

author = {Dries Trippas and David Kellen and Henrik Singmann and Gordon Pennycook and Derek J. Koehler and Jonathan A. Fugelsang and Chad Dubé},

url = {http://singmann.org/download/publications/Trippas-et-al.-2018-Characterizing-belief-bias-in-syllogistic-reasonin.pdf, published version

http://singmann.org/download/publications/trippas_kellen_singmann_et_al_submitted_online.pdf, accepted manuscript

https://osf.io/8dfyv/, data and modeling code},

year  = {2018},

date = {2018-12-01},

journal = {Psychonomic Bulletin & Review},

volume = {25},

number = {6},

pages = {2141–2174},

keywords = {hierarchical-Bayesian modeling, mathematical modeling, measurement models, Meta Analysis, Reasoning, Signal detection, syllogistic reasoning},

pubstate = {published},

tppubtype = {article}

}

@article{Bartsch2018,

title = {The Effects of Refreshing and Elaboration on Working Memory Performance, and their Contributions to Long-Term Memory Formation},

author = {Lea Bartsch and Henrik Singmann and Klaus Oberauer},

url = {http://singmann.org/download/publications/Bartsch-et-al.-2018-The-effects-of-refreshing-and-elaboration-on-worki.pdf, publisher version

https://osf.io/weuc2/, data and analysis code},

year  = {2018},

date = {2018-07-01},

journal = {Memory & Cognition},

volume = {46},

number = {5},

pages = {796-808},

keywords = {hierarchical-Bayesian modeling, working memory},

pubstate = {published},

tppubtype = {article}

}

@article{kellen_modeling_2014,

title = {Modeling source-memory overdistribution},

author = {Kellen, David and Singmann, Henrik and Klauer, Karl Christoph},

url = {http://singmann.org/download/publications/Kellen%20et%20al.%20-%202014%20-%20Modeling%20source-memory%20overdistribution.pdf, published article

http://singmann.org/download/publications/data-scripts/2014_kellen_singmann_klauer.zip, individual data},

year  = {2014},

date = {2014-10-01},

journal = {Journal of Memory and Language},

volume = {76},

pages = {216--236},

keywords = {Familiarity, hierarchical-Bayesian modeling, mathematical modeling, measurement models, memory, MPT models, overdistribution, source memory},

pubstate = {published},

tppubtype = {article}

}