@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{Boehm2018,

title = {Estimating Across-Trial Variability Parameters of the Diffusion Decision Model: Expert Advice and Recommendations},

author = {Udo Boehm and Jeff Annis and Michael Frank and Guy Hawkins and Andrew Heathcote and David Kellen and Angelos-Miltiadis Krypotos and Veronika Lerche and Gordon D Logan and Thomas Palmeri and Don van Ravenzwaaij and Mathieu Servant and Henrik Singmann and Jeffrey Starns and Andreas Voss and Thomas Wiecki and Dora Matzke and Eric-Jan Wagenmakers},

url = {https://psyarxiv.com/km28u/, preprint on OSF},

doi = {10.31234/osf.io/km28u},

year  = {2018},

date = {2018-12-01},

urldate = {2018-09-25},

journal = {Journal of Mathematical Psychology},

volume = {87},

pages = {46-75},

abstract = {For many years the Diffusion Decision Model (DDM) has successfully accounted for behavioral data from a wide range of domains. Important contributors to the DDM’s success are the across-trial variability parameters, which allow the model to account for the various shapes of response time distributions encountered in practice. However, several researchers have pointed out that estimating the variability parameters can be a challenging task. Moreover, the numerous fitting methods for the DDM each come with their own associated problems and solutions. This often leaves users in a difficult position. In this collaborative project we invited researchers from the DDM community to apply their various fitting methods to simulated data and provide advice and expert guidance on estimating the DDM’s between-trial variability parameters using these methods. Our study establishes a comprehensive reference resource and describes methods that can help to overcome the challenges associated with estimating the DDM’s across-trial variability parameters.},

keywords = {Diffusion model, mathematical modeling, measurement models, Software},

pubstate = {published},

tppubtype = {article}

}

@article{Mizrak2018,

title = {Forgetting Emotional Material in Working Memory},

author = {Eda Mizrak and Henrik Singmann and Ilke Öztekin},

url = {https://academic.oup.com/scan/article-pdf/13/3/331/24238824/nsx145.pdf, free publisher pdf

https://academic.oup.com/scan/article/13/3/331/4767720, publisher website},

year  = {2018},

date = {2018-03-01},

journal = {Social Cognitive and Affective Neuroscience},

volume = {13},

number = {3},

pages = {331–340},

keywords = {Diffusion model, emotion, measurement models, memory, neuro, working memory},

pubstate = {published},

tppubtype = {article}

}

@article{merkt_flanker_2013,

title = {Flanker performance in female college students with ADHD: a diffusion model analysis},

author = {Merkt, Julia and Singmann, Henrik and Bodenburg, Sebastian and Goossens-Merkt, Heinrich and Kappes, Andreas and Wendt, Mike and Gawrilow, Caterina},

url = {http://singmann.org/download/publications/Merkt%20et%20al.%20(2013)%20-%20ADHD.pdf, published article},

issn = {1866-6116, 1866-6647},

year  = {2013},

date = {2013-01-01},

journal = {ADHD Attention Deficit and Hyperactivity Disorders},

volume = {5},

number = {4},

pages = {321--341},

keywords = {ADHD, Diffusion model, Flanker task, mathematical modeling, measurement models},

pubstate = {published},

tppubtype = {article}

}