@article{Rey-Mermet2025,

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 = {http://singmann.org/download/publications/Rey-Mermet%20et%20al.%20-%202025%20-%20Neither%20measurement%20error%20nor%20speed%E2%80%93accuracy%20trade-offs%20explain%20the%20difficulty%20of%20establishing%20atten.pdf, publisher PDF

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

doi = {10.3758/s13423-025-02696-4},

year  = {2025},

date = {2025-07-01},

urldate = {2025-07-01},

journal = {Psychonomic Bulletin & Review},

volume = {32},

pages = {2585–2632},

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 = {published},

tppubtype = {article}

}