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2023 – Faster Action Reprogramming

Salomoni, S. E., Gronau, Q. F., Heathcote, A., Matzke, D., & Hinder, M. R. (2023). Faster action reprogramming, but not stopping, with proactive cues: Combining EMG and computational modelling in response-selective stop signal tasks. Manuscript submitted for publication.

2023 – A Unified Account of Simple

Gronau, Q.F., Hinder, M.R., Salomoni, S.E., Matzke, D., & Heathcote, A. (2023). A unified account of simple and response-selective inhibition. Manuscript submitted for publication. 

2022 – Reliability of Triggering Stop Process

Sebastian, A., Forstmann, B.U., & Matzke, D. (2022). Reliability of triggering the stop process is related to prefrontal-subthalamic hyperdirect pathway recruitment. Manuscript submitted for publication. 

2022 – A Hybrid Approach to Dynamic

Tanis, C.C., Heathcote, A., Zrubka, M., & Matzke, D. (2022). A hybrid approach to dynamic cognitive psychometrics. Manuscript submitted for publication.

2022 – Slower Processing Dominates Executive-Function

Heathcote, A., Garton, R., Hinder, M.R., Reynolds, A., Tanis, C., & Matzke, D. (2022). Slower processing dominates executive-function deficits in cognitive aging. Manuscript submitted for publication.

Sub – Tutorial on fitting joint models

Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2022). A tutorial on fitting joint models of M/EEG and behavior to understand cognition. Preprint.

Sub – Efficient numerical approximations of a non-regular Fokker-Plank equation

Boehm, U., Cox, S., Gantner, G. & Stevenson, R. (submitted). Efficient Numerical Approximation ofa Non Regular Fokker–Planck Equation Associated With First-Passage Time Distributions. ArXiv.

Sub – A Bayesian Multiverse Analysis of Many Labs 4

Haaf, J. M., Hoogeveen, S., Berkhout, S., Gronau, Q. F., & Wagenmakers, E. J. (2020). A Bayesian Multiverse Analysis of Many Labs 4: Quantifying the Evidence against Mortality Salience. PsyArXiv. [GitHub]

Sub – Capturing Ordinal Theoretical Constraint

Haaf, J. M., Klaassen, F., & Rouder, J. N. (submitted). Capturing Ordinal Theoretical Constraint in Psychological Science. PsyArXiv.

Sub – Bayes Factor vs. Posterior-Predictive Model Assessment

Haaf, J. M., Klaassen, F., & Rouder, J.N. (submitted). Bayes factor vs. Posterior-Predictive Model Assessment: Insights from Ordinal Constraints. PsyArXiv.

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