Publications

Montgomery, L. E., Baldini, C. M., Vandekerckhove, J., & Lee, M. D. (in press). Where's Waldo, Ohio? Using cognitive models to improve the aggregation of spatial knowledge. Computational Brain & Behavior.
Nunez, M. D., Fernandez, K., Srinivasan, R., & Vandekerckhove, J. (in press). A tutorial on fitting joint models of M/EEG and behavior to understand cognition. Behavior Research Methods.
Etz, A., Chávez De la Peña, A. F., Baroja, L., Medriano, K., & Vandekerckhove, J. (in press). The HDI+ROPE decision rule is logically incoherent but we can fix it. Psychological Methods.
Chwiesko, C., Janecek, J., Doering, S., Hollearn, M., McMillan, L., Vandekerckhove, J., Lee, M. D., Ratcliff, R., & Yassa, M. (in press). Parsing memory and non-memory contributions to age-related declines in mnemonic discrimination performance: A hierarchical Bayesian diffusion decision modeling approach. Learning & Memory.
Villarreal, J., Chávez De la Peña, A. F., Mistry, P., Menon, V. E., Vandekerckhove, J., & Lee, M. D. (in press). Bayesian graphical modeling with the circular drift diffusion model. Computational Brain & Behavior.
Oravecz, Z., & Vandekerckhove, J. (in press). Quantifying evidence for---and against---Granger causality with Bayes factors. Multivariate Behavioral Research.
Chávez De la Peña, A. F., & Vandekerckhove, J. (preprint). An EZ Bayesian hierarchical drift diffusion model for response time and accuracy. PsyArxiv.
Rouder, J., Chávez De la Peña, A. F., Mehrvarz, M., & Vandekerckhove, J. (preprint). On Cronbach’s merger: Why experiments may not be suitable for measuring individual differences. PsyArxiv.
Heshmati, S., Muth, C., Li, Y., Roeser, R. W., Smyth, J. M., Vandekerckhove, J., Chow, S., & Oravecz, Z. (preprint). Capturing the heterogeneous effects of a mobile-health psychological well-being intervention for early adults: results from a process-oriented approach. PsyArxiv.
Wagenmakers, E., Gronau, Q., & Vandekerckhove, J. (preprint). Five Bayesian intuitions for the Stopping Rule Principle. PsyArxiv.
Weisman, M. J., Kott, A., Ellis, J. E., Murphy, B. J., Parker, T. W., Smith, S., & Vandekerckhove, J. (preprint). Quantitative measurement of cyber resilience: modeling and experimentation. arXiv.
Vo, K., Sun, J. Q., Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2024). Deep latent variable joint cognitive modeling of neural signals and human behavior. NeuroImage, 291, 120559.
Vandekerckhove, J. (2024). Commensurability engineering is first and foremost a theoretical exercise. Behavioral and Brain Sciences, 47, e63.
Kott, A., Weisman, M. J., Vandekerckhove, J., Ellis, J. E., Parker, T. W., Murphy, B. J., & Smith, S. (2023). A methodology for quantitative measurement of cyber resilience (QMOCR). Army Research Labs Technical Report.
Weisman, M. J., Kott, A., & Vandekerckhove, J. (2023). Piecewise linear and stochastic models for the analysis of cyber resilience. 57th Annual Conference on Information Sciences and Systems (CISS).
Wiech, K., Eippert, F., Vandekerckhove, J., Zaman, J., Placek, K., Tuerlinckx, F., Vlaeyen, J., & Tracey, I. (2022). Cortico-brainstem mechanisms of biased perceptual decision-making in the context of pain. Journal of Pain, 23, 1372.
Kott, A., Weisman, M. J., & Vandekerckhove, J. (2022). Mathematical modeling of cyber resilience. IEEE Military Communications Conference Proceedings.
Ellis, J. E., Parker, T. W., Vandekerckhove, J., Murphy, B. J., & Smith, S. (2022). An experimentation infrastructure for quantitative measurements of cyber resilience. IEEE Military Communications Conference Proceedings.
Sun, J. Q., Vo, K., Lui, K. K., Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2022). Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signals. Proceedings of the International Joint Conference on Neural Networks.
Morey, R., Kaschak, M. P., Diez-Álamo, A. M., Glenberg, A. M., Zwaan, R., Lakens, D., Ibáñez, A., Garcia, A., Gianelli, C., Jones, J. L., Madden, J., Alifano, F., Bergen, B., Bloxsom, N. G., Bub, D. N., Cai, Z. G., Chartier, C. R., Chatterjee, A., Conwell, E., Cook, S. W., Davis, J. D., Evers, E., Girard, S., Harter, D., Hartung, F., Herrera, E., Huettig, F., Humphries, S., Juanchich, M., Kühne, K., Lu, S., Lynes, T., Masson, M. E., Ostarek, M., Pessers, S., Reglin, R., Steegen, S., Thiessen, E. D., Thomas, L. E., Trott, S., Vandekerckhove, J., Vanpaemel, W., Vlachou, M., Williams, K., & Ziv-Crispel, N. (2022). A pre-registered, multi-lab non-replication of the Action-sentence Compatibility Effect (ACE). Psychonomic Bulletin & Review, 29, 1239.
Etz, A., Goodman, S. N., & Vandekerckhove, J. (2022). Statistical inference in behavioral research: traditional and Bayesian approaches. Research Integrity: Best Practices for the Social and Behavioral Sciences.
Shiffrin, R. M., Matzke, D., Crystal, J. D., Wagenmakers, E., Chandramouli, S. H., Vandekerckhove, J., Zorzi, M., Morey, R., & Murphy, M. C. (2021). Extraordinary claims, extraordinary evidence? A discussion. Learning & Behavior, 49, 540.
Lui, K. K., Nunez, M. D., Cassidy, J. M., Vandekerckhove, J., Cramer, S. C., & Srinivasan, R. (2021). Timing of readiness potentials reflect a decision-making process in the human brain. Computational Brain & Behavior, 4, 547.
Devezer, B., Navarro, D. J., Vandekerckhove, J., & Buzbas, E. O. (2021). The case for formal methodology in scientific reform. Royal Society Open Science, 8, 200805.
Lucio, P., Vandekerckhove, J., Polanczyk, G., & Cogo-Moreira, H. (2021). Is it worthwhile to take account of "guessing" in the performance of the Raven test? Calling for the principle of parsimony for test validation. Journal of Psychoeducational Assessment, 39, 100–111.
Oravecz, Z., & Vandekerckhove, J. (2020). A joint process model of consensus and longitudinal dynamics. Journal of Mathematical Psychology, 98, 102386.
Aczel, B., Hoekstra, R., Gelman, A., Wagenmakers, E., Klugkist, I., Rouder, J., Vandekerckhove, J., Lee, M. D., Morey, R., Vanpaemel, W., Dienes, Z., & van Ravenzwaaij, D. (2020). Discussion points for Bayesian inference. Nature Human Behavior, 4, 561–563.
Oravecz, Z., Dirsmith, J., Heshmati, S., Vandekerckhove, J., & Brick, T. (2020). Psychological well-being and personality traits are associated with experiencing love in everyday life. Personality and Individual Differences, 154, 109620.
van den Bergh, D., Bogaerts, S., Spreen, M., Flohr, R., Vandekerckhove, J., Rhemtulla, M., Batchelder, W., & Wagenmakers, E. (2020). Cultural consensus theory for the evaluation of patients' mental health scores in forensic psychiatric hospitals. Journal of Mathematical Psychology, 98, 102383.
Guan, M., Stokes, R., Vandekerckhove, J., & Lee, M. D. (2020). A cognitive modeling analysis of risk in sequential choice tasks. Judgment and Decision Making, 15, 823-850.
Vandekerckhove, J., White, C., Trueblood, J., Rouder, J., Matzke, D., Leite, F., Etz, A., Donkin, C., Devezer, B., Criss, A., & Lee, M. D. (2019). Robust diversity in cognitive science. Computational Brain & Behavior, 2, 271–276.
Nunez, M. D., Gosai, A., Vandekerckhove, J., & Srinivasan, R. (2019). The latency of a visual evoked potential tracks the onset of decision making. NeuroImage, 197, 93–108.
Lee, M. D., Criss, A., Devezer, B., Donkin, C., Etz, A., Leite, F., Matzke, D., Rouder, J., Trueblood, J., White, C., & Vandekerckhove, J. (2019). Robust modeling in cognitive science. Computational Brain & Behavior, 2, 141–153.
Schubert, A., Nunez, M. D., Hagemann, D., & Vandekerckhove, J. (2019). Individual differences in cortical processing speed predict cognitive abilities: A model-based cognitive neuroscience account. Computational Brain & Behavior, 2, 64–84.
Dutilh, G., Annis, J., Brown, S., Cassey, P., Evans, N., Grasman, R., Hawkins, G., Heathcote, A., Holmes, W., Krypotos, A., Kupitz, C., Leite, F., Lerche, V., Lin, Y., Logan, G., Palmeri, T., Starns, J., Trueblood, J., van Maanen, L., van Ravenzwaaij, D., Vandekerckhove, J., Visser, I., Voss, A., White, C., Wiecki, T., Rieskamp, J., & Donkin, C. (2019). The quality of response time data inference: A blinded, collaborative approach to the validity of cognitive models. Psychonomic Bulletin & Review, 26, 1051–1069.
Heshmati, S., Oravecz, Z., Pressman, S., Batchelder, W., Muth, C., & Vandekerckhove, J. (2019). What does it mean to feel loved? Cultural agreement and individual differences. Journal of Social and Personal Relationships, 36, 214-243.
Matzke, D., Boehm, U., & Vandekerckhove, J. (2018). Bayesian Inference in Psychology, Part III: Bayesian parameter estimation in nonstandard models. Psychonomic Bulletin & Review, 25, 77–101.
Mistry, P., Pothos, E., Vandekerckhove, J., & Trueblood, J. (2018). A quantum probability account of individual differences in causal reasoning. Journal of Mathematical Psychology, 87, 76–97.
Etz, A., Haaf, J., Rouder, J., & Vandekerckhove, J. (2018). Bayesian inference and testing any hypothesis you can specify. Advances in Methods and Practices in Psychological Science, 1, 281–295.
Vandekerckhove, J., Rouder, J., & Kruschke, J. (2018). Editorial: Bayesian methods for advancing psychological science. Psychonomic Bulletin & Review, 25, 1–4.
Baribault, B., Donkin, C., Little, D., Trueblood, J., Oravecz, Z., van Ravenzwaaij, D., White, C., De Boeck, P., & Vandekerckhove, J. (2018). Metastudies for robust tests of theory. Proceedings of the National Academy of Sciences, 115, 2607–2612.
Rouder, J., Haaf, J., & Vandekerckhove, J. (2018). Bayesian Inference in Psychology, Part IV: Parameter estimation and Bayes factors. Psychonomic Bulletin & Review, 25, 102–113.
Etz, A., & Vandekerckhove, J. (2018). Introduction to Bayesian inference for psychology. Psychonomic Bulletin & Review, 25, 5–34.
Okada, K., Vandekerckhove, J., & Lee, M. D. (2018). Modeling when people quit: Bayesian censored geometric models with hierarchical and latent-mixture extensions. Behavior Research Methods, 50, 406–415.
Dutilh, G., Vandekerckhove, J., Ly, A., Matzke, D., Pedroni, A., Frey, R., Rieskamp, J., & Wagenmakers, E. (2017). A test of the diffusion model explanation for the Worst Performance Rule using preregistration and blinding. Attention, Perception, and Performance, 79, 713–725.
van Ravenzwaaij, D., Donkin, C., & Vandekerckhove, J. (2017). The EZ diffusion model provides a powerful test of simple empirical effects. Psychonomic Bulletin & Review, 24, 547–556.
Lucio, P., Salum, G., Rohde, L., Gadelha, A., Swardfager, W., Vandekerckhove, J., Pan, P., Polanczyk, G., do Rosario, M., Jackowski, A., Mari, J., & Cogo-Moreira, H. (2017). Poor stimulus discriminability as a common neuropsychological deficit between ADHD and reading ability in young children: a moderated mediation model. Psychological Medicine, 47, 255–266.
Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2017). How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters. Journal of Mathematical Psychology, 76, 117–130.
Oravecz, Z., Huentelman, M., & Vandekerckhove, J. (2016). Sequential Bayesian updating for Big Data. Big Data in Cognitive Science: From Methods to Insights, 13–33.
Vandekerckhove, J., & Wagenmakers, E. (2016). C. S. Peirce on the Crisis of Confidence and the "No More Bets" Heuristic. The Winnower, 4843.
Oravecz, Z., Muth, C., & Vandekerckhove, J. (2016). Do people agree on what makes one feel loved? A cognitive psychometric approach to the consensus on felt love. PLoS ONE, 11, e0152803.
Etz, A., & Vandekerckhove, J. (2016). A Bayesian perspective on the Reproducibility Project: Psychology. PLoS ONE, 11, e0149794.
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2016). Bayesian data analysis with the bivariate hierarchical Ornstein-Uhlenbeck process model. Multivariate Behavioral Research, 51, 106–119.
Guan, M., & Vandekerckhove, J. (2016). A Bayesian approach to mitigation of publication bias. Psychonomic Bulletin & Review, 23, 74–86.
Vandekerckhove, J., Matzke, D., & Wagenmakers, E. (2015). Model comparison and the principle of parsimony. Oxford Handbook of Computational and Mathematical Psychology, 300–317.
Nunez, M. D., Srinivasan, R., & Vandekerckhove, J. (2015). Individual differences in attention influence perceptual decision making. Frontiers in Psychology, 6, 18.
Van Elk, M., Matzke, D., Gronau, Q., Guan, M., Vandekerckhove, J., & Wagenmakers, E. (2015). Meta-analyses are no substitute for registered replications: a skeptical perspective on religious priming. Frontiers in Psychology, 6, 1365.
Mistry, P., Trueblood, J., Vandekerckhove, J., & Pothos, E. (2015). A latent-mixture quantum probability model of causal reasoning within a Bayesian inference framework. Proceedings of the 37th Annual Conference of the Cognitive Science Society.
Guan, M., Lee, M. D., & Vandekerckhove, J. (2015). A hierarchical cognitive threshold model of human decision making on different length optimal stopping problems. Proceedings of the 37th Annual Conference of the Cognitive Science Society, 824–829.
Kupitz, C., Buschkuehl, M., Jaeggi, S., Jonides, J., Shah, P., & Vandekerckhove, J. (2015). A diffusion model account of the transfer-of-training effect. Proceedings of the 37th Annual Conference of the Cognitive Science Society.
Salum, G., Sergeant, J., Sonuga-Barke, E., Vandekerckhove, J., Gadelha, A., Pan, P., Moriyama, T., Graeff-Martins, A., Gomes de Alvarenga, P., do Rosario, M., Manfro, G., Polanczyk, G., & Rohde, L. (2014). Specificity of basic information processing and inhibitory control in attention deficit/hyperactivity disorder. Psychological Medicine, 44, 617–631.
Wabersich, D., & Vandekerckhove, J. (2014). Extending JAGS: A tutorial on adding custom distributions to JAGS (with a diffusion model example). Behavior Research Methods, 46, 15-28.
Oravecz, Z., Vandekerckhove, J., & Batchelder, W. (2014). Bayesian Cultural Consensus Theory. Field Methods, 26, 207–222.
Salum, G., Sergeant, J., Sonuga-Barke, E., Vandekerckhove, J., Gadelha, A., Pan, P., Moriyama, T., Graeff-Martins, A., Gomes de Alvarenga, P., do Rosario, M., Manfro, G., Polanczyk, G., & Rohde, L. (2014). Mechanisms underpinning inattention and hyperactivity: neurocognitive support for ADHD dimensionality. Psychological Medicine, 44, 3189–3201.
Wiech, K., Vandekerckhove, J., Zaman, J., Tuerlinckx, F., Vlaeyen, J., & Tracey, I. (2014). Influence of prior information on pain involves biased perceptual decision-making. Current Biology, 24, R679–R681.
Vandekerckhove, J. (2014). A cognitive latent variable model for the simultaneous analysis of behavioral and personality data. Journal of Mathematical Psychology, 60, 58–71.
Wabersich, D., & Vandekerckhove, J. (2014). The RWiener package: an R package providing distribution functions for the Wiener diffusion model. The R Journal, 6, 49–56.
Murphy, P., Vandekerckhove, J., & Nieuwenhuis, S. (2014). Pupil-linked arousal determines variability in perceptual decision making. PLOS Computational Biology, 10, e1003854.
Lee, M. D., Newell, B., & Vandekerckhove, J. (2014). Modeling the adaptation of search termination in human decision making. Decision, 1, 223–251.
Zhang, S., Lee, M. D., Vandekerckhove, J., Maris, G., & Wagenmakers, E. (2014). Time-varying boundaries for diffusion models of decision making and response time. Frontiers in Psychology, 5, 1364.
Dutilh, G., Forstmann, B., Vandekerckhove, J., & Wagenmakers, E. (2013). A diffusion model account of age differences in posterror slowing. Psychology and Aging, 28, 64–76.
Vandekerckhove, J., Guan, M., & Styrcula, S. (2013). The consistency test may be too weak to be useful: Its systematic application would not improve effect size estimation in meta-analyses. Journal of Mathematical Psychology, 57, 170–173.
Pe, M., Vandekerckhove, J., & Kuppens, P. (2013). A diffusion model account of the relationship between the emotional flanker task and depression and rumination. Emotion, 13, 739–747.
Dutilh, G., Vandekerckhove, J., Forstmann, B., Keuleers, E., Brysbaert, M., & Wagenmakers, E. (2012). Testing theories of post-error slowing. Attention, Perception, & Psychophysics, 7, 454–465.
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2011). A hierarchical latent stochastic differential equation model for affective dynamics. Psychological Methods, 16, 468–490.
Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D. (2011). Hierarchical diffusion models for two-choice response times. Psychological Methods, 16, 44–62.
Vandekerckhove, J., Verheyen, S., & Tuerlinckx, F. (2010). A crossed random effects diffusion model for speeded semantic categorization data. Acta Psychologica, 133, 269–282.
Wetzels, R., Vandekerckhove, J., Tuerlinckx, F., & Wagenmakers, E. (2010). Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task. Journal of Mathematical Psychology, 54, 14–27.
Dutilh, G., Vandekerckhove, J., Tuerlinckx, F., & Wagenmakers, E. (2009). A diffusion model decomposition of the practice effect. Psychonomic Bulletin & Review, 16, 1026–1036.
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2009). A hierarchical Ornstein-Uhlenbeck model for continuous repeated measurement data. Psychometrika, 74, 395–418.
Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D. (2008). A Bayesian approach to diffusion process models of decision-making. Proceedings of the 30th Annual Conference of the Cognitive Science Society, 1429–1434.
Panis, S., De Winter, J., Vandekerckhove, J., & Wagemans, J. (2008). Identification of everyday objects on the basis of fragmented versions of outlines. Perception, 37, 271–289.
Vandekerckhove, J., & Tuerlinckx, F. (2008). Diffusion Model Analysis with MATLAB: A DMAT Primer. Behavior Research Methods, 40, 61–72.
Vandekerckhove, J., Panis, S., & Wagemans, J. (2007). The concavity effect is a compound of local and global effects. Perception & Psychophysics, 69, 1253–1260.
Spruyt, A., Hermans, D., De Houwer, J., Vandekerckhove, J., & Eelen, P. (2007). On the predictive validity of indirect attitude measures: Prediction of consumer choice behavior on the basis of affective priming in the picture--picture naming task. Journal of Experimental Social Psychology, 43, 599–610.
Vandekerckhove, J., & Tuerlinckx, F. (2007). Fitting the Ratcliff diffusion model to experimental data. Psychonomic Bulletin & Review, 14, 1011–1026.

Recorded presentations

Statistical power and evidence in the psychological literature: Presented at the National Academy of Sciences Arthur M. Sackler Colloquium on Reproducibility of Research: Issues and Proposed Remedies (Washington, D.C., March 2017)
Available via https://youtu.be/gMk984iYOcE.
Metastudies for robust tests of theory: Presented at the Interacting Minds Centre's Workshop on Open Science (Aarhus, Denmark, March 2019)
Available via https://youtu.be/JNfr8zELG1k.
Principal-component exploration of individual differences in the general-speed component of response times: Presented at Virtual MathPsych/ICCM 2022 (Virtual, July 2022)
Available via https://youtu.be/1FBg9AXaBdk.
Cognitive latent variable models: Presented at QuantDev Brownbag Meeting Fall 2023 (Virtual, September 2023)
Available via https://youtu.be/2XVd7bcXQ10.

Software

Bayesian bias correction: Calculates Bayes factors for simple designs under the possibility of publication bias.
Available via https://github.com/joachimvandekerckhove/bbc.
JAGS-Wiener: JAGS-Wiener is a JAGS module for the diffusion model.
Available via http://sourceforge.net/projects/jags-wiener/.
WinBUGS diffusion model plugin: The WinBUGS diffusion model plugin is a set of Component Pascal functions that implement the first hitting time distribution of a drift diffusion process as a stochastic node in WinBUGS.
Available via http://cidlab.com/files/winhdm.zip.
Bayesian Hierarchical Ornstein-Uhlenbeck model: BHOUM is a MATLAB toolbox for the joint analysis of two linked longitudinal variables, across multiple participants. The toolbox is freely available, either as a standalone program as a MATLAB package.
Available via https://sites.psu.edu/zitaoravecz/bayesian-ornstein-uhlenbeck-model/.
Bayesian Cultural Consensus Toolbox: BCCT is a user-friendly program for estimating psychometric models in the absence of a known answer key. Developed in MATLAB, BCCT is available as a stand-alone program, and requires no programming skill to use.
Available via https://sites.psu.edu/zitaoravecz/bayesian-cultural-consensus-theory/.
RWiener package: The RWiener package provides R functions for the Wiener diffusion model. It implements four new distribution functions dwiener, pwiener, qwiener and rwiener.
Available via http://sourceforge.net/projects/rwiener/.