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.
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.
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.
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.
Oravecz, Z., & Vandekerckhove, J. (in press). Quantifying evidence for---and against---Granger causality with Bayes factors. Multivariate Behavioral Research.
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).
Vo, K., Vishwanath, M., Srinivasan, R., Dutt, N., & Cao, H. (2022). Composing graphical models with generative adversarial networks for EEG signal modeling. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1231-1235.
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.
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.
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.
Sun, 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.
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.
Kott, A., Weisman, M. J., & Vandekerckhove, J. (2022). Mathematical modeling of cyber resilience. IEEE Military Communications Conference Proceedings.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zwaan, R., Etz, A., Lucas, R., & Donnellan, M. (2018). Making replication mainstream. Behavioral and Brain Sciences, 41, e120.
Ly, A., Raj, A., Etz, A., Marsman, M., & Wagenmakers, E. (2018). Bayesian reanalyses from summary statistics: A guide for academic consumers. Advances in Methods and Practices in Psychological Science, 1, 367–374.
Zwaan, R., Etz, A., Lucas, R., & Donnellan, M. (2018). Improving social and behavioral science by making replication mainstream: A response to commentaries. Behavioral and Brain Sciences, 41, e157.
Ly, A., Etz, A., Marsman, M., & Wagenmakers, E. (2018). Replication Bayes factors from evidence updating. Behavior Research Methods, 51, 2498–2508.
Etz, A., Gronau, Q., Dablander, F., Edelsbrunner, P., & Baribault, B. (2018). How to become a Bayesian in eight easy steps: An annotated reading list. Psychonomic Bulletin & Review, 25, 219–234.
Etz, A. (2018). Introduction to the concept of likelihood and its applications. Advances in Methods and Practices in Psychological Science, 1, 60–69.
Wagenmakers, E., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R., Gronau, Q., Dropmann, D., Boutin, B., Meerhol, F., Knight, P., Raj, A., van Kesteren, E., van Doorn, J., Smira, M., Epskamp, S., Etz, A., Matzke, D., Rouder, J., & Morey, R. (2018). Bayesian Inference for Psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25, 58–76.
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.
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.
Etz, A., & Vandekerckhove, J. (2018). Introduction to Bayesian inference for psychology. Psychonomic Bulletin & Review, 25, 5–34.
Bapat, A., Shafer-Skelton, A., Kupitz, C., & Golomb, J. (2017). Binding object features to locations: Does the "spatial congruency bias" update with object movement?. Attention, Perception, & Psychophysics, 79, 1682–1694.
Shafer-Skelton, A., Kupitz, C., & Golomb, J. (2017). Object-location binding across a saccade: A retinotopic spatial congruency bias. Attention, Perception, & Psychophysics, 79, 765–781.
Lakens, D., & Etz, A. (2017). Too true to be bad: When sets of studies with significant and non-significant findings are probably true. Social Psychological and Personality Science, 8, 875–881.
Etz, A., & Wagenmakers, E. (2017). J. B. S. Haldane's contribution to the Bayes factor hypothesis test. Statistical Science, 32, 313–329.
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., Nunez, P., & Srinivasan, R. (2016). Electroencephalography (EEG): neurophysics, experimental methods, and signal processing. Handbook of Neuroimaging Data Analysis, 175–197.
Holcombe, A., Brown, N., Goodbourn, P., Etz, A., & Geukes, S. (2016). Does sadness impair color perception? Flawed evidence and faulty methods. F1000Research.
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.
Oravecz, Z., Huentelman, M., & Vandekerckhove, J. (2016). Sequential Bayesian updating for Big Data. Big Data in Cognitive Science: From Methods to Insights, 13–33.
Subiaul, F., Krajkowski, E., Price, E., & Etz, A. (2015). Imitation by combination: Preschool age children evidence summative imitation in a novel problem-solving task. Frontiers in Psychology, 6.
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.
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.
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.
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.
Vandekerckhove, J., Matzke, D., & Wagenmakers, E. (2015). Model comparison and the principle of parsimony. Oxford Handbook of Computational and Mathematical Psychology, 300–317.
Guan, M., Lee, M. D., & Silva, A. (2014). Threshold models of human decision making on optimal stopping problems in different environments. Proceedings of the 36th Annual Conference of the Cognitive Science Society.
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.
Lee, M. D., Newell, B., & Vandekerckhove, J. (2014). Modeling the adaptation of search termination in human decision making. Decision, 1, 223–251.
Murphy, P., Vandekerckhove, J., & Nieuwenhuis, S. (2014). Pupil-linked arousal determines variability in perceptual decision making. PLOS Computational Biology, 10, e1003854.
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.
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.
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.
Chubb, C., Dickson, C., Dean, T., Fagan, C., Mann, D., Wright, C., Guan, M., Silva, A., Gregersen, P., & Kowalsky, E. (2013). Bimodal distribution of performance in discriminating major/minor modes. The Journal of the Acoustical Society of America, 134, 3067–3078.
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.
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.
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., 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.
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., Panis, S., & Wagemans, J. (2007). The concavity effect is a compound of local and global effects. Perception & Psychophysics, 69, 1253–1260.
Vandekerckhove, J., & Tuerlinckx, F. (2007). Fitting the Ratcliff diffusion model to experimental data. Psychonomic Bulletin & Review, 14, 1011–1026.
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.
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.
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.
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.
Oravecz, Z., & Vandekerckhove, J. (in press). Quantifying evidence for---and against---Granger causality with Bayes factors. Multivariate Behavioral Research.
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).
Vo, K., Vishwanath, M., Srinivasan, R., Dutt, N., & Cao, H. (2022). Composing graphical models with generative adversarial networks for EEG signal modeling. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1231-1235.
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.
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.
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.
Sun, 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.
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.
Kott, A., Weisman, M. J., & Vandekerckhove, J. (2022). Mathematical modeling of cyber resilience. IEEE Military Communications Conference Proceedings.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zwaan, R., Etz, A., Lucas, R., & Donnellan, M. (2018). Making replication mainstream. Behavioral and Brain Sciences, 41, e120.
Ly, A., Raj, A., Etz, A., Marsman, M., & Wagenmakers, E. (2018). Bayesian reanalyses from summary statistics: A guide for academic consumers. Advances in Methods and Practices in Psychological Science, 1, 367–374.
Zwaan, R., Etz, A., Lucas, R., & Donnellan, M. (2018). Improving social and behavioral science by making replication mainstream: A response to commentaries. Behavioral and Brain Sciences, 41, e157.
Ly, A., Etz, A., Marsman, M., & Wagenmakers, E. (2018). Replication Bayes factors from evidence updating. Behavior Research Methods, 51, 2498–2508.
Etz, A., Gronau, Q., Dablander, F., Edelsbrunner, P., & Baribault, B. (2018). How to become a Bayesian in eight easy steps: An annotated reading list. Psychonomic Bulletin & Review, 25, 219–234.
Etz, A. (2018). Introduction to the concept of likelihood and its applications. Advances in Methods and Practices in Psychological Science, 1, 60–69.
Wagenmakers, E., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R., Gronau, Q., Dropmann, D., Boutin, B., Meerhol, F., Knight, P., Raj, A., van Kesteren, E., van Doorn, J., Smira, M., Epskamp, S., Etz, A., Matzke, D., Rouder, J., & Morey, R. (2018). Bayesian Inference for Psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25, 58–76.
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.
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.
Etz, A., & Vandekerckhove, J. (2018). Introduction to Bayesian inference for psychology. Psychonomic Bulletin & Review, 25, 5–34.
Bapat, A., Shafer-Skelton, A., Kupitz, C., & Golomb, J. (2017). Binding object features to locations: Does the "spatial congruency bias" update with object movement?. Attention, Perception, & Psychophysics, 79, 1682–1694.
Shafer-Skelton, A., Kupitz, C., & Golomb, J. (2017). Object-location binding across a saccade: A retinotopic spatial congruency bias. Attention, Perception, & Psychophysics, 79, 765–781.
Lakens, D., & Etz, A. (2017). Too true to be bad: When sets of studies with significant and non-significant findings are probably true. Social Psychological and Personality Science, 8, 875–881.
Etz, A., & Wagenmakers, E. (2017). J. B. S. Haldane's contribution to the Bayes factor hypothesis test. Statistical Science, 32, 313–329.
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., Nunez, P., & Srinivasan, R. (2016). Electroencephalography (EEG): neurophysics, experimental methods, and signal processing. Handbook of Neuroimaging Data Analysis, 175–197.
Holcombe, A., Brown, N., Goodbourn, P., Etz, A., & Geukes, S. (2016). Does sadness impair color perception? Flawed evidence and faulty methods. F1000Research.
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.
Oravecz, Z., Huentelman, M., & Vandekerckhove, J. (2016). Sequential Bayesian updating for Big Data. Big Data in Cognitive Science: From Methods to Insights, 13–33.
Subiaul, F., Krajkowski, E., Price, E., & Etz, A. (2015). Imitation by combination: Preschool age children evidence summative imitation in a novel problem-solving task. Frontiers in Psychology, 6.
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.
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.
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.
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.
Vandekerckhove, J., Matzke, D., & Wagenmakers, E. (2015). Model comparison and the principle of parsimony. Oxford Handbook of Computational and Mathematical Psychology, 300–317.
Guan, M., Lee, M. D., & Silva, A. (2014). Threshold models of human decision making on optimal stopping problems in different environments. Proceedings of the 36th Annual Conference of the Cognitive Science Society.
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.
Lee, M. D., Newell, B., & Vandekerckhove, J. (2014). Modeling the adaptation of search termination in human decision making. Decision, 1, 223–251.
Murphy, P., Vandekerckhove, J., & Nieuwenhuis, S. (2014). Pupil-linked arousal determines variability in perceptual decision making. PLOS Computational Biology, 10, e1003854.
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.
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.
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.
Chubb, C., Dickson, C., Dean, T., Fagan, C., Mann, D., Wright, C., Guan, M., Silva, A., Gregersen, P., & Kowalsky, E. (2013). Bimodal distribution of performance in discriminating major/minor modes. The Journal of the Acoustical Society of America, 134, 3067–3078.
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.
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.
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., 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.
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., Panis, S., & Wagemans, J. (2007). The concavity effect is a compound of local and global effects. Perception & Psychophysics, 69, 1253–1260.
Vandekerckhove, J., & Tuerlinckx, F. (2007). Fitting the Ratcliff diffusion model to experimental data. Psychonomic Bulletin & Review, 14, 1011–1026.