Publications

Snyder, H. K., Rafferty, S. M., Haaf, J. M., & Rouder, J. N. (2019). Common or Distinct Attention Mechanisms for Contrast and Assimilation. Attention, Perception, & Psychophysics, 81(6). 1944-1950.

Rouder, J. N., Haaf, J. M., & Snyder, H. K. (2019). Minimizing Mistakes In Psychological Science. Advances in Methods and Practices in Psychological Science, 2(1), 3-11.

Rouder, J. N., Haaf, J. M., Davis-Stober, C., & Hilgard, J. (2019). Beyond overall effects: A Bayesian approach to finding constraints across a collection of studies in meta-analysis. Psychological Methods, 24(5), 606–621.

Rouder, J. N., & Haaf, J. M. (2019). A Psychometrics of Individual Differences in Experimental Tasks. Psychonomic Bulletin and Review, 26(2), 452-467.

Haaf, J. M., & Rouder, J. N. (2019). Some do and some don’t? Accounting for variability of individual difference structures. Psychonomic Bulletin and Review, 26(3), 772-789.

Haaf, J. M., Ly, A., & Wagenmakers, E.-J. (2019). Retire significance, but still test hypotheses. Nature, 567, 461.

Aust, F., Haaf, J. M., & Stahl, C. (2019). A memory-based judgment account of expectancy-liking dissociations in evaluative conditioning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(3), 417-439. [OSF project]

Heathcote, A., Holloway, E., & Sauer, J. (2019). Confidence and varieties of bias. Journal of Mathematical Psychology, 90, 31–46.

Dunn, J. C., Heathcote, A., & Kalish, M. (2019). Special issue on state-trace analysis. Journal of Mathematical Psychology, 90, 1–2. [Full Issue]

Lin, Y.-S., Heathcote, A., & Holmes, W. R. (2019). Parallel probability density approximation. Behavior Research Methods, 51(6), 2777–2799. [R package]

Garton, R., Reynolds, A., Hinder, M. R., & Heathcote, A. (2019). Equally flexible and optimal response bias in older compared to younger adults. Psychology and Aging, 34(6), 821–835. [Supplementary Materials]

Boag, R. J., Strickland, L., Heathcote, A., Neal, A., & Loft, S. (2019). Cognitive control and capacity for prospective memory in complex dynamic environments. Journal of Experimental Psychology. General, 148(12), 2181–2206.

Bird, L., Gretton, M., Cockerell, R., & Heathcote, A. (2019). The cognitive load of narrative lies. Applied Cognitive Psychology, 33(5), 936–942.

Hawkins, G. E., Mittner, M., Forstmann, B. U., & Heathcote, A. (2019). Modeling distracted performance. Cognitive Psychology, 112, 48–80. [Supplementary Material & Code]

Dutilh, G., Annis, J., Brown, S. D., Cassey, P., Evans, N. J., Grasman, R. P. P. P., Hawkins, G. E., Heathcote, A., Holmes, W. R., Krypotos, A.-M., Kupitz, C. N., Leite, F. P., Lerche, V., Lin, Y.-S., Logan, G. D., Palmeri, T. J., Starns, J. J., Trueblood, J. S., van Maanen, L., … Donkin, C. (2019). The quality of response time data inference: A blinded, collaborative assessment of the validity of cognitive models. Psychonomic Bulletin & Review, 26(4), 1051–1069. [Supplementary Materials]

Strickland, L., Elliott, D., Wilson, M. D., Loft, S., Neal, A., & Heathcote, A. (2019). Prospective memory in the red zone: Cognitive control and capacity sharing in a complex, multi-stimulus task. Journal of Experimental Psychology: Applied, 25(4), 695–715.

Palada, H., Neal, A., Strayer, D., Ballard, T., & Heathcote, A. (2019). Using response time modeling to understand the sources of dual-task interference in a dynamic environment. Journal of Experimental Psychology: Human Perception and Performance, 45(10), 1331–1345.

Starns, J. J., Cataldo, A. M., Rotello, C. M., Annis, J., Aschenbrenner, A., Bröder, A., Cox, G., Criss, A., Curl, R. A., Dobbins, I. G., Dunn, J., Enam, T., Evans, N. J., Farrell, S., Fraundorf, S. H., Gronlund, S. D., Heathcote, A., Heck, D. W., Hicks, J. L., … Wilson, J. (2019). Assessing theoretical conclusions with blinded inference to investigate a potential inference crisis. Advances in Methods and Practices in Psychological Science, 2(4), 335–349.

Weigard, A., Heathcote, A., & Sripada, C. (2019). Modeling the effects of methylphenidate on interference and evidence accumulation processes using the conflict linear ballistic accumulator. Psychopharmacology, 236(8), 2501–2512. [Data & Code]

Boag, R. J., Strickland, L., Loft, S., & Heathcote, A. (2019). Strategic attention and decision control support prospective memory in a complex dual-task environment. Cognition, 191, 103974.

Heathcote, A. (2019). What do the rules for the wrong game tell us about how to play the right game? Computational Brain & Behavior, 2(3), 187–189.

Osth, A. F., Dunn, J. C., Heathcote, A., & Ratcliff, R. (2019). Two processes are not necessary to understand memory deficits. Behavioral and Brain Sciences, 42.

Gronau, Q. F., Raj K. N., A., & Wagenmakers, E.-J. (2019). abtest: Bayesian A/B testing. R package
version 0.2.0.

Strickland, L., Loft, S., & Heathcote, A. (2019). Evidence accumulation modeling of event-based prospective memory. In J. Rummel & M.A. McDaniel (Eds.), Current issues in memory: Prospective memory (pp. 78-94). Taylor & Francis.

Schubert, A. L., 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(2), 64-84.

Nunez, P. L., Nunez, M. D., & Srinivasan, R. (2019). Multi-Scale Neural Sources of EEG: Genuine, Equivalent, and Representative. A Tutorial Review. Brain Topography, 1-22.

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.

Skippen, P., Matzke, D., Heathcote, A., Fulham, W.R., Michie, P., Karayanidis, F. (2019). Reliability of triggering inhibitory process is a better predictor of impulsivity than SSRT. Acta Psychologica, 192, 104-117.

Gronau, Q.F., Wagenmakers, E.-J., Heck, D.W., & Matzke, D. (2019). A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling. Psychometrika, 84, 261-284.

Love, J., Selker, R., Marsman, M., Jamil, T., Dropmann, D., Verhagen, J., Ly, A., Gronau, Q. F., …, Matzke, D., …, & Wagenmakers, E.-J. (2019). JASP- Graphical statistical software for common statistical designs. Journal of Statistical Software, 88, 1-17.

Matzke, D., Curley, S., Gong, C.Q., & Heathcote, A. (2019). Inhibiting responses to difficult choices. Journal of Experimental Psychology: General, 148, 124-142. [Supplementary Materials]

Heathcote, A., Lin, Y., Reynolds, A., Strickland, L., Gretton, M., & Matzke, D. (2019). Dynamic models of choice. Behavior Research Methods, 51, 961-985. [Software]

Castro, S., Strayer, D., Matzke, D., & Heathcote, A. (2019). Cognitive workload measurement and modeling under divided attention. Journal of Experimental Psychology: Human Perception and Performance, 45, 826-839.

Stephens, R.G., Matzke, D., & Hayes, B.K. (2019). Disappearing dissociations in experimental psychology: Using state-trace analysis to test for multiple processes. Journal of Mathematical Psychology, 90, 3–22.

Weigard, A., Heathcote, A., Matzke, D., & Huang-Pollock, C. (2019). Cognitive modeling suggests that attentional failures drive longer stop-signal reaction time estimates in attention deficit/hyperactivity disorder. Clinical Psychological Science, 7, 856-872. [Code]

Verbruggen, F., Aron, A.R., Band, G.P.H., Beste, C., Bissett, P.G., Brockett, A.T., …, Heathcote, A., …, Matzke, D., …, Boehler, C.N. (2019). A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. eLIFE, 8, e46323. [Supplementary Materials]

Lee, M., Criss, A.H., Devezer, B., Donkin, C., Etz, A., Leite,F., Matzke, D., …, Vandekerckhove, J. (2019). Robust modeling in cognitive science. Computational Brain & Behavior, 2, 141–153.

Vandekerckhove, J., White, C.N., Trueblood, J.S., Rouder, J.N., Matzke, D., Leite, F.P., …, Lee., M.D. (2019). Robustness and diversity in cognitive modeling. Computational Brain & Behavior, 2, 271–276.

Marsman, M., Tanis, C. C., Bechger, T. M., & Waldorp, L. J. Network Psychometrics in Educational Practice. In B. P. Veldkamp & C. Sluijter (Eds.). Theoretical and Practical Advances in Computer-based Educational Measurement (pp. 93–120). Cham: Springer International Publishing

Gronau, Q. F., & Wagenmakers, E.-J. (2019). Limitations of Bayesian leave-one-out cross-validation for model selection. Computational Brain & Behavior, 2(1), 1–11.

Gronau, Q. F., & Wagenmakers, E.-J. (2019). Rejoinder: More limitations of Bayesian leave-one-out cross-validation. Computational Brain & Behavior, 2(1), 35–47.

Boffo, M., Zerhouni, O., Gronau, Q. F., van Beek, R. J. J., Nikolaou, K., Marsman, M., & Wiers, R. W. (2019). Cognitive bias modification for behavior change in alcohol and smoking addiction: Bayesian meta-analysis of individual participant data. Neuropsychology Review, 29(1), 52–78.

Dongen, N. N. N. van, Doorn, J. B. van, Gronau, Q. F., Ravenzwaaij, D. van, Hoekstra, R., Haucke, M. N., Lakens, D., Hennig, C., Morey, R. D., Homer, S., Gelman, A., Sprenger, J., & Wagenmakers, E.-J. (2019). Multiple perspectives on inference for two simple statistical scenarios. The American Statistician, 73(sup1), 328–339.

Heck, D. W., Overstall, A. M., Gronau, Q. F., & Wagenmakers, E.-J. (2019). Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models. Statistics and Computing, 29(4), 631–643.

Stefan, A. M., Gronau, Q. F., Schönbrodt, F. D., & Wagenmakers, E.-J. (2019). A tutorial on Bayes Factor Design Analysis using an informed prior. Behavior Research Methods, 51(3), 1042–1058.

Carr, S. C., MacLachlan, M., Heathcote, A., & Heath, R. A. (1997). The approaches to study inventory in Malawi: A lesson for educational testing? Psychological Teaching Review, 6, 157–164.

Heathcote, A., Popiel, S. J., & Mewhort, D. J. (1991). Analysis of response time distributions: An example using the Stroop task. Psychological Bulletin, 109(2), 340–347.