Publications

Rouder, J. N., Haaf, J. M., & Aust, F. (2018). From theories to models to predictions: A Bayesian model comparison approach. Communication Monographs, 85(1), 41-56.

Rouder, J., & Haaf, J. (2018). Power, Dominance, and Constraint: A Note on the Appeal of Different Design Traditions. Advances in Methods and Practice in Psychological Science, 1(1), 19-26. [GitHub repository]

Rouder, J. N., Haaf, J. M., & Vandekerckhove, J. (2018). Bayesian Inference in Psychology, Part IV: Parameter estimation and Bayes factors. Psychonomic Bulletin and Review, 25(1), 102-113.

Heycke, T., Gehrmann, S. M., Haaf, J., & Stahl, C. (2018). Of two minds or one? A registered replication of Rydell et al. (2006). Emotion and Cognition, 32(8), 1708-1727. [OSF project]

Etz, A., Haaf, J. M., Rouder, J. N., & Vandekerckhove, J. (2018). Bayesian inference and testing any hypothesis you can specify. Advances in Methods and Practices in Psychological Science, 1(2), 281-295. [OSF project (with app)]

Quinn, R. K., James, M. H., Hawkins, G. E., Brown, A. L., Heathcote, A., Smith, D. W., Cairns, M. J., & Dayas, C. V. (2018). Temporally specific miRNA expression patterns in the dorsal and ventral striatum of addiction-prone rats. Addiction Biology, 23(2), 631–642.

Provost, A., Jamadar, S., Heathcote, A., Brown, S. D., & Karayanidis, F. (2018). Intertrial RT variability affects level of target-related interference in cued task switching. Psychophysiology, 55(3), e12971.

Weigard, A., Huang-Pollock, C., Brown, S., & Heathcote, A. (2018). Testing formal predictions of neuroscientific theories of ADHD with a cognitive model-based approach. Journal of Abnormal Psychology, 127(5), 529–539. [Supplementary Materials]

Osth, A. F., Jansson, A., Dennis, S., & Heathcote, A. (2018). Modeling the dynamics of recognition memory testing with an integrated model of retrieval and decision making. Cognitive Psychology, 104, 106–142. [Data]

Osth, A. F., Fox, J., McKague, M., Heathcote, A., & Dennis, S. (2018). The list strength effect in source memory: Data and a global matching model. Journal of Memory and Language, 103, 91–113. [Supplementary Data Set 1, Data Set 2]

Evans, N. J., Brown, S. D., Mewhort, D. J. K., & Heathcote, A. (2018). Refining the law of practice. Psychological Review, 125(4), 592–605. [Data]

Strickland, L., Loft, S., Remington, R. W., & Heathcote, A. (2018). Racing to remember: A theory of decision control in event-based prospective memory. Psychological Review, 125(6), 851–887. [Supplementary Material]

Palada, H., Neal, A., Tay, R., & Heathcote, A. (2018). Understanding the causes of adapting, and failing to adapt, to time pressure in a complex multistimulus environment. Journal of Experimental Psychology. Applied, 24(3), 380–399.

Weigard, A., Huang-Pollock, C., Heathcote, A., Hawk, L., & Schlienz, N. J. (2018). A cognitive model-based approach to testing mechanistic explanations for neuropsychological decrements during tobacco abstinence. Psychopharmacology, 235(11), 3115–3124. [Supplementary Materials]

Boehm, U., Steingroever, H., & Wagenmakers, E-J. (2018). Using Bayesian regression to test hypotheses
about relationships between parameters and covariates in cognitive models. Behavior Research Methods,
50 (3), 1248–1269.

Gronau, Q. F. & Singmann, H. (2018). bridgesampling: Bridge sampling for marginal likelihoods
and Bayes Factors. R package version 0.7-2.

Ly, A., Boehm, U., Heathcote, A., Turner, B.M., Forstmann, B., Marsman, M., & Matzke, D. (2018). A flexible and efficient hierarchical Bayesian approach to the exploration of individual differences in cognitive-model-based neuroscience. In A.A. Moustafa (Ed.), Computational models of brain and behavior (pp. 467-480). Wiley Blackwell.

Matzke, D., Boehm, U., & Vandekerckhove, J. (2018). Bayesian inference for psychology. Part III: Parameter estimation in nonstandard models. Psychonomic Bulletin & Review, 25, 77-101.

Wagenmakers, E.-J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, A.J., …,Gronau, Q.F., …, Matzke, D., et al. (2018). Bayesian inference for psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25, 58-76.

Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, A.J., Love, J., …, Matzke, D., et al. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review, 25, 35-57.

Sebastian, A., Forstmann, B.U., & Matzke, D. (2018). Towards a model-based cognitive neuroscience of stopping: A neuroimaging perspective. Neuroscience & Biobehavioral Reviews, 90, 130-136.

Matzke, D., Verbruggen, F., & Logan, G. (2018). The stop-signal paradigm. In E.-J. Wagenmakers & J.T. Wixted (Eds.), Stevens’ handbook of experimental psychology and cognitive neuroscience, Volume five: Methodology (4th ed., pp. 383-427). John Wiley & Sons, Inc.

Beek, T.F., Matzke, D., Pinto, Y., Rotteveel, M., Gierholz, A., Verhagen, J., et al. (2018). Incidental haptic sensations may not influence social judgements: A purely confirmatory replication attempt of Study 1 by Ackerman, Noreca, & Bargh (2010). Journal of Articles in Support of the Null Hypothesis, 14, 69-90.

Bridwell, D. A., Cavanagh, J. F., Collins, A. G., Nunez, M. D., Srinivasan, R., Stober, S., & Calhoun, V. D. (2018). Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior. Frontiers in Human Neuroscience, 12, 106.

Boehm, U., Marsman, M., Matzke, D., & Wagenmakers, E.-J. (2018). On the importance of avoiding shortcuts in applying cognitive models to hierarchical data. Behavioral Research Methods, 50, 1614-1631.

Boehm, U., Annis, J., Frank, M.J., Hawkins, G.E., Heathcote, A., Kellen, D., …, Matzke, D., & Wagenmakers, E.-J. (2018). Estimating between-trial variability parameters of the diffusion decision model: Expert advice and recommendations. Journal of Mathematical Psychology, 87, 46-75. [Supplementary Materials & Data]

Derks, K., Burger, J., van Doorn, J., Kossakowski, J.J., Matzke, D., Atticciati, L., et al. (2018). Network models to organize a dispersed literature: A case of misunderstanding analysis of covariance. Journal of European Psychology Students, 9, 48-57.

Nunez, M.D., Horton, C., Deng, s., Winter, W., & Srinivasan, R. (2018) – artscreenEEG: MATLAB repository to perform basic artifact correction on EEG data. MATLAB package version 0.14.2

Etz, A., Gronau, Q. F., Dablander, F., Edelsbrunner, P. A., & Baribault, B. (2018). How to become a Bayesian in eight easy steps: An annotated reading list. Psychonomic Bulletin & Review, 25(1), 219–234.

Aczel, B., Palfi, B., Szollosi, A., Kovacs, M., Szaszi, B., Szecsi, P., Zrubka, M., Gronau, Q. F., van den Bergh, D., & Wagenmakers, E.-J. (2018). Quantifying support for the null hypothesis in psychology: An empirical investigation. Advances in Methods and Practices in Psychological Science, 1(3), 357–366.

Ly, A., Raj, A., Etz, A., Marsman, M., Gronau, Q. F., & Wagenmakers, E.-J. (2018). Bayesian reanalyses from summary statistics: A guide for academic consumers. Advances in Methods and Practices in Psychological Science, 1(3), 367–374.

Gronau, Q. F., & Wagenmakers, E.-J. (2018). Bayesian evidence accumulation in experimental mathematics: A case study of four irrational numbers. Experimental Mathematics, 27(3), 277–286.

Osth, A. F., Dennis, S., & Heathcote, A. (2017). Likelihood ratio sequential sampling models of recognition memory. Cognitive Psychology, 92, 101–126. [Data & Code]

Evans, N. J., Howard, Z. L., Heathcote, A., & Brown, S. D. (2017). Model flexibility analysis does not measure the persuasiveness of a fit. Psychological Review, 124(3), 339–345.

Bushmakin, M. A., Eidels, A., & Heathcote, A. (2017). Breaking the rules in perceptual information integration. Cognitive Psychology, 95, 1–16.

Hawkins, G. E., Mittner, M., Forstmann, B. U., & Heathcote, A. (2017). On the efficiency of neurally-informed cognitive models to identify latent cognitive states. Journal of Mathematical Psychology, 76, 142–155. [R Code]

Thiele, J. E., Haaf, J. M., & Rouder, J. N. (2017). Is there variation across individuals in processing? Bayesian analysis for systems factorial technology. Journal of Mathematical Psychology, 81, 40-54. [Github]

Sense, F., Morey, C. C., Prince, M., Heathcote, A., & Morey, R. D. (2017). Opportunity for verbalization does not improve visual change detection performance: A state-trace analysis. Behavior Research Methods, 49(3), 853–862.

Haaf, J. M., & Rouder, J. N. (2017). Developing Constraint in Bayesian Mixed Models. Psychological Methods, 22(4), 779-798. [GitHub repository]

Houpt, J. W., Heathcote, A., & Eidels, A. (2017). Bayesian analyses of cognitive architecture. Psychological Methods, 22(2), 288–303.

Tillman, G., Osth, A. F., van Ravenzwaaij, D., & Heathcote, A. (2017). A diffusion decision model analysis of evidence variability in the lexical decision task. Psychonomic Bulletin & Review, 24(6), 1949–1956.

Tillman, G., Strayer, D., Eidels, A., & Heathcote, A. (2017). Modeling cognitive load effects of conversation between a passenger and driver. Attention, Perception, & Psychophysics, 79(6), 1795–1803. [Supplementary Materials]

Strickland, L., Heathcote, A., Remington, R. W., & Loft, S. (2017). Accumulating evidence about what prospective memory costs actually reveal. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(10), 1616–1629. [Supplementary Materials]

Osth, A. F., Bora, B., Dennis, S., & Heathcote, A. (2017). Diffusion vs. linear ballistic accumulation: Different models, different conclusions about the slope of the zROC in recognition memory. Journal of Memory and Language, 96, 36–61.

Grootswagers, T., Ritchie, J. B., Wardle, S. G., Heathcote, A., & Carlson, T. A. (2017). Asymmetric compression of representational space for object animacy categorization under degraded viewing conditions. Journal of Cognitive Neuroscience, 29(12), 1995–2010.

Evans, N. J., Hawkins, G. E., Boehm, U., Wagenmakers, E.-J. & Brown, S. D. (2017). The computations
that support simple decision-making: A comparison between the diffusion and urgency-gating models. Scientific Reports, 7 (1), 16433.

Matzke, D., Love, J., & Heathcote A. (2017). A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm. Behavior Research Methods, 49, 267-281. Download Supplementary Materials.

Wagenmakers, E.-J., Verhagen, A.J., Ly, A., Matzke, D., Steingroever, H., Rouder, J.N., & Morey, R.D. (2017). The need for Bayesian hypothesis testing in psychological science. In S.O. Lilienfeld & I. Waldman (Eds.), Psychological science under scrutiny: Recent challenges and proposed solutions (pp. 123-138). John Wiley and Sons.

Dutilh, G., Vandekerckhove, J., Ly, A., Matzke, D., Pedroni, A., Frey, R., et al. (2017). A test of the diffusion model explanation of the worst performance rule using preregistration and blinding. Attention, Perception, & Psychophysics, 79, 713-725.

Matzke, D., Hughes, M., Badcock, J.C., Michie, P., & Heathcote, A. (2017). Failures of cognitive control or attention? The case of stop-signal deficits in schizophrenia. Attention, Perception, & Psychophysics, 79, 1078-1086.

Krypotos, A.-M., Blanken, T.F., Arnaudova, I., Matzke, D., & Beckers, T. (2017). A primer on Bayesian analysis for experimental psychopathologists. Journal of Experimental Psychopathology, 8, 140-157.

Matzke, D., Ly, A., Selker, R., Weeda, W. D., Scheibehenne, B., Lee, M.D., & Wagenmakers, E.-J. (2017). Bayesian inference for correlations in the presence of estimation uncertainty and measurement error. Collabra: Psychology, 3(1), 25. [Supplementary Materials]

Gronau, Q. F., Sarafoglou, A., Matzke, D., Ly, A., Boehm, U., Marsman, M., et al. (2017). A tutorial on bridge sampling. Journal of Mathematical Psychology, 81, 80-97.

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(B), 117-130.

Gronau, Q. F., Erp, S. V., Heck, D. W., Cesario, J., Jonas, K. J., & Wagenmakers, E.-J. (2017). A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: The case of felt power. Comprehensive Results in Social Psychology, 2(1), 123–138.

Scheibehenne, B., Gronau, Q. F., Jamil, T., & Wagenmakers, E.-J. (2017). Fixed or random? A resolution through model averaging: Reply to Carlsson, Schimmack, Williams, and Bürkner (2017). Psychological Science, 28(11), 1698–1701.

Gronau, Q. F., Duizer, M., Bakker, M., & Wagenmakers, E.-J. (2017). Bayesian mixture modeling of significant p values: A meta-analytic method to estimate the degree of contamination from H₀. Journal of Experimental Psychology: General, 146(9), 1223–1233.

Sheu, C.-F., & Heathcote, A. (2001). A nonlinear regression approach to estimating signal detection models for rating data. Behavior Research Methods, Instruments, & Computers, 33(2), 108–114.

Kelly, A., Heathcote, A., Heath, R., & Longstaff, M. (2001). Response-Time Dynamics: Evidence for Linear and Low-Dimensional Nonlinear Structure in Human Choice Sequences. The Quarterly Journal of Experimental Psychology Section A, 54(3), 805–840.

Andrews, S., & Heathcote, A. (2001). Distinguishing common and task-specific processes in word identification: A matter of some moment? Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(2), 514–544.