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.

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.

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.

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.

Prince, M., Brown, S., & Heathcote, A. (2012). The design and analysis of state-trace experiments. Psychological Methods, 17(1), 78–99.

Heathcote, A., & Hayes, B. (2012). Diffusion versus linear ballistic accumulation: Different models for response time with different conclusions about psychological mechanisms? Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 66(2), 125–136.

Heathcote, A., & Love, J. (2012). Linear deterministic accumulator models of simple choice. Frontiers in Psychology, 3.

Prince, M., Hawkins, G., Love, J., & Heathcote, A. (2012). An R package for state-trace analysis. Behavior Research Methods, 44(3), 644–655.

Todd, J., Provost, A., Whitson, L. R., Cooper, G., & Heathcote, A. (2012). Not so primitive: Context-sensitive meta-learning about unattended sound sequences. Journal of Neurophysiology, 109(1), 99–105.

Heathcote, A., & Elliott, D. (2005). Nonlinear dynamical analysis of noisy time series. Nonlinear Dynamics, Psychology, and Life Sciences, 9(4), 399–433.

Brown, S., & Heathcote, A. (2005). A ballistic model of choice response time. Psychological Review, 112(1), 117–128.

Brown, S., & Heathcote, A. (2005). Practice increases the efficiency of evidence accumulation in perceptual choice. Journal of Experimental Psychology. Human Perception and Performance, 31(2), 289–298.

Brown, S., & Heathcote, A. (2003). Averaging learning curves across and within participants. Behavior Research Methods, Instruments, & Computers, 35(1), 11–21.

Brown, S., & Heathcote, A. (2003). QMLE: Fast, robust, and efficient estimation of distribution functions based on quantiles. Behavior Research Methods, Instruments, & Computers, 35(4), 485–492.

Brown, S., & Heathcote, A. (2003). Bias in exponential and power function fits due to noise: Comment on Myung, Kim, and Pitt. Memory & Cognition, 31(4), 656–661.

Heathcote, A. (2003). Item recognition memory and the receiver operating characteristic. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(6), 1210–1230.

Karayanidis, F., Coltheart, M., Michie, P. T., & Murphy, K. (2003). Electrophysiological correlates of anticipatory and poststimulus components of task switching. Psychophysiology, 40(3), 329–348.