Publications

Heathcote, A., Verbruggen, F., Boehler, C.N., & Matzke, D. (in press). Cognitive control of choices and action. In B.U. Forstmann & B. Turner (Eds.), An introduction to model-based cognitive neuroscience (2^nd ed.). Springer.
Starns, J. & Heathcote, A. (in press). Evidence accumulation and decision processes. Oxford Handbook of Human Memory.
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.
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
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., 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.
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.
Narra, M., Heathcote, A., & Finkbeiner, M. (2016). Time course differences between bilinguals and monolinguals in the Simon task. In J. B. Schwieter (Ed.), Cognitive control and consequences of multilingualism (pp. 399–427). John Benjamins Publishing.
Nunez, M. D., Nunez, P. L., & Srinivasan, R. (2016). Electroencephalography (EEG), neurophysics, experimental methods, and signal processing. In Ombao, H., Linquist, M., Thompson, W. & Aston, J. (Eds.) Handbook of Neuroimaging Data Analysis (pp. 175-197), Chapman & Hall/CRC.
Donkin, C., Rae, B., Heathcote, A. & Brown, S.D. (2015). Why is accurately labelling simple magnitudes so hard? In J. R. Busemeyer, J. T. Townsend, Z. J. Wang, & A. Eidels, (Eds.), Oxford handbook of computational and mathematical psychology (pp. 121-141). Oxford University Press.
Heathcote, A., Brown, S.D. & Wagenmakers, E.-J. (2015). An introduction to good practices in cognitive modeling. In B. U. Forstmann, & E.-J. Wagenmakers (Eds.), An introduction to model-based cognitive neuroscience (pp. 25-48). Springer: New York.
Vandekerckhove, J., Matzke, D., & Wagenmakers, E.-J. (2015). Model comparison and the principle of parsimony. In J. R. Busemeyer, J. T. Townsend, Z. J. Wang, & A. Eidels (Eds.), The Oxford handbook of computational and mathematical psychology (pp. 300-319). Oxford University Press. [code for computing MDL and Bayes factors]
Gronau, Q. F. & Oreel, T. (2014). Including prior information in Bayesian procedures. In H. J., Adér, G. J.
& Mellenbergh (Eds.), Advising on research methods: Selected topics (pp. 67-85). Johannes van
Kessel Publishing: Huizen.
Matzke, D., Lee, M.D., & Wagenmakers, E.-J. (2013). Multinomial processing trees. In M.D. Lee, & E.-J. Wagenmakers, Bayesian cognitive modeling: A practical course (pp. 187-195). Cambridge University Press.
Matzke, D., Lee, M.D., & Wagenmakers, E.-J. (2013). Signal detection theory: Parameter expansion. In M.D. Lee, & E.-J. Wagenmakers, Bayesian cognitive modeling: A practical course (pp. 164-167). Cambridge University Press.
Matzke, D., Lee, M.D., & Wagenmakers, E.-J. (2013). Getting started with WinBUGS. In M.D. Lee, & E.-J. Wagenmakers, Bayesian cognitive modeling: A practical course (pp. 16-34). Cambridge University Press.
Heathcote, A., & Elliott, D. (2010). Nonlinear dynamical analysis of noisy time series. In S. J. Guastello & R. A. M. Gregson (Eds.), Nonlinear dynamical systems analysis for the behavioral sciences using real data (pp. 117–148). CRC Press.