Welcome to the
Amsterdam Mathematical
Psychology Laboratory
The primary aim of the Amsterdam Mathematical Psychology Laboratory (AMPL) is the experimental investigation of cognitive processes that underpin decision making, response inhibition, language, attention, memory, and learning. AMPL has two broad foci: (1) mathematical and computational cognitive models; and (2) the application of cognitive psychology to applied problems.
Recent Publications
In press
Ciobanu, L.G., Stankov, L., Ahmed, M., Heathcote, A., Clark, S.R., & Aidman, E. (2023). Multifactorial structure of cognitive assessment tests in the UK Biobank: A combined exploratory factor and structural equation modelling analyses. Frontiers in Psychology: Cognition.
Strickland, L., Boag, R.J., Heathcote, A., Bowden, V., & Loft, S. (2022). Automated decision aids: When are they advisors and when do they take control of human decision making? Journal of Experimental Psychology: Applied.
Isherwood, S.J.S, Bazin, P.L., Miletic, S., Stevenson, N.R., Trutti, A.C., Tse, D.H.Y, Heathcote, A., Matzke, D., Innes, R.J., Habli, S., Sokolowski, D.R., Alkemade, A., Haberg, A.K., & Forstmann, B.U. (2023). Investigating intra-individual networks of response inhibition and interference resolution using 7T MRI. NeuroImage.
van Doorn, J., Haaf, J.M., Stefan, A.M., Wagenmakers, E.-J., Cox, G.E., Davis-Stober, C.P., Heathcote, A., Heck, D., Kalish, M., Kellen, D., Matzke, D., Morey, R.D., Nicenboim, B., van Ravenzwaaij, D., Rouder, J.N., Shiffrin, R.M., Schad, D.J., Singmann, H., Vasishth, S., Verissimo, J., Chandramouli, S., Dunn, J.C., Gronau, Q.F., Navarro, D., Yadav, H., Bockting, F., Linde, M., McMullin, S.D., Schnuerch, M., & Aust. F. (2023). Bayes factors for mixed models: A discussion. Computational Brain & Behavior, 6, 140–158.
Kvam, P., Marley, A. A. J., & Heathcote, A. (2022). A unified theory of discrete and continuous responding. Psychological Review.
Castro, S., Heathcote, A., Cooper, J., & Strayer, D. (accepted 16/3/2022). Dynamic workload measurement and modeling: Driving and conversing. Journal of Experimental Psychology: Applied.
*Boehm, U., Cox, S., Gantner, G. & Stevenson, R. (in press). Fast solutions for the first-passage distribution
of diffusion models with space-time-dependent drift functions and time-dependent boundaries.
Journal of Mathematical Psychology, 105, 102613. (*authors listed alphabetically) [OSF repository]Heck, D. W., Boehm, U., Boeing-Messing, F., Buerkner, P.-C., Derks, K.,…, & Hoijtink, H. (in press).
A review of applications of the Bayes factor in psychological research. Psychological Methods.Boehm, U., Marsman, M., van der Maas, H. L. J., Maris, G. (in press). An attention-based diffusion
model for psychometric analyses. Psychometrika.Sarafoglou, A., Haaf, J. M., Ly, A., Gronau, Q. F., Wagenmakers, E., & Marsman, M. (in press). Evaluating Multinomial Order Restrictions with Bridge Sampling. Psychological Methods.
In print
Elliott, J. G. C., Gilboa-Schechtman, E., Grigorenko, E. L., Heathcote, A., Purdie-Greenaway, V. J., Uddin, L. Q., van der Maas, H. L. J., & Waldmann, M. R. (2022). Editorial. Psychological Review, 129, 1-3.
Ballard, T., Neal, A., Farrell, S., Lloyd, E., Lim, J., & Heathcote, A. (2022). A general architecture for modeling the dynamics of goal-directed motivation and decision making. Psychological Review, 129, 146-174.
Albertella, L., Kirkham R, …, Heathcote, A., …, & Yücel M. (2023). Building a transdisciplinary expert consensus on the cognitive drivers of performance under pressure: An international multi-panel Delphi study. Frontiers in Psychology: Performance Science, 13, 1017675.
Bartoš, F., Aust, F., & Haaf, J. M. (2022). Informed Bayesian survival analysis. BMC Medical Research Methodology, 22, 238.
Rouder, J. N., Schnuerch, M., Haaf, J. M., & Morey, R. D. (2022). Principles of Model Specification in ANOVA Designs. Computational Brain & Behavior, 1-14.
Damaso, K. A. M., Williams, P. G., & Heathcote, A. (2022). What happens after a fast versus slow error, and how does it relate to evidence accumulation? Computational Brain & Behavior, 5, 527–546.
Boag, R.J., Strickland, L., Heathcote, A., Neal, A., Palada, H., & Loft, S. (2022). Evidence accumulation modelling in the wild: Understanding safety-critical decisions. Trends in Cognitive Sciences, 27, 175-188.
Kumar, A., Benjamin, A. S., Heathcote, A., & Steyvers, M. (2022). Comparing models of learning and relearning in large-scale cognitive training data sets. npj Science of Learning, 7, 24.
Aidman, E., Fogarty, G. J., Crampton, J., Bond, J., Taylor, P., Heathcote, A., & Zaichkowsky, L. (2022). An app-enhanced cognitive fitness training program for athletes: The rationale and validation protocol. Frontiers in Psychology, 13, 957551.
Choo, Y., Matzke, D., Bowren, M. D, Tranel, D., & Wessel, J. R. (2022). Right inferior frontal gyrus damage is associated with impaired initiation of inhibitory control, but not its implementation. eLife, 11:e79667.