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
Rammensee, R. A., Heathcote, A., & Basten, U. (2026). Positive bias in affective decision making: Neurocognitive mechanisms mediating links to resilience. Journal of Neuroscience. [Accepted for publication March 17]
In print
Boag, R.J., Strickland, L., Heathcote, A., & Loft, S. (2026). Quantifying the effects of prospective memory and ongoing task difficulty on capacity sharing and cognitive control. Memory and Cognition. https://doi.org/10.3758/s13421-026-01929-8
Strickland, L. Elliott, D. Loft, S., Stevenson, N., & Heathcote, A. (2026). Adaptive cognitive control in prospective memory. Journal of Experimental Psychology: Learning, Memory & Cognition. Advance online publication. https://doi.org/10.1037/xlm0001644
Strickland, L., Boag, R. J., Stevenson, N., & Heathcote, A. (2026). An illustrative guide to expressing cognitive theories using evidence accumulation modelling. Behavior Research Methods, 58, 101. https://doi.org/10.3758/s13428-026-02970-w
Molloy, M.F., Lee, T.G., Jonides, J., Zhang, H., Sellers, J., Heathcote, A., Sripada, C., & Weigard, A.S. (2026). Joint cognitive models reveal sources of robust individual differences in conflict processing. Computational Brain & Behavior. https://doi.org/10.1007/s42113-026-00263-1
Weigard, A., Molloy, M.F., Sripada, C., & Heathcote, A., (2026). The diffusion model’s drift rate parameter primarily reflects efficiency, rather than speed, of evidence accumulation. Psychonomic Bulletin & Review, 33. https://doi.org/10.3758/s13423-026-02861-3
Stevenson, N., Donzallaz, M.C., Innes, R., Forstmann B., Matzke, D., & Heathcote, A. (2026). Bayesian hierarchical cognitive modeling with the EMC2 package. Behavior Research Methods, 58, 35. https://doi.org/10.3758/s13428-025-02869-y
Donzallaz, M.C., Boehm, U., Heathcote, A., Donkin, C., Matzke, D., & Haaf, J.M. (2026). Comparing the reliability of individual differences for various measurement models in conflict tasks. Psychonomic Bulletin & Review, 33, 40. https://doi.org/10.3758/s13423-025-02801-7
Donzallaz, M.C., Stevenson, N., Heathcote, A., & Matzke, D. (2026). Disentangling within- and between-subject correlations in cognitive models: The essential role of hierarchical estimation. Behavior Research Methods, 58, 259.=
Ertekin, Ş.N., Hofman, A.D., van der Maas, H., Matzke, D., Streitberger, C., & Haaf, J.M. (2025). Extending empirical benchmarks of working memory to children: Insights from an adaptive learning environment. Developmental Psychology, 61(10), 1963–1990. https://doi.org/10.1037/dev0001992
Nunez, M. D., Schubert, A. L., Frischkorn, G. T., & Oberauer, K. (2025). Cognitive models of decision-making with identifiable parameters: Diffusion Decision Models with within-trial noise. Journal of Mathematical Psychology, 125. https://doi.org/10.1016/j.jmp.2025.102917.