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
Jun, E. J.*, Bautista, A. R.*, Nunez, M. D.*, Allen, D. C., Tak, J. H., Alvarez, E., Basso, M. A. (2021). Causal role for the primate superior colliculus in the computation of evidence for perceptual decisions. Nature Neuroscience
*Contributed Equally
Ballard, T., Neal, A., Farrell, S., Lloyd, E., Lim, J. & Heathcote, A. (in press). A general architecture for modeling the dynamics of goal-directed motivation and decision making. Psychological Review.
Shiffrin, R. M., Matzke, D., Crystal, J. D., Wagenmakers, E.-J., Chandramouli, S. H., Vandekerckhove, J., Zorzi, M., Morey, R. D., & Murphy, M. C. (in press). Extraordinary claims, extraordinary evidence? A discussion. Learning & Behavior.
Gronau, Q. F., Heck, D. W., Berkhout, S. W., Haaf, J. M., & Wagenmakers, E.-J. (in press). A primer on
Bayesian model-averaged meta-analysis. Advances in Methods and Practices in Psychological Science.Starns, J. & Heathcote, A. (in press). Evidence accumulation and decision processes. Oxford Handbook of Human Memory.
Strickland, L., Heathcote, A., Bowden, V., Boag, R., Wilson, M. D., Khan, S. & Loft, S. (in press). Inhibitory cognitive control allows automated advice to improve accuracy while minimizing misuse. Psychological Science.
Strickland, L., Heathcote, A., Humphreys, M. S., & Loft, S. (in press). Target learning in event-based prospective memory. Journal of Experimental Psychology: Learning, Memory, and Cognition.
Wagenmakers, E.-J., Gronau, Q. F., Dablander, F., & Etz, A. (in press). The support interval. Erkenntnis.
Gronau, Q. F., Raj, K. N. A., & Wagenmakers, E.-J. (in press). Informed Bayesian inference for the A/B test. Journal of Statistical Software.
He, J. L., Hirst, R. J., Puri, R., Coxon, J., Byblow, W., Hinder, M., Skippen, P., Matzke, D., Heathcote, A., Wadsley, C. G., Hyde, C., Parmar, D., Pedapati, E., Gilbert, D. L., Huddleston, D. A., Mostofsky, S., Leunissen, I., MacDonald, H. J., Chowdhury, N. S., Gretton, M., Nikitenko, T., Zandbelt, B., Strickland, S., Puts, N. A. J. (in press). OSARI, an open-source anticipated response inhibition task. Behavioral Research Methods.
In print
Charupanit, K., Nunez, M. D., Bernardo, D., Bebin, E. M., Krueger, D. Northrup, H., Sahin, M., Wu, J. Y., & Lopour, B. A. (2018). Automated Detection of High Frequency Oscillations in Human Scalp Electroencephalogram. International Conference of the IEEE Engineering in Medicine and Biological Society. Honolulu, Hawaii. July 2018.
Nunez, M. D., Gosai, A., Vandekerckhove, J. & Srinivasan, R. (2017). EEG measures of neural processing speed reflect human visual encoding time. Conference on Cognitive Computational Neuroscience. New York, New York. September 2017.
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.
Lui, K. K., Nunez, M. D., Cassidy, J. M., Vandekerckhove, J., Cramer, S. C., & Srinivasan, R. (2020). Timing of readiness potentials reflect a decision-making process in the human brain. Computational Brain & Behavior, 1-20. [Data & Analysis Code]
Nunez, M. D., Gosai, A., Vandekerckhove, J., & Srinivasan, R. (2019). The latency of a visual evoked potential tracks the onset of decision making. NeuroImage, 197, 93-108.
Nunez, P. L., Nunez, M. D., & Srinivasan, R. (2019). Multi-Scale Neural Sources of EEG: Genuine, Equivalent, and Representative. A Tutorial Review. Brain Topography, 1-22.
Schubert, A. L., Nunez, M. D., Hagemann, D., & Vandekerckhove, J. (2019). Individual differences in cortical processing speed predict cognitive abilities: A model-based cognitive neuroscience account. Computational Brain & Behavior, 2(2), 64-84.
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.
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.
Nunez, M. D., Srinivasan, R. & Vandekerckhove, J. (2015). Individual differences in attention influence perceptual decision making. Frontiers in Psychology, 8, 18.