Michael D. Nunez

I work as an assistant professor in the Psychological Methods Unit of the University of Amsterdam. I received my PhD in Psychology with a concentration in Cognitive Neuroscience from the University of California, Irvine in 2017 and a MS in Statistics from the same university in 2015.

In general my research focus is on model-based Cognitive Neuroscience, such that I use mathematical modelling to understand the brain and mind. I am currently working on two lines of research: (1) to further understanding of decision making and cognitive differences across individuals and (2) to classify and model markers of epilepsy and related cognitive impairments. I evaluate electrical signals recorded from the brain (often using scalp-recorded EEG) while also evaluating human behaviour during experiments. To answer specific scientific questions with this data, I often use signal processing methods and Bayesian hierarchical statistical modelling.

My CV

Publications by Michael D. Nunez

Sun, Q. J., Vo, K., Lui, K., Nunez, M., Vandekerckhove, J., & Srinivasan, R. (2022, July). Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signals. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-9). IEEE.
Ghaderi-Kangavari, A., Rad, J. A., & Nunez, M. D. (2023). A general integrative neurocognitive modeling framework to jointly describe EEG and decision-making on single trials. Computational Brain & Behavior, 1-60.
Ghaderi-Kangavari, A., Rad, J. A., Parand, K., & Nunez, M. D. (2022). Neuro-cognitive models of single-trial EEG measures describe latent effects of spatial attention during perceptual decision making. Journal of Mathematical Psychology, 111, 102725.
Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2022). A tutorial on fitting joint models of M/EEG and behavior to understand cognition. Preprint.
Nunez, M. D., Charupanit, K., Sen-Gupta, I., Lopour, B. A., & Lin, J. J. (2022). Beyond rates: Time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone. Journal of Neural Engineering, 19, 016034.
Ghaderi-Kangavari, A., Parand, K., Ebrahimpour, R., Nunez, M. D., Rad, J. A. (2021). How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis. Manuscript submitted for publication.
Nunez, M. D., Charupanit, K., Sen-Gupta, I., Lopour, B. A., Lin, J. J. Towards automatic classification of pathological epileptic tissue with interictal high-frequency oscillations. Manuscript submitted for publication.
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]
Weigard, A., Matzke, D., Tanis, C., & Heathcote, A. (2023). Cognitive process modeling addresses context independence violations in the ABCD study stop-signal task. Developmental Cognitive Neuroscience, 59, 101191.
Nunez, M.D. (2020). pyhddmjags: example Hierarchical Drift Diffusion Model (HDDM) code using JAGS in Python. Repository version 0.4.4
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.
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., Horton, C., Deng, s., Winter, W., & Srinivasan, R. (2018) – artscreenEEG: MATLAB repository to perform basic artifact correction on EEG data. MATLAB package version 0.14.2
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., 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., 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., 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.
Nunez, M. D., Srinivasan, R. & Vandekerckhove, J. (2015). Individual differences in attention influence perceptual decision making. Frontiers in Psychology, 8, 18.