Schedule

Important Dates

  • Workshop dates: July 27–31, 2026
  • Poster session: Wednesday, July 29, 3:00–5:00 pm (boards, tacks, and easels provided; set up just before the session)

Schedule at a Glance

Time Monday Tuesday Wednesday Thursday Friday
09:00–10:45 Opening + participant lightning talks Bayesian inference + traditional and amortized Bayesian computation SBI tools: MATLAB, HSSM + BayesFlow Joint modeling with behavioral, EEG + fMRI data Group project preparation
11:00–13:00 Cognitive modeling + Bayesian inference Breakout meetings + PyMC tutorial Hierarchical MCMC + recurrent networks Moderational learning + hierarchical modeling with diffusion models Group reports + feedback and discussion
13:00–14:00 Lunch Lunch Lunch Lunch Lunch
14:00–16:00 Machine learning, SBI + latent variable modeling Hands-on simulation, parameter estimation, MCMC + neural methods Variational autoencoders + poster session Joint modeling workshop + breakout meetings Research opportunities, future directions + roundtable
16:00– Breakout group formation Application breakouts + Q&A Poster session (to 17:00) Panel discussion Closing / farewell

Day-by-Day Program

Day 1 — Monday, July 27: Introduction to computational cognitive modeling

  • 09:00 Opening remarks by organizers
  • 09:15 Lightning talk introductions by participants (1–2 min each)
  • 10:45 Morning coffee / tea
  • 11:00 Cognitive modeling (Pan) — Reading: Wilson & Collins (2019) · slides
  • 12:00 Bayesian inference (Turner) — Reading: Rouder & Province (2019) · slides, first half · R scripts
  • 13:00 Lunch
  • 14:00 Machine learning (Kvam) — Reading: Kvam et al. (2024) · slides
  • 14:30 Simulation-based inference (Radev) — Reading: Cranmer et al. (2020)
  • 15:30 Latent variable modeling (Sokratous) — Reading: Khemakhem et al. (2020) slides
  • 16:00 Formation of breakout groups (~5 participants per group, matched with an organizer by topic)

Day 1 Recordings

  • Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. eLife, 8, e49547.
  • Rouder, J. N., & Province, J. M. (2019). Bayesian hierarchical models in psychological science: A tutorial. New Methods in Cognitive Psychology, 32–66.
  • Kvam, P. D., Sokratous, K., Fitch, A., & Hintze, A. (2024). Using artificial intelligence to fit, compare, evaluate, and discover computational models of decision behavior. Decision, 11(4), 599–618.
  • Cranmer, K., Brehmer, J., & Louppe, G. (2020). The frontier of simulation-based inference. PNAS, 117(48), 30055–30062.
  • Khemakhem, I., Kingma, D., Monti, R., & Hyvarinen, A. (2020). Variational autoencoders and nonlinear ICA: A unifying framework. AISTATS, 2207–2217.

Day 2 — Tuesday, July 28: Bayesian inference and simulation-based methods

  • 09:00 Bayes’ rule, parameter estimation, model comparison (Turner) — Reading: Turner & Van Zandt (2012) · slides, second half · R scripts
  • 10:00 Traditional and amortized Bayesian computation (Radev) — Reading: Bürkner et al. (2023)
  • 10:45 Morning coffee / tea
  • 11:00 Breakout group meetings
  • 12:00 A tutorial on PyMC (Fengler) — Reading: Abril-Pla et al. (2023) · materials
  • 13:00 Lunch
  • 14:00 Hands-on: simulating data from a cognitive model (Fengler) — materials · DDM explorer
  • 14:30 Hands-on: toy models for parameter estimation (Fengler) — materials
  • 15:00 — MCMC methods (Fengler) — Reading: van Ravenzwaaij et al. (2018) · materials
  • 15:30 — Neural network methods (Sokratous) — Reading: Sokratous et al. (2023) · slides materials
  • 16:00 Short breakout meetings: identifying applications · Q&A

Day 2 Recordings

  • Turner, B. M., & Van Zandt, T. (2012). A tutorial on approximate Bayesian computation. Journal of Mathematical Psychology, 56, 69–85.
  • Bürkner, P. C., Scholz, M., & Radev, S. T. (2023). Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy. Statistics Surveys, 17, 216–310.
  • van Ravenzwaaij, D., Cassey, P., & Brown, S. D. (2018). A simple introduction to Markov Chain Monte-Carlo sampling. Psychonomic Bulletin & Review, 25, 143–154.
  • Abril-Pla, O., et al. (2023). PyMC: a modern, and comprehensive probabilistic programming framework in Python. PeerJ Computer Science, 9, e1516.

Day 3 — Wednesday, July 29: Tools for SBI

  • 09:00 MATLAB Deep Learning Toolbox (Kvam) — Reading: Kvam et al. (2025) · slides
  • 09:30 HSSM (Fengler) — Reading: Xu et al. (2025) · materials
  • 10:00 BayesFlow (Radev) — Reading: Kühmichel et al. (2025) · materials
  • 10:45 Morning coffee / tea
  • 11:00 MCMC sampling for hierarchical Bayesian models (Turner, Fengler) — Reading: Fengler et al. (2021) · companion notebook · slides materials
  • 12:00 Recurrent networks for dynamic data (Pan) — Reading: Pan et al. (2025) · materials
  • 13:00 Lunch
  • 14:00 Variational autoencoders (Sokratous) — Reading: Doersch (2016) · slides
  • 15:00 Participant poster session (to 17:00)

Day 3 Recordings

  • Fengler, A., Govindarajan, L. N., Chen, T., & Frank, M. J. (2021). Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience. eLife, 10, e65074.
  • Xu, P., Omar, A., Paniagua, C., Fengler, A., Frank, M. J., & Bera, K. (2025). lnccbrown/HSSM: 0.2.11. Zenodo.
  • Pan, T. F., Li, J. J., Thompson, B., & Collins, A. G. E. (2025). Latent variable sequence identification for cognitive models with neural network estimators. Behavior Research Methods, 57(10), 272.
  • Kühmichel, L., Huang, J. M., Pratz, V., Arruda, J., Olischläger, H., Habermann, D., … & Radev, S. T. (2026). BayesFlow 2.0: Multi-Backend Amortized Bayesian Inference in Python. arXiv preprint arXiv:2602.07098.
  • Kvam, P. D., Sokratous, K., Fitch, A. K., & Vassileva, J. (2025). Comparing likelihood-based and likelihood-free approaches to fitting and comparing models of intertemporal choice. Behavior Research Methods, 57(9), 1–29.
  • Doersch, C. (2016). Tutorial on variational autoencoders. arXiv:1606.05908.

Day 4 — Thursday, July 30: Complex models and joint modeling

  • 09:00 Introduction to joint modeling (Turner & Nunez) — Reading: Palestro et al. (2018) · slides, first half
  • 09:30 Joint modeling with EEG (Nunez) — Reading: Nunez et al. (2024) · slides
  • 10:00 Joint modeling with fMRI (Turner) — Reading: Turner et al. (2015) · slides, second half
  • 10:30 Morning coffee / tea
  • 10:45 Exploratory modeling with moderational learning (Zhao) — Reading: Zhao et al. (2025) · slides
  • 12:00 Advanced modeling with diffusion methods (Radev) — Reading: Arruda et al. (2025) · materials
  • 13:00 Lunch
  • 14:00 Joint modeling workshop: using a deep neural network to build in covariation (Nunez) — Reading: Ghaderi-Kangavari et al. (2023) · slides materials
  • 14:45 Breakout group meetings
  • 16:00 Panel discussion with organizers + keynote speakers: future of SBI, open problems, career advice

Day 4 Recordings

  • Palestro, J. J., Bahg, G., Sederberg, P. B., Lu, Z.-L., Steyvers, M., & Turner, B. M. (2018). A tutorial on joint models of neural and behavioral measures of cognition. Journal of Mathematical Psychology, 84, 20–48.
  • 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, 6(3), 317–376.
  • Turner, B. M., Van Maanen, L., & Forstmann, B. U. (2015). Informing cognitive abstractions through neuroimaging: the neural drift diffusion model. Psychological Review, 122(2), 312.
  • Nunez, M. D., Fernandez, K., Srinivasan, R., & Vandekerckhove, J. (2024). A tutorial on fitting joint models of M/EEG and behavior to understand cognition. Behavior Research Methods, 56(6), 6020–6050.
  • Kvam, P. D., Romeu, R. J., Turner, B. M., Vassileva, J., & Busemeyer, J. R. (2021). Testing the factor structure underlying behavior using joint cognitive models. Psychological Methods, 26(1), 18–37.
  • Arruda, J., Pandey, V., Sherry, C., Barroso, M., Intes, X., Hasenauer, J., & Radev, S. T. (2025). Compositional amortized inference for large-scale hierarchical Bayesian models. arXiv:2505.14429.

Day 5 — Friday, July 31: Findings and future directions

  • 09:00 Breakout groups put together presentations
  • 10:45 Morning coffee / tea
  • 11:00 Breakout groups report back: short (~10–15 min) presentations on planned projects and progress, with feedback and discussion
  • 13:00 Lunch
  • 14:00 Concluding remarks + research opportunities: funding, collaborations, open datasets and problems; Discord, OSF, and GitHub materials
  • 15:00 Roundtable: incorporating SBI in participants’ own research
  • 16:00 Closing / farewell