We have investigated Bayesian approaches to cognitive modeling, used Bayesian techniques for parameter estimation and model comparison, and used Bayesian hierarchical modeling to understand individual differences.

Annis, J., Evans, N.J., Miller, B.J., & Palmeri, T.J. (2019). Thermodynamic integration and steppingstone sampling methods for estimating Bayes factors: A tutorial for psychologists. Journal of Mathematical Psychology.

Annis, J., & Palmeri, T. J. (2019). Modeling memory dynamics in visual expertise. Journal of Experimental Psychology: Learning, Memory, and Cognition.

Boehm, U., Annis, J., Frank, M.J., Hawkins, G.E., Heathcote, A., Kellen, D., Krypotos, A.-M., Lerche, V., Logan, G.D., Palmeri, T.J., Servant, M., Singmann, H., van Ravenzwaaij, D., Wiecki, T.V., Starns, J.J., Voss, A., Matzke, D., Wagenmakers, E.-J. (2019). Estimating between-trial variability parameters of the drift diffusion model: Expert advice and recommendations. Journal of Mathematical Psychology.

Annis, J., & Palmeri, T.J. (2018). Bayesian statistical approaches to evaluating cognitive models. Wiley Interdisciplinary Reviews in Cognitive Science.

Annis, J., Miller, B.J., & Palmeri, T.J. (2018). Bayesian inference with Stan: A tutorial on adding custom distributions. Behavioral Research Methods.

Dutilh, G., Annis, J., Brown, S.D., Cassey, P., Evans, N.J., Grasman, R.P.P.P., Hawkins, G.E., Heathcote, A., Holmes, W.R., Krypotos, A.-M., Kupitz, C.-N., Leite, F.P. Lerche, V., Lin, Y.S., Logan, G.D., Palmeri, T.J., Starns, J.J., Trueblood, J.S., van Maanen, L., van Ravenzwaaij, D., Vandekerckhove, J., Visser, I., Voss, A., White, C.N., Wiecki, T.V., Rieskamp, J., & Donkin, C. (2018). The quality of response time data inference: A blinded, collaborative approach to the validity of cognitive models. Psychonomic Bulletin & Review.

Shen, J., & Palmeri, T.J. (2016). Modeling individual differences in visual categorization. Visual Cognition, 24, 260-283.