BUGS in Cognitive Science

Many research papers in cognitive science use BUGS/JAGS/STAN to develop models and analyze data. Here is a list that we are sure is incomplete, and hope will be soon be extremely out-of-date. Please contact us if you know about papers that are missing from the list.

Ahn, W.-J., Krawitz, A., Kim, W., Busenmeyer, J. R., & Brown, J. W. (2011). A model-based fMRI analysis with hierarchical Bayesian parameter estimation. Journal of Neuroscience, Psychology, and Economics, 4, 95-110.

Anders, R. & Batchelder, W. H. (2012). Cultural consensus theory for multiple consensus truths. Journal of Mathematical Psychology, 56, 452-469.

Arnold, N. R., Bayen, U. J., Kuhlmann, B. G., & Vaterrodt, B. (2013). Hierarchical modeling of contingency-based source monitoring: A test of the probability-matching account. Psychonomic Bulletin & Review, 20, 326-333.

Averell, L., & Heathcote, A. (2011). The form of the forgetting curve and the fate of memories. Journal of Mathematical Psychology, 55, 23-35.

Batchelder, W. H., & Anders, R. (2012). Cultural Consensus Theory: Comparing different concepts of cultural truth. Journal of Mathematical Psychology, 56, 316-332.

Bäumler, D., Voigt, B., Robert, M., Stalder, T., Kirschbaum, C., & Kliegel, M. (in press). The relation of the cortisol awakening response and prospective memory functioning in young children. Biological Psychology.

Behseta, S., Berdyyeva, T., Olson, C. R., & Kass, R. E. (2009). Bayesian correction for attenuation of correlation in multi-trial spike count data. Journal of Neurophysiology, 101, 2186-2193.

Davidson, D. J., & Martin, A. E. (2013). Modeling accuracy as a function of response time with the generalized linear mixed effects model. Acta Psychologica, 144, 83-96.

DeCarlo, L. T. (2012). On a signal detection approach to m-alternative forced choice with bias, with maximum likelihood and Bayesian approaches to estimation. Journal of Mathematical Psychology, 56, 196-207.

Dennis, S. J., Lee, M. D., & Kinnell, A. (2008). Bayesian analysis of recognition memory: The case of the list-length effect. Journal of Memory and Language, 59, 361-376.

Donkin, C., Averell, L., Brown, S. D., & Heathcote, A. (2009). Getting more from accuracy and response time data: Methods for testing the Linear Ballistic Accumulator model. Behavior Research Methods, 41, 1095-1110.

Dutilh, G., Vandekerckhove, J., Tuerlinckx, F., & Wagenmakers, E.-J. (2009). A diffusion model decomposition of the practice effect. Psychonomic Bulletin & Review, 16, 1026-1036.

Dyjas, O., Grasman, R. P. P. P., Wetzels, R., van der Maas, H. L. J., & Wagenmakers, E.-J.(2012). What’s in a name: A Bayesian hierarchical analysis of the name-letter effect. Frontiers in Quantitative Psychology and Measurement, 3:334.

Hawkins, G. E., Marley, A. A. J., Heathcote, A., Flynn, T. N., Louviere, J. J., & Brown, S. D. (2014). The best of times and the worst of times are interchangeable. Decision, 1, 192-214.

Hemmer, P., Tauber, S., & Steyvers, M. (in press). Moving beyond qualitative evaluations of Bayesian models of cognition. Psychonomic Bulletin & Review.

Horn, S. S., Pachur, T., & Mata, R. (2015). How does aging affect recognition-based inference? A hierarchical Bayesian modeling approach. Acta Psychologica, 154, 77-85.

Jepma, M., Wagenmakers, E.-J., & Nieuwenhuis, S. (2012). Temporal expectation and information processing: A model-based analysis. Cognition, 122, 426-441. Model specification, model code, and model output is available from a zip file (23.3 MB) here: ShiftedWaldMixUniform.zip.

Johnson, T. R., & Kuhn, K. M. (2013). Bayesian Thurstonian models for ranking data using JAGS. Behavior Research Methods, 45, 857-872.

Lee, M. D. (2008). Three case studies in the Bayesian analysis of cognitive models. Psychonomic Bulletin & Review, 15, 1-15.

Lee, M. D. (2015). Evidence for and against a simple interpretation of the less-is-more effect. Judgment and Decision Making, 10, 18-33.

Lee, M. D., & Sarnecka, B. W. (2011). Number-knower levels in young children: Insights from Bayesian modeling. Cognition, 120, 391-402.

Lee, M. D., & Wetzels, R. (2010). Individual differences in attention during category learning. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 387-392. Austin, TX: Cognitive Science Society.

Lee, M. D., Zhang, S., Munro, M. N., & Steyvers, M. (2011). Psychological models of human and optimal performance on bandit problems. Cognitive Systems Research, 12, 164-174.

Lodewyckx, T., Kim, W., Tuerlinckx, F., Kuppens, P., Lee, M. D., & Wagenmakers, E.-J. (2011). A tutorial on Bayes factor estimation with the product space method. Journal of Mathematical Psychology, 55, 331-347.

Matzke, D. & Wagenmakers, E.-J. (2009). Psychological interpretation of ex-Gaussian and shifted Wald parameters: A diffusion model analysis. Psychonomic Bulletin & Review, 16, 798-817. Data, R code, and WinBUGS code is available here: AppendicesExGaussShiftedWald.zip.

Matzke, D., Dolan, C. V., Batchelder, W. H., & Wagenmakers, E.-J. (in press). Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items. Psychometrika.

Matzke, D., Dolan, C. V., Logan, G. D., Brown, S. D., & Wagenmakers, E.-J. (in press). Bayesian parametric estimation of stop-signal reaction time distributions. Journal of Experimental Psychology: General.

Morey, R. D., Rouder, J. N., Pratte, M. S., & Speckman, P. L. (2011). Using MCMC chain outputs to efficiently estimate Bayes factors. Journal of Mathematical Psychology, 55, 368-378.

Newell, B. R., Koehler, D. J., James, G., Rakow, T., & van Ravenzwaaij, D. (2013). Probability matching in risky choice: The interplay of feedback and strategy availability. Memory & Cognition, 41, 329-338.

Nilsson, H., Rieskamp, J., & Wagenmakers, E.-J. (2011). Hierarchical Bayesian parameter estimation for cumulative prospect theory. Journal of Mathematical Psychology, 55, 84-93. Code used to fit cumulative prospect theory is available here: CPTcode.zip.

Okada, K., & Shigemasu, K. (2010). Bayesian multidimensional scaling for the estimation of a Minkowski exponent. Behavior Research Methods, 42, 899-905.

Ortega, A., Wagenmakers, E.-J., Lee, M. D., Markowitsch, H. J., & Piefke, M. (2012). A Bayesian latent group analysis for detecting poor effort in the assessment of malingering. Archives of Clinical Neuropsychology, 27, 453-465.

Ortega, A., Piefke, M., & Markowitsch, H. J. (2014). A Bayesian latent group analysis for detecting poor effort in a sample of cognitively impaired patients. Journal of Clinical and Experimental Neuropsychology, 36, 659-667.

Pooley, J. P., Lee, M. D., & Shankle, W. R. (2011). Understanding Alzheimer’s using memory models and hierarchical Bayesian analysis. Journal of Mathematical Psychology, 55, 47-56.

Scheibehenne, B., Rieskamp, J., & Wagenmakers, E.-J. (2013). Testing adaptive toolbox models: A Bayesian hierarchical approach. Psychological Review, 120, 39-64.

Scheibehenne, B., & Studer, B. (2014). A hierarchical Bayesian model of the influence of run length on sequential predictions. Psychonomic Bulletin & Review, 21, 211-217.

Shiffrin, R. M., Lee, M. D., Kim, W., & Wagenmakers, E.-J. (2008). A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cognitive Science, 32, 1248-1284.

Steingroever, H., Wetzels, R., & Wagenmakers, E.-J. (2013). Validating the PVL-Delta model for the Iowa gambling task. Frontiers in Decision Neuroscience, 4:898.

Steingroever, H., Wetzels, R., & Wagenmakers, E.-J. (in press). Absolute performance of reinforcement-learning models for the Iowa Gambling Task. Decision.

Steyvers, M., Wallsten, T. S., Merkle, E. C., & Turner, B. M. (2014). Evaluating probabilistic forecasts with Bayesian signal detection models. Risk Analysis, 34, 435-452.

Stringer, S., Borsboom, D., & Wagenmakers, E.-J. (2011). Bayesian inference for the information gain model. Behavior Research Methods, 43, 297-309.

Vandekerckhove, J. and Tuerlinckx, F., & Lee, M. D. (2011). Hierarchical diffusion models for two-choice response times. Psychological Methods, 16, 44-62.

van der Maas, H. L. J., Molenaar, D., Maris, G., Kievit, R. A., & Borsboom, D. (2011). Cognitive psychology meets psychometric theory: On the relation between process models for decision making and latent variable models for individual differences. Psychological Review, 118, 339-356.

van Driel, J., Knapen, T., van Es, D. M., & Cohen, M. X. (2014). Interregional alpha-band synchrony supports temporal cross-modal integration. NeuroImage, 101, 404-415.

Vanpaemel, W. (2009). BayesGCM: Software for Bayesian inference with the Generalized Context Model. Behavior Research Methods, 41, 1111-1120.

van Ravenzwaaij, D., Dutilh, G., & Wagenmakers, E.-J. (2011). Cognitive model decomposition of the BART: Assessment and application. Journal of Mathematical Psychology, 55, 94-105.

Wabersich, D., & Vandekerckhove, J. (in press). Extending JAGS: A tutorial on adding custom distributions to JAGS (with a diffusion model example). Behavior Research Methods.

Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R. (2010). Bayesian hypothesis testing for psychologists: A tutorial on the Savage-Dickey method. Cognitive Psychology, 60, 158-189.

Wetzels, R., Lee, M. D., & Wagenmakers, E.-J. (2010). Bayesian inference using WBDev: A tutorial for social scientists. Behavior Research Methods, 42, 884-897.

Wetzels, R., Raaijmakers, J. G. W., Jakab, E., & Wagenmakers, E.-J. (2009). How to quantify support for and against the null hypothesis: A flexible WinBUGS implementation of a default Bayesian t test. Psychonomic Bulletin & Review, 16, 752-760.

Wetzels, R., Vandekerckhove, J., Tuerlinckx, F., & Wagenmakers, E.-J. (2010). Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task. Journal of Mathematical Psychology, 54, 14-27.

Zeigenfuse, M. D., & Lee, M. D. (2010). A general latent-assignment approach for modeling psychological contaminants. Journal of Mathematical Psychology, 54, 352-362.

Zhang, S., & Lee, M.D., (2010). Cognitive models and the wisdom of crowds: A case study using the bandit problem. In R. Catrambone, & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 1118-1123. Austin, TX: Cognitive Science Society.

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