This skill encodes expert knowledge for building hierarchical Bayesian cognitive models using probabilistic programming languages (Stan, PyMC). It addresses the modeling decisions that require domain expertise beyond knowing Stan/PyMC syntax: how to choose priors that respect cognitive constraints, when to use hierarchical structure, how to diagnose MCMC pathologies, and how to evaluate model adequacy through post...
A competent programmer without cognitive modeling training would get wrong: which prior families are appropriate for cognitive parameters (e.g., RT must be positive, learning rates bounded in [0,1]), when partial pooling outperforms complete pooling or no pooling, how to detect non-identifiability in cognitive models, and what constitutes adequate MCMC convergence for publishable results.
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