bayesian cognitive model builder

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.

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.