Bayesian Cognitive Model Builder

Purpose

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 posterior predictive checks.

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.

When to Use This Skill

• Building a generative model of a cognitive process (decision-making, learning, memory, perception) where parameters have psychological interpretations
• Estimating individual differences in cognitive parameters while borrowing strength across participants (hierarchical/multilevel models)
• Working with small samples or sparse data per participant where regularization through priors prevents overfitting
• Parameter uncertainty matters for your scientific conclusions (credible intervals, not just point estimates)
• Comparing competing cognitive models via information criteria (LOO-CV, WAIC) or Bayes factors
• Fitting established cognitive models (DDM, signal detection, reinforcement learning, multinomial processing trees) in a Bayesian framework

When NOT to Use This Skill