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<p><i>EMC2</i> is an <i>R</i> package that provides a comprehensive five-phase workflow for Bayesian hierarchical analysis of cognitive models of choice. In the design phase, <i>EMC2</i> bridges the gap between standard regression analyses and cognitive modeling through linear-model specifications for cognitive-model parameters. In the Bayesian specification and sampling phases, the package provides flexible priors, hierarchical structures, and efficient sampling algorithms, enabling fast, user-friendly estimation of computationally intensive cognitive models. In the final two phases, <i>EMC2</i> provides a suite of functions for model criticism and inference. Using two leading evidence-accumulation models for illustration, we provide a tutorial on the <i>EMC2</i>-based workflow that eases and guides the process of specifying, evaluating, refining, comparing, and interpreting Bayesian hierarchical cognitive models.</p>

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Bayesian hierarchical cognitive modeling with the EMC2 package

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Niek Stevenson,
  • Michelle C. Donzallaz,
  • Reilly J. Innes,
  • Birte U. Forstmann,
  • Dora Matzke,
  • Andrew Heathcote

摘要

EMC2 is an R package that provides a comprehensive five-phase workflow for Bayesian hierarchical analysis of cognitive models of choice. In the design phase, EMC2 bridges the gap between standard regression analyses and cognitive modeling through linear-model specifications for cognitive-model parameters. In the Bayesian specification and sampling phases, the package provides flexible priors, hierarchical structures, and efficient sampling algorithms, enabling fast, user-friendly estimation of computationally intensive cognitive models. In the final two phases, EMC2 provides a suite of functions for model criticism and inference. Using two leading evidence-accumulation models for illustration, we provide a tutorial on the EMC2-based workflow that eases and guides the process of specifying, evaluating, refining, comparing, and interpreting Bayesian hierarchical cognitive models.