ACT-R Model Builder
ACT-R Model Builder
Purpose
This skill encodes expert knowledge for constructing computational cognitive models within the ACT-R (Adaptive Control of Thought -- Rational) architecture. It provides guidance on chunk type definition, production rule authoring, subsymbolic parameter selection with empirically validated defaults, model fitting workflows, and validation procedures. A general-purpose programmer would not know the architecture constraints, parameter defaults, or model validation standards without specialized cognitive modeling training.
When to Use This Skill
- Designing a new ACT-R model for a cognitive task (memory retrieval, decision-making, skill acquisition)
- Setting subsymbolic parameters and understanding their theoretical justification
- Structuring chunk types and production rules for a specific experimental paradigm
- Fitting an ACT-R model to behavioral data (RT, accuracy)
- Validating a model via parameter recovery, cross-validation, or qualitative predictions
- Choosing between ACT-R 7.x (Lisp) and pyactr (Python) for implementation
Research Planning Protocol
Before executing the domain-specific steps below, you MUST:
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