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:

1. State the research question -- What specific question is this analysis/paradigm addressing?
2. Justify the method choice -- Why is this approach appropriate? What alternatives were considered?
3. Declare expected outcomes -- What results would support vs. refute the hypothesis?
4. Note assumptions and limitations -- What does this method assume? Where could it mislead?
5. Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

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.

ACT-R Architecture Overview

ACT-R is a hybrid cognitive architecture with symbolic and subsymbolic components (Anderson, 2007; Anderson & Lebiere, 1998).

Core Modules and Buffers

Processing Cycle

1. Buffers hold one chunk each (the "bottleneck" assumption; Anderson, 2007, Ch. 1)
2. Productions match against buffer contents (pattern matching)
3. Conflict resolution selects one production per cycle (~50 ms per production firing; Anderson, 2007)
4. Selected production modifies buffers or makes requests to modules
5. Modules process requests asynchronously

Building the Symbolic Model

Step 1: Define Chunk Types

Chunk types define the structure of declarative knowledge:

;; ACT-R 7.x Lisp syntax
(chunk-type addition-problem arg1 arg2 answer)
(chunk-type counting-fact number next)

Decision rules for chunk type design:

1. Each chunk type represents one category of knowledge (Anderson, 2007, Ch. 2)
2. Slots should correspond to meaningful features of the domain
3. Use inheritance when chunk types share structure (e.g., a "problem" parent type)
4. Keep chunks small -- typically 3-6 slots per chunk type (Anderson & Lebiere, 1998)

Step 2: Write Production Rules

Productions follow an IF-THEN structure:

(p retrieve-answer
 =goal>
 isa addition-problem
 arg1 =num1
 arg2 =num2
 answer nil
 ?retrieval>
 state free
==>
 +retrieval>
 isa addition-fact
 addend1 =num1
 addend2 =num2
 =goal>
)

Production rule guidelines:

Step 3: Structure the Goal Stack

Is the task sequential with clear phases?
 |
 +-- YES --> Use a single goal chunk with a "step" slot
 | that tracks the current phase
 |
 +-- NO --> Does the task require subgoaling?
 |
 +-- YES --> Use goal push/pop (stack)
 |
 +-- NO --> Use the imaginal buffer for
 intermediate representations

Subsymbolic Parameters

These parameters govern memory activation, retrieval, and production selection. See references/parameter-table.yaml for the complete table.

Core Declarative Memory Parameters

Production Utility Parameters

Timing Parameters

Activation Equation

Total activation of chunk i:

A_i = B_i + sum_j(W_j * S_ji) + PM_i + noise

Where:

• B_i = base-level activation (log of weighted recency; decay d; Anderson & Schooler, 1991)
• W_j * S_ji = spreading activation from source j (Anderson, 2007, Ch. 5)
• PM_i = partial matching component (Anderson, 2007)
• noise = logistic noise with scale s (Anderson, 2007)

Retrieval time: RT = F * e^(-f * A_i) (Anderson, 2007)

Model Fitting Workflow

Step 1: Identify Free Parameters

How many free parameters?
 |
 +-- <= 3 --> Standard practice; proceed
 |
 +-- 4-6 --> Acceptable if justified by model complexity
 |
 +-- > 6 --> Warning: overfitting risk. Consider fixing some
 to default values (Anderson, 2007)

Rule of thumb: The number of free parameters should be substantially less than the number of independent data points being fit (Roberts & Pashler, 2000).

Step 2: Choose Fitting Method

Step 3: Fit to Multiple Dependent Variables

ACT-R models should simultaneously account for:

• Response times (correct trials, mean or quantiles)
• Accuracy (proportion correct by condition)
• Qualitative patterns (error types, learning curves)

Use weighted sum of squared deviations or log-likelihood across measures (Anderson, 2007, Ch. 4).

Step 4: Parameter Recovery

Before trusting fitted parameter values, conduct a parameter recovery study. See the parameter-recovery-checker skill.

Common Model Patterns

See references/model-patterns.md for detailed implementations of:

1. Memory retrieval -- Paired associates, fan effect (Anderson, 2007, Ch. 5)
2. Skill acquisition -- Production compilation, power law of practice (Taatgen & Anderson, 2002)
3. Decision-making -- Instance-based learning, utility-based selection (Gonzalez et al., 2003)
4. Problem solving -- Means-ends analysis, goal stacking (Anderson, 2007, Ch. 8)