Goals and Scope

The goal of this package is to provide basic functions and types for building models based on the ACT-R cognitive architecture. There are several approaches for developing ACT-R models. The classic approach involves simulating the internal cognitive processes as a discrete event simulation. A proof-of-concept implementation in Julia can be found in the package ACTRSimulators.jl. Another approach is to derive a likelihood function to enable maximum likelihood or Bayesian parameter estimation. This approach is limited to mathematically tractible ACT-R models, typically of low to moderate complexity. The repo ACTRTutorials.jl provides a collection of tutorials for developing ACT-R models within the likelihood approach.

Installation

In the REPL, type ] to enter the package model and enter the following:

add ACTRModels

Help

You can find information about the package in one of two ways. First, you can navigate through the documation using the panel on the left. Second, you can switch to the help model in the REPL by typing ? followed by the name of a function or type. For example,

help?> retrieve
search: retrieve retrieval_request retrieval_prob retrieval_probs DiscreteUnivariateDistribution DiscreteMultivariateDistribution

  retrieve(actr::AbstractACTR; request...)

  Retrieves a chunk given a retrieval request. By default, current time is computed with get_time.

  Arguments
  ≡≡≡≡≡≡≡≡≡≡≡

    •  actr: an ACT-R object

  Keywords
  ≡≡≡≡≡≡≡≡≡≡

    •  request...: optional keyword arguments representing a retrieval request, e.g. person=:bob

Quick Example

The example below shows how to create a simple ACT-R model and retrieve a memory using (animal,rat) as a retrieval request.

using ACTRModels
using Random

Random.seed!(28194)
# create chunks of declarative knowledge
chunks = [Chunk(;name=:Sigma, animal=:dog),
    Chunk(;name=:Bonkers, animal=:rat)]

# initialize declarative memory
declarative = Declarative(memory=chunks)

# specify model parameters: partial matching, noise, mismatch penalty, activation noise
Θ = (mmp=true, noise=true, δ=1.0, s=0.20, blc=1.5)

# create an ACT-R object with activation noise and partial matching
actr = ACTR(;declarative, Θ...)

# retrieve a memory chunk
retrieve(actr; animal=:rat)

Chunks
┌──────────────────────────────────┬───────┬───────┬──────────────┬────────┬────
│ slots                            │ N     │ L     │ time_created │ recent │ a ⋯
├──────────────────────────────────┼───────┼───────┼──────────────┼────────┼────
│ (name = :Bonkers, animal = :rat) │  1.00 │  1.00 │  0.00        │ [0.0]  │   ⋯
└──────────────────────────────────┴───────┴───────┴──────────────┴────────┴────
                                                               7 columns omitted

References

Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological review, 111(4), 1036.