A cognitive modeling toolbox implementing the ACT-R 6 theory.

Cognitive models have explained a great deal of behavioral and neurophysiological data.   On the road to understanding the mind, cognitive architectures have specified a core set of representations and mechanisms common to a variety of models. Such architectures separate general functional components and their abilities from domain-specific instantiations, such as knowledge and strategies.

On the road to describing cognition in real-world situations, models have been becoming increasingly complex. As task complexity increases, a careful analysis of the components of the model is necessary. For anything but the simplest cognitive models in ACT-R, many of the procedures and data structures they define are often not evaluated:  the specifics of many of the components of the model may be irrelevant to the story a model has to tell.  The solution to this problem is under-specification.  In what we call the Accountable Modeling paradigm, we suggest applying Occam’s razor and specifying only what is meant to be directly or indirectly evaluated.  As a consequence, we arrive at models that can be more complex yet faster and easier to prototype, while still using the same core representations and mechanisms of the architecture.  

This motivated ACT-UP, a reimplementation of the ACT-R theory (Anderson 2007).  ACT-UP implements a substantial subset of the ACT-R theory. ACT-UP is a toolbox providing ACT-R functionality in a piecemeal fashion as well as commands at a higher abstraction level.  The ACT-UP library is a stand-alone system, and independent of ACT-R 6.  It provides a set of Lisp functions and macros; modelers interact with it on the basis of source code that follows Common Lisp syntax (see this tutorial for a step-by-step introduction).  ACT-UP models predict the two major behavioral outcome types: choice and timing.

Unlike in ACT-R 6, modelers do not write out series of production rules to program the model. Instead, they use a programming language with its loops, conditionals and variable definitions. The evaluation is sequential rather than parallel (unless parallelism is needed).

Implemented ACT-R modules and functionalities

In the following, we give a high-level overview of the cognitive functionalities of ACT-UP in relation to ACT-R 6.

• Declarative Memory: Cue-based memory retrieval of chunks (base-level learning, spreading activation and assocative learnig, partial matching/similarity retrieval, blending, transient and static noise). (Chunks may be extended with non-ACT-R, user-defined structures.)
• Procedural Memory: Procedures defined as LISP functions with Loops, Conditionals, lexically-scoped variables. Procedure selection via utilities (with noise). Reinforcement-learning based utility learning as in ACT-R. Procedure compilation, sidestepping DM requests (and other module interaction), akin production compilation in ACT-R.
• Parallelism: parallel requests to different modules; modules block each other (threaded cognition).
• Choice and timing predictions for DM; Choice and limited timing predictions for procedural memory (timing for procedures is usually estimated from data in ACT-UP).
• Vision and Motor modules not yet included in the public distribution.

Examples and Tutorials

Most of the tutorial models from the ACT-R 6 distribution have been re-implemented in ACT-UP; they come with ACT-UP.

Online Documentation

Please visit the following links to get a head start on ACT-UP. (This is also included with the ACT-UP distribution.)

API documentation for ACT-UP

How do I... document

On debugging models in ACT-UP

Slides from our 2010 MODSIM World presentation (Oct 2010)

Downloads

Right-click the link and choose "Save Link As..." to save the document to your computer.

ACT-UP.ZIP (Beta version, updated regularly) Current Version: 27bc8ed Sun Jul 17 07:10:59 EDT 2011

Requirements

Common Lisp: SBCL, OpenMCL/CCL, LispWorks work. Allegro: limited testing.

Mailing lists

Subscribe to the low-volume ACT-UP mailing list for user support, and for development announcements.

Publications

Primary: Descriptions of ACT-UP

 

David Reitter and Christian Lebiere.
Accountable modeling in ACT-UP, a scalable, rapid-prototyping ACT-R implementation.
In Proceedings of the 10th International Conference on Cognitive Modeling (ICCM), pages 199-204, Philadelphia, PA, 2010.
  abstract   bib   PDF  

Secondary: Models written in ACT-UP

 

David Reitter and Christian Lebiere.
Towards cognitive models of communication and group intelligence.
In Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, pages 734-739, Boston, MA, July 2011.
  .pdf   bib  

 

David Reitter and Christian Lebiere.
A cognitive model of spatial path planning.
Computational and Mathematical Organization Theory, 16(3):220-245, 2010.
  abstract   bib   PDF  

 

David Reitter and Christian Lebiere.
Towards explaining the evolution of domain languages with cognitive simulation.
Cognitive Systems Research 12(2):175-185, 2011.
  abstract   bib   PDF   http  

 

David Reitter, Frank Keller, and Johanna D. Moore.
A computational cognitive model of syntactic priming.
Cognitive Science 35(4):587-637, 2011.
  abstract   bib   PDF  

 

David Reitter and Christian Lebiere.
Did social networks shape language evolution? A multi-agent cognitive simulation.
In Proc. Cognitive Modeling and Computational Linguistics Workshop (CMCL), pages 9-17, Uppsala, Sweden, 2010. Association for Computational Linguistics.
  abstract   bib   PDF  

 

David Reitter, Ion Juvina, Andrea Stocco, and Christian Lebiere.
Resistance is futile: Winning lemonade market share through metacognitive reasoning in a three-agent cooperative game.
In Proceedings of the 19th Behavior Representation in Modeling & Simulation (BRIMS), Charleston, SC, 2010.
  abstract   bib   PDF  

Authors / Contact

Your feedback is important to us. Please click on the link below to send us your comments and suggestions.

David Reitter, Christian Lebiere, Jasmeet Ajmani, Carnegie Mellon University

Thanks: Dan Bothell for code contributions and advice.

 

 

Last Modification: Sat Apr 29 2023  DR