Innovative Learning

ACT-R (John Anderson)


ACT-R is a general theory of cognition developed by John Anderson and colleagues at Carnegie Mellon Univeristy that focuses on memory processes . It is an elaboration of the original ACT theory (Anderson, 1976) and builds upon HAM, a model of semantic memory proposed by Anderson & Bower (1973). Anderson (1983) provides a complete description of ACT-R. In addition, Anderson (1990) provides his own critique of ACT-R and Anderson (1993) provides the outline for a broader development of the theory. See the CMU ACT site for the most up-to-date information on the theory.


ACT-R distinguishes among three types of memory structures: declarative, procedural and working memory. Declarative memory takes the form of a semantic net linking propositions, images, and sequences by associations. Procedural memory (also long-term) represents information in the form of productions; each production has a set of conditions and actions based in declarative memory. The nodes of long-term memory all have some degree of activation and working memory is that part of long-term memory that is most highly activated.

According to ACT-R, all knowledge begins as declarative information; procedural knowledge is learned by making inferences from already existing factual knowledge. ACT-R supports three fundamental types of learning: generalization, in which productions become broader in their range of application, discrimination, in which productions become narrow in their range of application, and strengthening, in which some productions are applied more often. New productions are formed by the conjunction or disjunction of existing productions.


ACT-R can explain a wide variety of memory effects as well as account for higher order skills such as geometry proofs, programming and language learning (see Anderson, 1983; 1990). ACT-R has been the basis for intelligent tutors (Anderson, Boyle, Farrell & Reiser, 1987; Ritter et al, 2007).


One of the strengths of ACT is that it includes both proposition and procedural representation of knowledge as well as accounting for the use of goals and plans. For example, here is a production rule that could be used to convert declarative sentences into a question:

IF the goal is to question whether the proposition (LVrelation LVagent LVobject) is true THEN set as subgoals

1. to plan the communication (LVrelation LVagent LVobject)
2. to move the first word in the description of LVrelation to the beginning of the sentence
3. to execute the plan

This production rule could be used to convert the sentence: "The lawyer is buying the car." into the question: "Is the lawyer buying the car?"


  1. Identify the goal structure of the problem space.
  2. Provide instruction in the context of problem-solving.
  3. Provide immediate feedback on errors.
  4. Minimize working memory load.
  5. Adjust the "grain size" of instruction with learning to account for the knowledge compilation process.
  6. Enable the student to approach the target skill by successive approximation.


Anderson, J. (1976). Language, Memory and Thought. Hillsdale, NJ: Erlbaum Associates.
Anderson, J. (1983). The Architecture of Cognition. Cambridge, MA: Harvard University Press.
Anderson, J. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum Associates.
Anderson, J. (1993). Rules of the Mind. Hillsdale, NJ: Erlbaum.
Anderson, J. & Bower, G. (1973). Human Associative Memory. Washington, DC: Winston.
Anderson, J., Boyle, C., Farrell, R. & Reiser, B. (1987). Cognitive principles in the design of computer tutors. In P. Morris (ed.), Modeling Cognition. NY: John Wiley.
Anderson, J. & Lebiere, C. (1998). The Atomic Components of Thought. Mahwah, NJ: Erlbaum Associates.
Ritter, S.,  Anderson, J., Koendinger, K. & Corbett, A. (2007). Cognitive tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14(2), 249-255.

Note: Many of Anderson's articles are available from John Anderson's CMU home page