ACT-R (pronounced /ˌækt ˈɑr/; short for "Adaptive Control of Thought—Rational") is a cognitive architecture mainly developed by John Robert Anderson and Christian Lebiere at Carnegie Mellon University.
ACT-R has been inspired by the work of Allen Newell, and especially by his lifelong championing the idea of unified theories as the only way to truly uncover the underpinnings of cognition.
Like other influential cognitive architectures (including Soar, CLARION, and EPIC), the ACT-R theory has a computational implementation as an interpreter of a special coding language.
In recent years, ACT-R has also been extended to make quantitative predictions of patterns of activation in the brain, as detected in experiments with fMRI.
ACT-R's most important assumption is that human knowledge can be divided into two irreducible kinds of representations: declarative and procedural.
Within the ACT-R code, declarative knowledge is represented in the form of chunks, i.e. vector representations of individual properties, each of them accessible from a labelled slot.
Chunks are held and made accessible through buffers, which are the front-end of what are modules, i.e. specialized and largely independent brain structures.
[3] Its entities (chunks and productions) are discrete and its operations are syntactical, that is, not referring to the semantic content of the representations but only to their properties that deem them appropriate to participate in the computation(s).
None of these properties counter the fundamental nature of these entities as symbolic, regardless of their role in unit selection and, ultimately, in computation.
For instance, the actual implementation makes use of additional 'modules' that exist only for purely computational reasons, and are not supposed to reflect anything in the brain (e.g., one computational module contains the pseudo-random number generator used to produce noisy parameters, while another holds naming routines for generating data structures accessible through variable names).
Finally, while Anderson's laboratory at CMU maintains and releases the official ACT-R code, other alternative implementations of the theory have been made available.
[24] With the integration of perceptual-motor capabilities, ACT-R has become increasingly popular as a modeling tool in human factors and human-computer interaction.
In this domain, it has been adopted to model driving behavior under different conditions,[25][26] menu selection and visual search on computer application,[27][28] and web navigation.
ACT-R is the ultimate successor of a series of increasingly precise models of human cognition developed by John R. Anderson.
[39] In the late eighties, Anderson devoted himself to exploring and outlining a mathematical approach to cognition that he named Rational analysis.
To highlight the importance of the new approach in the shaping of the architecture, its name was modified to ACT-R, with the "R" standing for "Rational" [42] In 1993, Anderson met with Christian Lebiere, a researcher in connectionist models mostly famous for developing with Scott Fahlman the Cascade Correlation learning algorithm.
[43] Thanks to Mike Byrne (now at Rice University), version 4.0 also included optional perceptual and motor capabilities, mostly inspired from the EPIC architecture, which greatly expanded the possible applications of the theory.
After the release of ACT-R 4.0, John Anderson became more and more interested in the underlying neural plausibility of his life-time theory, and began to use brain imaging techniques pursuing his own goal of understanding the computational underpinnings of the human mind.
ACT-R 5.0 introduced the concept of modules, specialized sets of procedural and declarative representations that could be mapped to known brain systems.
[44] In addition, the interaction between procedural and declarative knowledge was mediated by newly introduced buffers, specialized structures for holding temporarily active information (see the section above).
Lynne M. Reder, also at Carnegie Mellon University, developed SAC in the early 1990s, a model of conceptual and perceptual aspects of memory that shares many features with the ACT-R core declarative system, although differing in some assumptions.