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==Brief history== ===Early years: 1973β1990=== ACT-R is the ultimate successor of a series of increasingly precise models of human cognition developed by [[John Robert Anderson (psychologist)|John R. Anderson]]. Its roots can be backtraced to the original HAM (Human Associative Memory) model of memory, described by John R. Anderson and [[Gordon Bower]] in 1973.<ref>Anderson, J. R., & Bower, G. H. (1973). ''Human associative memory''. Washington, DC: Winston and Sons.</ref> The HAM model was later expanded into the first version of the ACT theory.<ref>Anderson, J. R. (1976) ''Language, memory, and thought''. Mahwah, NJ: Lawrence Erlbaum Associates. {{ISBN|0-89859-107-4}}.</ref> This was the first time the procedural memory was added to the original declarative memory system, introducing a computational dichotomy that was later proved to hold in human brain.<ref>Cohen, N. J., & Squire, L. R. (1980). Preserved learning and retention of pattern-analyzing skill in amnesia: dissociation of knowing how and knowing that. ''Science'', ''210(4466)'', 207β210</ref> The theory was then further extended into the ACT* model of human cognition.<ref>Anderson, J. R. (1983). ''The architecture of cognition''. Cambridge, Massachusetts: Harvard University Press. {{ISBN|0-8058-2233-X}}.</ref> ===Integration with rational analysis: 1990β1998=== In the late eighties, Anderson devoted himself to exploring and outlining a mathematical approach to cognition that he named [[Rational analysis]].<ref>Anderson, J. R. (1990) ''The adaptive character of thought''. Mahwah, NJ: Lawrence Erlbaum Associates. {{ISBN|0-8058-0419-6}}.</ref> The basic assumption of Rational Analysis is that cognition is optimally adaptive, and precise estimates of cognitive functions mirror statistical properties of the environment.<ref>Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. ''Psychological Science'', ''2'', 396β408.</ref> Later on, he came back to the development of the ACT theory, using the Rational Analysis as a unifying framework for the underlying calculations. 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" <ref>Anderson, J. R. (1993). ''Rules of the mind''. Hillsdale, NJ: Lawrence Erlbaum Associates. {{ISBN|0-8058-1199-0}}.</ref> In 1993, [[John Robert Anderson (psychologist)|Anderson]] met with Christian Lebiere, a researcher in [[connectionism|connectionist models]] mostly famous for developing with [[Scott Fahlman]] the [[Cascade correlation algorithm|Cascade Correlation]] learning algorithm. Their joint work culminated in the release of ACT-R 4.0.<ref>Anderson, J. R., & Lebiere, C. (1998). ''The atomic components of thought''. Hillsdale, NJ: Lawrence Erlbaum Associates. {{ISBN|0-8058-2817-6}}.</ref> 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. ===Brain Imaging and Modular Structure: 1998β2015=== After the release of ACT-R 4.0, [[John Robert Anderson (psychologist)|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. The necessity of accounting for brain localization pushed for a major revision of the theory. ACT-R 5.0 introduced the concept of modules, specialized sets of procedural and declarative representations that could be mapped to known brain systems.<ref>Anderson, J. R., et al. (2004) An integrated theory of the mind. ''Psychological Review'', ''111(4)''. 1036β1060</ref> 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). Buffers were thought to reflect cortical activity, and a subsequent series of studies later confirmed that activations in cortical regions could be successfully related to computational operations over buffers. A new version of the code, completely rewritten, was presented in 2005 as ACT-R 6.0. It also included significant improvements in the ACT-R coding language. This included a new mechanism in ACT-R production specification called dynamic pattern matching.Β Unlike previous versions which required the pattern matched by a production to include specific slots for the information in the buffers, dynamic pattern matching allows the slots to be matched to also be specified by the buffer contents. A description and motivation for the ACT-R 6.0 is given in Anderson (2007).<ref>Anderson, J. R. (2007). ''How can the human mind occur in the physical universe?'' New York, NY: Oxford University Press. {{ISBN|0-19-532425-0}}.</ref> ===ACT-R 7.0: 2015-Present=== At the 2015 workshop, it was argued that software changes required an increment in the model numbering to ACT-R 7.0. A major software change was removal of the requirement that chunks must be specified based on predefined chunk-types.Β The chunk-type mechanism was not removed, but changed from being a required construct of the architecture to being an optional syntactic mechanism in the software.Β This allowed for more flexibility in knowledge representation for modeling tasks that require learning novel information and extended the functionality provided through dynamic pattern matching now allowing models to create new "types" of chunks.Β This also lead to a simplification of the syntax required for specifying the actions in a production because all the actions now have the same syntactic form.Β The ACT-R software has also been subsequently updated to include a remote interface based on JSON RPC 1.0.Β That interface was added to make it easier to build tasks for models and work with ACT-R from languages other than Lisp, and the tutorial included with the software has been updated to provide Python implementations for all of the example tasks performed by the tutorial models. ===Workshop and summer school=== In 1995, [[Carnegie Mellon University]] began hosting their Annual ACT-R Workshop and Summer School.<ref>{{cite web | url=http://act-r.psy.cmu.edu/workshops/ | title=ACT-R Β» Workshops }}</ref> Their ACT-R Workshop is currently hosted at the annual MathPsych/ICCM Conference, and their Summer School is hosted on-campus with a virtual attendance option at [[Carnegie Mellon University]]. ===Spin-offs=== The long development of the ACT-R theory gave birth to a certain number of parallel and related projects. The most important ones are the '''PUPS production system''', an initial implementation of Anderson's theory, later abandoned; and '''ACT-RN''',<ref name="actrn"/> a neural network implementation of the theory developed by Christian Lebiere. [[Lynne M. Reder]], also at [[Carnegie Mellon University]], developed '''[[SAC (Computational model)|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. For his dissertation at [[Carnegie Mellon University]], [[Christopher L. Dancy]] developed, and successfully defended in 2014, '''ACT-R/Phi''',<ref>Dancy, C. L., Ritter, F. E., & Berry, K. (2012). Towards adding a physiological substrate to ACT-R. In ''21st Annual Conference on Behavior Representation in Modeling and Simulation 2012, BRiMS 2012'' (pp. 75-82). (21st Annual Conference on Behavior Representation in Modeling and Simulation 2012, BRiMS 2012).</ref> an implementation of ACT-R with added physiological modules which enable ACT-R to interface with human physiological processes. A lightweight Python-based implementation of the working memory component of ACT-R, '''pyACTUp''',<ref>{{cite web |last1=Morrison |first1=Don |title=pyactup |url=https://github.com/rogerlew/pyactup |website=github.com |access-date=15 September 2023}}</ref> was created by Don Morrison at [[Carnegie Mellon University]], who maintains the ACT-R codebase. This library implements ACT-R as a unimodal [[supervised learning]] model for classification tasks.
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