The Carnegie Mellon University researchers John Robert Anderson and Christian Lebiere primarily developed the cognitive architecture known as ACT-R ("Adaptive Control of Thought—Rational"). 

The goal of ACT-R, as with any cognitive architecture, is to specify the fundamental and unreduced cognitive and perceptual operations that support the human mind. As a result, ACT-R is a cognitive architecture that maps out the functions of our higher cognitive processes to show how people process information and then act on it. Each task humans can perform should, in theory, consist of several discrete operations. 

Furthermore, the progress of cognitive neuroscience has also inspired many of the ACT-basic R's assumptions. ACT-R is a method of specifying how the brain is so that individual processing modules can produce cognition.

Inspiration

Allen Newell's work has significantly influenced ACT-R, particularly his lifelong advocacy for the idea of unified theories as the only way to understand the foundations of cognition fully. Anderson frequently acknowledges Newell as having had a significant influence on his theory.

The ACT-R theory has a computational implementation as an interpreter of a unique coding language, just like other significant cognitive architectures (such as Soar, CLARION, and EPIC). The interpreter is in Common Lisp, and this approach implies that any researcher can access the theory entirely using the ACT-R interpreter by downloading the ACT-R code from the ACT-R website, loading it into a Common Lisp distribution, and so on. Additionally, this makes it possible for researchers to describe human cognition models as scripts in the ACT-R language. The fundamental building blocks of language and the data types reflect the underlying theories of human cognition. These presumptions are based on several data points from cognitive psychology and brain imaging experiments.

ACT-R framework

Like a programming language, ACT-R is a framework that allows researchers to build "models" (i.e., programs) for various tasks, such as the Tower of Hanoi, memory for text or lists of words, language comprehension, communication, and aircraft control. These models reflect the ACT-R view of cognition's assumptions about the task. We could then execute the model.

ACT-R has produced quantitative forecasts of brain activation patterns in fMRI experiments in recent years. The left prefrontal cortex, anterior cingulate cortex, basal ganglia, and hand and mouth regions of the motor cortex are just a few of the brain regions for which ACT-R predicts the shape and time-course of the BOLD response.

Conclusion

In the early 1990s, Lynne M. Reder, also of Carnegie Mellon University, developed SAC, a model of conceptual and perceptual aspects of memory that shares many features with the ACT-R core declarative system but differs in some assumptions. 

The long development of the ACT-R theory resulted in the birth of several parallel and related projects. For example, researchers argued at the 2015 workshop that software updates necessitated an increase in the model numbering to ACT-R 7.0. Furthermore, the PUPS production system, an early implementation of Anderson's theory that researchers later abandoned, and ACT-RN, a neural network implementation of the approach developed by Christian Lebiere, are two of the most important.