ACT-R
Encyclopedia
ACT-R is a cognitive architecture
mainly developed by John Robert Anderson at Carnegie Mellon University
. Like any cognitive architecture, ACT-R aims to define the basic and irreducible cognitive and perceptual operations that enable the human mind.
In theory, each task that humans can perform should consist of a series of these discrete operations.
Most of the ACT-R basic assumptions are also inspired by the progress of cognitive neuroscience, and ACT-R can be seen and described as a way of specifying how the brain itself is organized in a way that enables individual processing modules to produce cognition.
, and especially by his life-long championing the idea of unified theories as the only way to truly uncover the underpinnings of cognition.
In fact, John Anderson usually credits Allen Newell
as the major source of influence over his own theory.
, CLARION
, and EPIC
), the ACT-R theory has a computational implementation as an interpreter of a special coding language. The interpreter itself is written in Lisp
, and might be loaded into any of the most common distributions of the Lisp language.
This means that any researcher may download the ACT-R code from the ACT-R website, load it into a Lisp distribution, and gain full access to the theory in the form of the ACT-R interpreter.
Also, this enables researchers to specify models of human cognition in the form of a script in the ACT-R language. The language primitives and data-types are designed to reflect the theoretical assumptions about human cognition.
These assumptions are based on numerous facts derived from experiments in cognitive psychology
and brain imaging.
Like a programming language
, ACT-R is a framework: for different tasks (e.g., Tower of Hanoi, memory for text or for list of words, language comprehension, communication, aircraft controlling), researchers create "models" (i.e., programs) in ACT-R.
These models reflect the modelers' assumptions about the task within the ACT-R view of cognition.
The model might then be run.
Running a model automatically produces a step-by-step simulation of human behavior which specifies each individual cognitive operation (i.e., memory encoding and retrieval, visual and auditory encoding, motor programming and execution, mental imagery manipulation).
Each step is associated with quantitative predictions of latencies and accuracies.
The model can be tested by comparing its results with the data collected in behavioral experiments.
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.
In particular, ACT-R has been augmented to predict the shape and time-course of the BOLD response of several brain areas, including the hand and mouth areas in the motor cortex
, the left prefrontal cortex
, the anterior cingulate cortex
, and the basal ganglia
.
and procedural
.
Within the ACT-R code, declarative knowledge is represented in 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.
There are two types of modules:
All the modules can only be accessed through their buffers.
The contents of the buffers at a given moment in time represents the state of ACT-R at that moment.
The only exception to this rule is the procedural module, which stores and applies procedural knowledge.
It does not have an accessible buffer and is actually used to access other module's contents.
Procedural knowledge is represented in form of productions. The term "production" reflects the actual implementation of ACT-R as a production system
, but, in fact, a production is mainly a formal notation to specify the information flow from cortical areas (i.e. the buffers) to the basal ganglia, and back to the cortex.
At each moment, an internal pattern matcher searches for a production that matches the current state of the buffers. Only one such production can be executed at a given moment. That production, when executed, can modify the buffers and thus change the state of the system. Thus, in ACT-R, cognition
unfolds as a succession of production firings.
s, different theories are usually ascribed to either the "symbolic
" or the "connectionist
" approach to cognition. ACT-R clearly belongs to the "symbolic" field and is classified as such in standard textbooks and collections. 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). This is seen clearly in the chunk slots and in the properties of buffer matching in productions, both of which function as standard symbolic variables.
Members of the ACT-R community, including its developers, prefer to think of ACT-R as a general framework that specifies how the brain is organized, and how its organization gives birth to what is perceived (and, in cognitive psychology, investigated) as mind, going beyond the traditional symbolic/connectionist debate. None of this, naturally, argues against the classification of ACT-R as symbolic system, because all symbolic approaches to cognition aim to describe the mind, as a product of brain function, using a certain class of entities and systems to achieve that goal.
A common misunderstanding suggests that ACT-R may not be a symbolic system because it attempts to characterize brain function. This is incorrect on two counts: First, because all approaches to computational modeling of cognition, symbolic or otherwise, must in some respect characterize brain function, because the mind is brain function. And second, because all such approaches, including connectionist approaches, attempt to characterize the mind at a cognitive level of description and not at the neural level, because it is only at the cognitive level that important generalizations can be retained.
Further misunderstandings arise because of the associative character of certain ACT-R properties, such as chunks spreading activation to each other, or chunks and productions carrying quantitative properties relevant to their selection. None of these properties counter the fundamental nature of these entities as symbolic, regardless of their role in unit selection and, ultimately, in computation.
In fact, much of the implementation does not reflect the theory.
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).
Also, the actual implementation is designed to enable researchers to modify the theory, e.g. by altering the standard parameters, or creating new modules, or partially modifying the behavior of the existing ones.
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.
These alternative implementations include jACT-R (written in Java
by Anthony M. Harrison at the University of Pittsburgh
) and Python ACT-R (written in Python
by Terrence C. Stewart and Robert L. West at Carleton University
, Canada).
Similarly, ACT-RN (now discontinued) was a full-fledged neural implementation of the 1993 version of the theory.
All of these versions were fully functional, and models have been written and run with all of them.
Because of these implementational degrees of freedom, the ACT-R community usually refers to the "official", lisp-based, version of the theory, when
adopted in its original form and left unmodified, as "Vanilla ACT-R".
since its inception. In the course of years, it has been adopted to successfully model a large number of known effects. They include the fan effect of interference for associated information, primacy and recency effects for list memory, and serial recall.
ACT-R has been used to model attentive and control processes in a number of cognitive paradigms. These include the Stroop task, task switching
, the psychological refractory period
, and multi-tasking.
understanding and production. They include models of syntactic parsing, language understanding, language acquisition and metaphor comprehension.
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, menu selection and visual search on computer application, and web navigation.
Such "Cognitive Tutors" are being used as a platform for research on learning and cognitive modeling as part of the Pittsburgh Science of Learning Center. Some of the most successful applications, like the Cognitive Tutor for Mathematics, are used in thousands of schools across the United States.
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. The HAM model was later expanded into the first version of the ACT theory. 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. The theory was then further extended into the ACT* model of human cognition.
In 1993, Anderson met with Christian Lebiere, a researcher in connectionist models
mostly famous for developing with Scott Fahlman
the Cascade Correlation
learning algorithm. Their joint work culminated in the release of ACT-R 4.0. 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.
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. 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.
The most important ones are the PUPS production system, an initial implementation of Anderson's theory, later abandoned; and ACT-RN, a neural network implementation of the theory developed by Christian Lebiere.
Lynne Reder, also at Carnegie Mellon University
, developed in the early nineties SAC, 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.
Cognitive architecture
A cognitive architecture is a blueprint for intelligent agents. It proposes computational processes that act like certain cognitive systems, most often, like a person, or acts intelligent under some definition. Cognitive architectures form a subset of general agent architectures...
mainly developed by John Robert Anderson at Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University is a private research university in Pittsburgh, Pennsylvania, United States....
. Like any cognitive architecture, ACT-R aims to define the basic and irreducible cognitive and perceptual operations that enable the human mind.
In theory, each task that humans can perform should consist of a series of these discrete operations.
Most of the ACT-R basic assumptions are also inspired by the progress of cognitive neuroscience, and ACT-R can be seen and described as a way of specifying how the brain itself is organized in a way that enables individual processing modules to produce cognition.
Inspiration
ACT-R has been inspired by the work of Allen NewellAllen Newell
Allen Newell was a researcher in computer science and cognitive psychology at the RAND corporation and at Carnegie Mellon University’s School of Computer Science, Tepper School of Business, and Department of Psychology...
, and especially by his life-long championing the idea of unified theories as the only way to truly uncover the underpinnings of cognition.
In fact, John Anderson usually credits Allen Newell
Allen Newell
Allen Newell was a researcher in computer science and cognitive psychology at the RAND corporation and at Carnegie Mellon University’s School of Computer Science, Tepper School of Business, and Department of Psychology...
as the major source of influence over his own theory.
What ACT-R looks like
Like other influential cognitive architectures (including SoarSoar (cognitive architecture)
Soar is a symbolic cognitive architecture, created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University, now maintained by John Laird's research group at the University of Michigan. It is both a view of what cognition is and an implementation of that view through a...
, CLARION
CLARION (cognitive architecture)
Connectionist Learning with Adaptive Rule Induction ON-line is a cognitive architecture that incorporates the distinction between implicit and explicit processes and focuses on capturing the interaction between these two types of processes. By focusing on this distinction, CLARION has been used to...
, and EPIC
EPIC (cognitive architecture)
EPIC is a cognitive architecture developed by Professors David E. Kieras and David E. Meyer at the University of Michigan ....
), the ACT-R theory has a computational implementation as an interpreter of a special coding language. The interpreter itself is written in Lisp
Lisp programming language
Lisp is a family of computer programming languages with a long history and a distinctive, fully parenthesized syntax. Originally specified in 1958, Lisp is the second-oldest high-level programming language in widespread use today; only Fortran is older...
, and might be loaded into any of the most common distributions of the Lisp language.
This means that any researcher may download the ACT-R code from the ACT-R website, load it into a Lisp distribution, and gain full access to the theory in the form of the ACT-R interpreter.
Also, this enables researchers to specify models of human cognition in the form of a script in the ACT-R language. The language primitives and data-types are designed to reflect the theoretical assumptions about human cognition.
These assumptions are based on numerous facts derived from experiments in cognitive psychology
Cognitive psychology
Cognitive psychology is a subdiscipline of psychology exploring internal mental processes.It is the study of how people perceive, remember, think, speak, and solve problems.Cognitive psychology differs from previous psychological approaches in two key ways....
and brain imaging.
Like a programming language
Programming language
A programming language is an artificial language designed to communicate instructions to a machine, particularly a computer. Programming languages can be used to create programs that control the behavior of a machine and/or to express algorithms precisely....
, ACT-R is a framework: for different tasks (e.g., Tower of Hanoi, memory for text or for list of words, language comprehension, communication, aircraft controlling), researchers create "models" (i.e., programs) in ACT-R.
These models reflect the modelers' assumptions about the task within the ACT-R view of cognition.
The model might then be run.
Running a model automatically produces a step-by-step simulation of human behavior which specifies each individual cognitive operation (i.e., memory encoding and retrieval, visual and auditory encoding, motor programming and execution, mental imagery manipulation).
Each step is associated with quantitative predictions of latencies and accuracies.
The model can be tested by comparing its results with the data collected in behavioral experiments.
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.
In particular, ACT-R has been augmented to predict the shape and time-course of the BOLD response of several brain areas, including the hand and mouth areas in the motor cortex
Motor cortex
Motor cortex is a term that describes regions of the cerebral cortex involved in the planning, control, and execution of voluntary motor functions.-Anatomy of the motor cortex :The motor cortex can be divided into four main parts:...
, the left prefrontal cortex
Prefrontal cortex
The prefrontal cortex is the anterior part of the frontal lobes of the brain, lying in front of the motor and premotor areas.This brain region has been implicated in planning complex cognitive behaviors, personality expression, decision making and moderating correct social behavior...
, the anterior cingulate cortex
Cingulate cortex
The cingulate cortex is a part of the brain situated in the medial aspect of the cortex. It includes the cortex of the cingulate gyrus, which lies immediately above the corpus callosum, and the continuation of this in the cingulate sulcus...
, and the basal ganglia
Basal ganglia
The basal ganglia are a group of nuclei of varied origin in the brains of vertebrates that act as a cohesive functional unit. They are situated at the base of the forebrain and are strongly connected with the cerebral cortex, thalamus and other brain areas...
.
Brief outline
ACT-R's most important assumption is that human knowledge can be divided into two irreducible kinds of representations: declarativeDeclarative memory
Declarative memory is one of two types of long term human memory. It refers to memories which can be consciously recalled such as facts and knowledge. Its counterpart is known as non-declarative or Procedural memory, which refers to unconscious memories such as skills...
and procedural
Procedural memory
Procedural memory is memory for how to do things. Procedural memory guides the processes we perform and most frequently resides below the level of conscious awareness. When needed, procedural memories are automatically retrieved and utilized for the execution of the integrated procedures involved...
.
Within the ACT-R code, declarative knowledge is represented in 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.
There are two types of modules:
- Perceptual-motor modules, which take care of the interface with the real world (i.e., with a simulation of the real world). The most well-developed perceptual-motor modules in ACT-R are the visual and the manual modules.
- Memory modules. There are two kinds of memory modules in ACT-R:
- Declarative memory, consisting of facts such as Washington, D.C. is the capital of United States, France is a country in Europe, or 2+3=5
- Procedural memory, made of productions. Productions represent knowledge about how we do things: for instance, knowledge about how to type the letter "Q" on a keyboard, about how to drive, or about how to perform addition.
All the modules can only be accessed through their buffers.
The contents of the buffers at a given moment in time represents the state of ACT-R at that moment.
The only exception to this rule is the procedural module, which stores and applies procedural knowledge.
It does not have an accessible buffer and is actually used to access other module's contents.
Procedural knowledge is represented in form of productions. The term "production" reflects the actual implementation of ACT-R as a production system
Production system
A production system is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior. These rules, termed productions, are a basic representation found useful in automated planning, expert systems and action selection...
, but, in fact, a production is mainly a formal notation to specify the information flow from cortical areas (i.e. the buffers) to the basal ganglia, and back to the cortex.
At each moment, an internal pattern matcher searches for a production that matches the current state of the buffers. Only one such production can be executed at a given moment. That production, when executed, can modify the buffers and thus change the state of the system. Thus, in ACT-R, cognition
unfolds as a succession of production firings.
The symbolic vs. connectionist debate
In the cognitive scienceCognitive science
Cognitive science is the interdisciplinary scientific study of mind and its processes. It examines what cognition is, what it does and how it works. It includes research on how information is processed , represented, and transformed in behaviour, nervous system or machine...
s, different theories are usually ascribed to either the "symbolic
Cognitivism (psychology)
In psychology, cognitivism is a theoretical framework for understanding the mind that came into usage in the 1950s. The movement was a response to behaviorism, which cognitivists said neglected to explain cognition...
" or the "connectionist
Connectionism
Connectionism is a set of approaches in the fields of artificial intelligence, cognitive psychology, cognitive science, neuroscience and philosophy of mind, that models mental or behavioral phenomena as the emergent processes of interconnected networks of simple units...
" approach to cognition. ACT-R clearly belongs to the "symbolic" field and is classified as such in standard textbooks and collections. 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). This is seen clearly in the chunk slots and in the properties of buffer matching in productions, both of which function as standard symbolic variables.
Members of the ACT-R community, including its developers, prefer to think of ACT-R as a general framework that specifies how the brain is organized, and how its organization gives birth to what is perceived (and, in cognitive psychology, investigated) as mind, going beyond the traditional symbolic/connectionist debate. None of this, naturally, argues against the classification of ACT-R as symbolic system, because all symbolic approaches to cognition aim to describe the mind, as a product of brain function, using a certain class of entities and systems to achieve that goal.
A common misunderstanding suggests that ACT-R may not be a symbolic system because it attempts to characterize brain function. This is incorrect on two counts: First, because all approaches to computational modeling of cognition, symbolic or otherwise, must in some respect characterize brain function, because the mind is brain function. And second, because all such approaches, including connectionist approaches, attempt to characterize the mind at a cognitive level of description and not at the neural level, because it is only at the cognitive level that important generalizations can be retained.
Further misunderstandings arise because of the associative character of certain ACT-R properties, such as chunks spreading activation to each other, or chunks and productions carrying quantitative properties relevant to their selection. None of these properties counter the fundamental nature of these entities as symbolic, regardless of their role in unit selection and, ultimately, in computation.
Theory vs. implementation, and Vanilla ACT-R
The importance of distinguishing between the theory itself and its implementation is usually highlighted by ACT-R developers.In fact, much of the implementation does not reflect the theory.
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).
Also, the actual implementation is designed to enable researchers to modify the theory, e.g. by altering the standard parameters, or creating new modules, or partially modifying the behavior of the existing ones.
Finally, while Anderson's laboratory at CMU
Carnegie Mellon University
Carnegie Mellon University is a private research university in Pittsburgh, Pennsylvania, United States....
maintains and releases the official ACT-R code, other alternative implementations of the theory have been made available.
These alternative implementations include jACT-R (written in Java
Java (programming language)
Java is a programming language originally developed by James Gosling at Sun Microsystems and released in 1995 as a core component of Sun Microsystems' Java platform. The language derives much of its syntax from C and C++ but has a simpler object model and fewer low-level facilities...
by Anthony M. Harrison at the University of Pittsburgh
University of Pittsburgh
The University of Pittsburgh, commonly referred to as Pitt, is a state-related research university located in Pittsburgh, Pennsylvania, United States. Founded as Pittsburgh Academy in 1787 on what was then the American frontier, Pitt is one of the oldest continuously chartered institutions of...
) and Python ACT-R (written in Python
Python (programming language)
Python is a general-purpose, high-level programming language whose design philosophy emphasizes code readability. Python claims to "[combine] remarkable power with very clear syntax", and its standard library is large and comprehensive...
by Terrence C. Stewart and Robert L. West at Carleton University
Carleton University
Carleton University is a comprehensive university located in the capital of Canada, Ottawa, Ontario. The enabling legislation is The Carleton University Act, 1952, S.O. 1952. Founded as a small college in 1942, Carleton now offers over 65 programs in a diverse range of disciplines. Carleton has...
, Canada).
Similarly, ACT-RN (now discontinued) was a full-fledged neural implementation of the 1993 version of the theory.
All of these versions were fully functional, and models have been written and run with all of them.
Because of these implementational degrees of freedom, the ACT-R community usually refers to the "official", lisp-based, version of the theory, when
adopted in its original form and left unmodified, as "Vanilla ACT-R".
Applications
Over the years, ACT-R models have been used in more than 700 different scientific publications, and have been cited in many more.Memory, attention, and executive control
The ACT-R declarative memory system has been used to model human memoryMemory
In psychology, memory is an organism's ability to store, retain, and recall information and experiences. Traditional studies of memory began in the fields of philosophy, including techniques of artificially enhancing memory....
since its inception. In the course of years, it has been adopted to successfully model a large number of known effects. They include the fan effect of interference for associated information, primacy and recency effects for list memory, and serial recall.
ACT-R has been used to model attentive and control processes in a number of cognitive paradigms. These include the Stroop task, task switching
Task switching (psychology)
Task switching is an experimental research paradigm frequently used in cognitive and experimental psychology. It is mainly used to investigate executive functions.- Background & History:...
, the psychological refractory period
Psychological refractory period
The term psychological refractory period refers to the period of time during which the response to a second stimulus is significantly slowed because a first stimulus is still being processed. This delay in response time when one is required to divide attention can exhibit a negative effect that is...
, and multi-tasking.
Natural language
A number of researchers have been using ACT-R to model several aspects of natural languageLanguage
Language may refer either to the specifically human capacity for acquiring and using complex systems of communication, or to a specific instance of such a system of complex communication...
understanding and production. They include models of syntactic parsing, language understanding, language acquisition and metaphor comprehension.
Complex tasks
ACT-R has been used to capture how humans solve complex problems like the Tower of Hanoi, or how people solve algebraic equations. It has also been used to model human behavior in driving and flying.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, menu selection and visual search on computer application, and web navigation.
Cognitive neuroscience
More recently, ACT-R has been used to predict patterns of brain activation during imaging experiments. In this field, ACT-R models have been successfully used to predict prefrontal and parietal activity in memory retrieval, anterior cingulate activity for control operations, and practice-related changes in brain activity.Education
ACT-R has been often adopted as the foundation for cognitive tutors. These systems use an internal ACT-R model to mimic the behavior of a student and personalize his/her instructions and curriculum, trying to "guess" the difficulties that students may have and provide focused help.Such "Cognitive Tutors" are being used as a platform for research on learning and cognitive modeling as part of the Pittsburgh Science of Learning Center. Some of the most successful applications, like the Cognitive Tutor for Mathematics, are used in thousands of schools across the United States.
Early years: 1973-1990
ACT-R is the ultimate successor of a series of increasingly precise models of human cognition developed by 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. The HAM model was later expanded into the first version of the ACT theory. 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. The theory was then further extended into the ACT* model of human cognition.
Integration with rational analysis: 1990-1998
In the late eighties, Anderson devoted himself to exploring and outlining a mathematical approach to cognition that he named Rational Analysis. The basic assumption of Rational Analysis is that cognition is optimally adaptive, and precise estimates of cognitive functions mirror statistical properties of the environment. 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"In 1993, Anderson met with Christian Lebiere, a researcher in connectionist models
Connectionism
Connectionism is a set of approaches in the fields of artificial intelligence, cognitive psychology, cognitive science, neuroscience and philosophy of mind, that models mental or behavioral phenomena as the emergent processes of interconnected networks of simple units...
mostly famous for developing with Scott Fahlman
Scott Fahlman
Scott Elliott Fahlman is a computer scientist at Carnegie Mellon University. He is notable for early work on automated planning in a blocks world, on semantic networks, on neural networks , on the Dylan programming language, and on Common Lisp...
the Cascade Correlation
Cascade correlation algorithm
Cascade-Correlation is an architecture and supervised learning algorithm for artificial neural networks developed by Scott Fahlman at Carnegie Mellon in 1990....
learning algorithm. Their joint work culminated in the release of ACT-R 4.0. Thanks to Mike Byrne (now at Rice University
Rice University
William Marsh Rice University, commonly referred to as Rice University or Rice, is a private research university located on a heavily wooded campus in Houston, Texas, United States...
), version 4.0 also included optional perceptual and motor capabilities, mostly inspired from the EPIC
EPIC (cognitive architecture)
EPIC is a cognitive architecture developed by Professors David E. Kieras and David E. Meyer at the University of Michigan ....
architecture, which greatly expanded the possible applications of the theory.
Current developments 1998-present
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 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. 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.
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, a neural network implementation of the theory developed by Christian Lebiere.
Lynne Reder, also at Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University is a private research university in Pittsburgh, Pennsylvania, United States....
, developed in the early nineties SAC, 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.
External links
- Official ACT-R website (with a lot of online material, including the source code, list of publications, and tutorials)
- jACT-R A Java re-writing of ACT-R.
- ACT-R: The Java Simulation & Development Environment, another open-source Java re-implementation of ACT-R.
- PythonACT-R A Python implementation of ACT-R.