Editing ACT-R (section)

==What ACT-R looks like==
Like other influential cognitive architectures (including [[Soar (cognitive architecture)|Soar]], [[CLARION (cognitive architecture)|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 [[Common Lisp]], and might be loaded into any of the Common Lisp language distributions.

This means that any researcher may download the ACT-R code from the ACT-R website, load it into a Common 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 [[Blood-oxygen-level dependent|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]].

===Brief outline===
ACT-R's most important assumption is that human knowledge can be divided into two irreducible kinds of representations: ''[[Declarative memory|declarative]]'' and ''[[Procedural memory|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.

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 represent 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 modules' contents.

Procedural knowledge is represented in form of ''productions''.  The term "production" reflects the actual implementation of ACT-R as a [[production system (computer science)|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.

===The symbolic vs. connectionist debate===
In the [[cognitive science]]s, different theories are usually ascribed to either the "[[Cognitivism (psychology)|symbolic]]" or the "[[connectionism|connectionist]]" approach to cognition. ACT-R clearly belongs to the "symbolic" field and is classified as such in standard textbooks and collections.<ref>{{cite book |first=T. A.|last=Polk|author2=C. M. Seifert|year=2002|title=Cognitive Modeling|publisher=MIT Press|location=Cambridge, Massachusetts|isbn=0-262-66116-0}}</ref> 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, 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, 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.<ref>Pylyshyn, Z. W. (1984). ''Computation and Cognition: Toward a Foundation for Cognitive Science''. Cambridge, Massachusetts: MIT Press.  {{ISBN|0-262-66058-X}}.</ref>

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 [[Carnegie Mellon University|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'' <ref>Harrison, A. (2002). jACT-R: Java ACT-R. ''Proceedings of the 8th Annual ACT-R Workshop'' [http://act-r.psy.cmu.edu/workshops/workshop-2002/talks/AnthonyHarrison.pdf PDF] {{webarchive |url=https://web.archive.org/web/20080907005223/http://act-r.psy.cmu.edu/workshops/workshop-2002/talks/AnthonyHarrison.pdf |date=September 7, 2008 }}</ref> (written in [[Java (programming language)|Java]] by Anthony M. Harrison at the [[Naval Research Laboratory]]) and ''Python ACT-R'' (written in [[Python (programming language)|Python]] by Terrence C. Stewart and Robert L. West at [[Carleton University]], Canada).<ref>Stewart, T. C. and West, R. L. (2006) Deconstructing ACT-R. ''Proceedings of the seventh international conference on cognitive modeling'' [https://web.archive.org/web/20180127084017/https://pdfs.semanticscholar.org/1357/6cb9ffe422e11e6042ba76f3dcefd081baf8.pdf PDF]</ref>

Similarly, ACT-RN (now discontinued) was a full-fledged neural implementation of the 1993 version of the theory.<ref name="actrn">Lebiere, C., & Anderson, J. R. (1993). A connectionist Implementation of the ACT-R production system. In ''Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society'' (pp. 635–640). Mahwah, NJ: Lawrence Erlbaum Associates</ref>  
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 (programming language)|Lisp]]-based, version of the theory, when adopted in its original form and left unmodified, as "Vanilla ACT-R".