CLARION (cognitive architecture)
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Connectionist Learning with Adaptive Rule Induction On-line (CLARION) is a computational
cognitive architecture A cognitive architecture refers to both a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science. The formalized mod ...
that has been used to simulate many domains and tasks in
cognitive psychology Cognitive psychology is the scientific study of mental processes such as attention, language use, memory, perception, problem solving, creativity, and reasoning. Cognitive psychology originated in the 1960s in a break from behaviorism, which ...
and
social psychology Social psychology is the scientific study of how thoughts, feelings, and behaviors are influenced by the real or imagined presence of other people or by social norms. Social psychologists typically explain human behavior as a result of the r ...
, as well as implementing intelligent systems in
artificial intelligence Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech re ...
applications. An important feature of CLARION is the distinction between
implicit Implicit may refer to: Mathematics * Implicit function * Implicit function theorem * Implicit curve * Implicit surface * Implicit differential equation Other uses * Implicit assumption, in logic * Implicit-association test, in social psychology ...
and
explicit Explicit refers to something that is specific, clear, or detailed. It can also mean: * Explicit knowledge, knowledge that can be readily articulated, codified and transmitted to others * Explicit (text) The explicit (from Latin ''explicitus est'', ...
processes and focusing on capturing the interaction between these two types of processes. The system was created by the research group led by
Ron Sun Ron Sun is a cognitive scientist who made significant contributions to computational psychology and other areas of cognitive science and artificial intelligence. He is currently professor of cognitive sciences at Rensselaer Polytechnic Institute ...
.


Overview

CLARION is an integrative cognitive architecture, it is used to explain and simulate cognitive-psychological phenomena, which could potentially lead to an unified explanation of psychological phenomena. There are three layers to the CLARION theory, the first layer is the core theory of mind. The main theories consists of a number of distinct subsystems, which are the essential structures of CLARION, with a dual representational structure in each subsystem (implicit versus explicit representations; Sun et al., 2005). Its subsystems include the action-centered subsystem, the non-action-centered subsystem, the motivational subsystem, and the meta-cognitive subsystem. The second layer consists of the computational models that implements the basic theory, it is more detailed than the first level theory but is still general. The third layer consists of the specific implemented models and simulations of the psychological processes or phenomena. The models of this layer arise from the basic theory and the general computational models.


Dual Representational Structure

The distinction between implicit and explicit processes is fundamental to the Clarion cognitive architecture. This distinction is primarily motivated by evidence supporting
implicit memory In psychology, implicit memory is one of the two main types of long-term human memory. It is acquired and used unconsciously, and can affect thoughts and behaviours. One of its most common forms is procedural memory, which allows people to perfo ...
and
implicit learning Implicit learning is the learning of complex information in an unintentional manner, without awareness of what has been learned. According to Frensch and Rünger (2003) the general definition of implicit learning is still subject to some controver ...
. Clarion captures the implicit-explicit distinction independently from the distinction between
procedural memory Procedural memory is a type of implicit memory (unconscious, long-term memory) which aids the performance of particular types of tasks without conscious awareness of these previous experiences. Procedural memory guides the processes we perform, ...
and
declarative memory Explicit memory (or declarative memory) is one of the two main types of long-term human memory, the other of which is implicit memory. Explicit memory is the conscious, intentional recollection of factual information, previous experiences, and con ...
. To capture the implicit-explicit distinction, Clarion postulates two parallel and interacting representational systems capturing implicit an explicit knowledge respectively. Explicit knowledge is associated with localist representation and implicit knowledge with distributed representation. Explicit knowledge resides in the top level of the architecture, whereas implicit knowledge resides in the bottom level. In both levels, the basic representational units are
connectionist Connectionism refers to both an approach in the field of cognitive science that hopes to explain mental phenomena using artificial neural networks (ANN) and to a wide range of techniques and algorithms using ANNs in the context of artificial int ...
nodes, and the two levels differ with respect to the type of encoding. In the top level, knowledge is encoded using localist chunk nodes whereas, in the bottom level, knowledge is encoded in a distributed manner through collections of (micro)feature nodes. Knowledge may be encoded redundantly between the two levels and may be processed in parallel within the two levels. In the top level, information processing involves passing activations among chunk nodes by means of rules and, in the bottom level, information processing involves propagating (micro)feature activations through
artificial neural networks Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected unit ...
. Top-down and bottom-up information flows are enabled by links between the two levels. Such links are established by Clarion chunks, each of which consists of a single chunk node, a collection of (micro)feature nodes, and links between the chunk node and the (micro)feature nodes. In this way a single chunk of knowledge may be expressed in both explicit (i.e., localist) and implicit (i.e., distributed) form, though such dual expression is not always required. The dual representational structure allows implicit and explicit processes to communicate and, potentially, to encode content redundantly. As a result, Clarion theory can account for various phenomena, such as speed-up effects in learning, verbalization-related performance gains, performance gains in transfer tasks, and the ability to perform similarity-based reasoning, in terms of synergistic interaction between implicit and explicit processes. These interactions involve both the flow of activations within the architecture (e.g., similarity-based reasoning is supported by spreading activation among chunks through shared (micro)features) as well as bottom-up, top-down and parallel learning processes. In bottom-up learning, associations among (micro)features in the bottom level are extracted and encoded as explicit rules. In top-down learning, rules in the top level guide the development of implicit associations in the bottom level. Additionally, learning may be carried out in parallel, touching both implicit and explicit processes simultaneously. Through these learning processes knowledge may be encoded redundantly or in complementary fashion, as dictated by agent history. Synergy effects arise, in part, from the interaction of these learning processes. Another important mechanism for explaining synergy effects is the combination and relative balance of signals from different levels of the architecture. For instance, in one Clarion-based modeling study, it has been proposed that an anxiety-driven imbalance in the relative contributions of implicit versus explicit processes may be the mechanism responsible for performance degradation under pressure.


Subsystems

The Clarion cognitive architecture consists of four subsystems.


Action-centered subsystem

The role of the action-centered subsystem is to control both external and internal
actions Action may refer to: * Action (narrative), a literary mode * Action fiction, a type of genre fiction * Action game, a genre of video game Film * Action film, a genre of film * ''Action'' (1921 film), a film by John Ford * ''Action'' (1980 fi ...
. The implicit layer is made of neural networks called Action Neural Networks, while the explicit layer is made up of action rules. There can be synergy between the two layers, for example learning a skill can be expedited when the agent has to make explicit rules for the procedure at hand. It has been argued that implicit knowledge alone cannot optimize as well as the combination of both explicit and implicit.


Non-action-centered subsystem

The role of the non-action-centered subsystem is to maintain general knowledge. The implicit layer is made of Associative Neural Networks, while the bottom layer is associative rules. Knowledge is further divided into semantic and episodic, where semantic is generalized knowledge, and episodic is knowledge applicable to more specific situations. It is also important to note since there is an implicit layer, that not all declarative knowledge has to be explicit.


Motivational subsystem

The role of the motivational subsystem is to provide underlying
motivation Motivation is the reason for which humans and other animals initiate, continue, or terminate a behavior at a given time. Motivational states are commonly understood as forces acting within the agent that create a disposition to engage in goal-dire ...
s for perception, action, and cognition. The motivational system in CLARION is made up of drives on the bottom level, and each drive can have varying strengths. There are low level drives, and also high level drives aimed at keeping an agent sustained, purposeful, focused, and adaptive. The explicit layer of the motivational system is composed of goals. explicit goals are used because they are more stable than implicit motivational states. the CLARION framework views that human motivational processes are highly complex and can't be represented through just explicit representation. Examples of some low level drives include: * food * water * reproduction * avoiding unpleasant stimuli (not mutually exclusive of other low level drives, but separate for the possibility of more specific stimuli) Examples of some high level drives include: * Affiliation and belongingness * Recognition and achievement * Dominance and power * Fairness There is also a possibility for derived drives (usually from trying to satisfy primary drives) that can be created by either conditioning, or through external instructions. each drive needed will have a proportional strength, opportunity will also be taken into account


Meta-cognitive subsystem

The role of the meta-cognitive subsystem is to monitor, direct, and modify the operations of all the other subsystems. Actions in the meta-cognitive subsystem include: setting goals for the action-centred subsystem, setting parameters for the action and non-action subsystems, and changing an ongoing process in both the action and non-action subsystems.


Learning

Learning can be represented with both explicit and implicit knowledge individually while also representing bottom-up and top-down learning. Learning with implicit knowledge is represented through Q-learning, while learning with just explicit knowledge is represented with
one-shot learning One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning-based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classif ...
such as hypothesis testing. Bottom-up learning (Sun et al., 2001) is represented through a neural network propagating up to the explicit layer through the Rule-Extraction-Refinement algorithm (RER), while top-down learning can be represented through a variety of ways.


Comparison with other cognitive architectures

To compare with a few other cognitive architectures (Sun, 2016): *
ACT-R 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. Like any cognitive architecture, ACT-R ...
employs a division between procedural and declarative memory that is somewhat similar to CLARION’s distinction between the Action-Centered Subsystem and the Non-Action-Centered Subsystem. However, ACT-R does not have a clear-cut (process-based or representation-based) distinction between implicit and explicit processes, which is a fundamental assumption in the CLARION theory. * Soar does not include a clear representation-based or process-based difference between implicit and explicit cognition, or between procedural and declarative memory; it is based on the ideas of problem spaces, states, and operators. When there is an outstanding goal on the goal stack, different productions propose different operators and operator preferences for accomplishing the goal. * EPIC adopts a production system similar to ACT-R’s. However, it does not include the dichotomy of implicit and explicit processes, which is essential in CLARION.


Theoretical applications

CLARION has been used to account for a variety of psychological data (Sun, 2002, 2016), such as the serial reaction time task, the artificial grammar learning task, the process control task, a categorical inference task, an alphabetical arithmetic task, and the Tower of Hanoi task. The serial reaction time and process control tasks are typical implicit learning tasks (mainly involving implicit reactive routines), while the Tower of Hanoi and alphabetic arithmetic are high-level
cognitive skill acquisition Cognition refers to "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses". It encompasses all aspects of intellectual functions and processes such as: perception, attention, thought, ...
tasks (with a significant presence of explicit processes). In addition, extensive work has been done on a complex minefield navigation task, which involves complex sequential decision-making. Work on organizational decision tasks and other social simulation tasks (e.g., Naveh and Sun, 2006), as well as meta-cognitive tasks, has also been initiated. Other applications of the cognitive architecture include simulation of
creativity Creativity is a phenomenon whereby something new and valuable is formed. The created item may be intangible (such as an idea, a scientific theory, a musical composition, or a joke) or a physical object (such as an invention, a printed literary w ...
(Helie and Sun, 2010) and addressing the computational basis of
consciousness Consciousness, at its simplest, is sentience and awareness of internal and external existence. However, the lack of definitions has led to millennia of analyses, explanations and debates by philosophers, theologians, linguisticians, and scien ...
(or
artificial consciousness Artificial consciousness (AC), also known as machine consciousness (MC) or synthetic consciousness (; ), is a field related to artificial intelligence and cognitive robotics. The aim of the theory of artificial consciousness is to "Define that wh ...
) (Coward and Sun, 2004).


References

Coward, L.A. & Sun, R. (2004). Criteria for an effective theory of consciousness and some preliminary attempts. ''Consciousness and Cognition'', ''13'', 268-301. Helie, H. and Sun, R. (2010).
Incubation, insight, and creative problem solving: A unified theory and a connectionist model.
''Psychological Review'', ''117'', 994-1024. Naveh, I. & Sun, R. (2006). A cognitively based simulation of academic science. ''Computational and Mathematical Organization Theory'', ''12'', 313-337. Sun, R. (2002)
Duality of the Mind: A Bottom-up Approach Toward Cognition
Mahwah, NJ: Lawrence Erlbaum Associates. Sun, R. (2016). Anatomy of the Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture. Oxford University Press, New York. Sun, R. (2003)
A Tutorial on CLARION 5.0
Technical Report, Cognitive Science Department, Rensselaer Polytechnic Institute. Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skills to explicit knowledge: A bottom-up model of skill learning. ''Cognitive Science'', ''25'', 203-244. https://web.archive.org/web/20191218033659/http://www.cogsci.rpi.edu/~rsun/ Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: A dual-process approach. ''Psychological Review'', ''112'', 159-192. https://web.archive.org/web/20191218033659/http://www.cogsci.rpi.edu/~rsun/ Sun, R. & Zhang, X. (2006). Accounting for a variety of reasoning data within a cognitive architecture. ''
Journal of Experimental and Theoretical Artificial Intelligence The ''Journal of Experimental and Theoretical Artificial Intelligence'' is a quarterly peer-reviewed scientific journal published by Taylor and Francis. It covers all aspects of artificial intelligence and was established in 1989. The editor-in-chi ...
'', ''18'', 169-191.


External links


The CLARION project

The CLARION project page at sites.google

The CogArch Lab

The CogArch Lab at sites.google

pyClarion
Cognitive architecture