CLARION (cognitive architecture)

Connectionist Learning with Adaptive Rule Induction ON-line (CLARION) is a cognitive architecture that has been used to simulate several tasks in cognitive psychology and social psychology, as well as implementing intelligent systems in artificial intelligence applications. An important feature of CLARION is the distinction between implicit and explicit 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.

Overview of CLARION

CLARION is an integrative architecture, consisting of a number of distinct subsystems, with a dual representational structure in each subsystem (implicit versus explicit representations). Its subsystems include the action-centered subsystem, the non-action-centered subsystem, the motivational subsystem, and the meta-cognitive subsystem.

Action Centered Subsystem

The role of the action-centered subsystem is to control both external and internal actions. The implicit layer is made of neural networks called Action Neural Networks, while the explicit layer has 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.

Motivational Subsystem

The role of the motivational subsystem is to provide underlying motivations 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 (Sun 2008). The explicit layer of the motivational system is comprised 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 cant be represented through just explicit representation.

example of some low level drives:

food
water
sleep
avoiding physical danger
reproduction
avoiding unpleasant stimuli (not mutually exclusive of other low level drives, but separate for the possibility of more specific stimuli)

example of some high level drives:

Affiliation and belongingnes
Recognition and achievement
Dominance and power
Autonomy
Deference
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.

Learning

Comparison with other cognitive architectures

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 of CLARION

CLARION has been used to account for a variety of psychological data, 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 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, as well as meta-cognitive tasks, has also been initiated.

Other applications of the cognitive architecture include simulation of creativity and addressing the computational basis of consciousness (or artificial consciousness).

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. (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. 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. 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, 18, 169-191.

External links