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Connectionist Learning with Adaptive Rule Induction ON-line (CLARION) 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 simulate several tasks in cognitive psychology and social psychology. CLARION has also been used to implement intelligent systems in artificial intelligence applications. It was created by the research group led by Ron Sun.
Overview of CLARION Edit
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.
- The role of the action-centered subsystem is to control actions;
- The role of the non-action-centered subsystem is to maintain general knowledge;
- The role of the motivational subsystem is to provide underlying motivations for perception, action, and cognition;
- The role of the meta-cognitive subsystem is to monitor, direct, and modify the operations of all the other subsystems.
Comparison with other cognitive architectures Edit
- 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 Edit
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, as also been initiated.
Coward, L.A. & Sun, R. (2004). Criteria for an effective theory of consciousness and some preliminary attempts. Consciousness and Cognition, 13, 268-301.
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.
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