Connectionist Learning with Adaptive Rule Induction On-line (CLARION) is a computational cognitive architecture that has been used to simulate many domains and 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 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 and implicit learning. Clarion captures the implicit-explicit distinction independently from the distinction between procedural memory and declarative memory. 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 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