CLARION (cognitive architecture)

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.

The third layer consists of the specific implemented models and simulations of the psychological processes or phenomena.

The distinction between implicit and explicit processes is fundamental to the Clarion cognitive architecture.

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.

The dual representational structure allows implicit and explicit processes to communicate and, potentially, to encode content redundantly.

In bottom-up learning, associations among (micro)features in the bottom level are extracted and encoded as explicit rules.

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.

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.

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.

The CLARION framework views that human motivational processes are highly complex and can't be represented through just explicit representation.