CLARION (cognitive architecture)

The distinction between implicit and explicit processes is fundamental to the Clarion cognitive architecture.[1] 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.[1][2] 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 among chunk nodes by means of rules and, in the bottom level, information processing involves propagating (micro)feature activations through artificial neural networks. 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.[1][3][4][5] 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.[6]

The Clarion cognitive architecture consists of four subsystems.

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 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.

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.

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. 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

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.