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. It is also important to note since there is an implicit layer, that not all declarative knowledge has to be explicit. 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. 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 Meta-cognitive subsystem 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. Learning Learning can be represented with both explicit and implicit knowledge individually while also representing bottom-up and top-down learning. Learning with implicit knowledge is represented through Q-learning, while learning with just explicit knowledge is represented with one-shot learning such as hypothesis testing. Bottom-up learning (Sun et al., 2001) is represented through a neural network propagating up to the explicit layer through the Rule-Extraction-Refinement algorithm (RER), while top-down learning