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Procedural knowledge (i.e., knowledge-how) is different from descriptive knowledge (i.e., knowledge-that) in that it can be directly applied to a task. [2] [4] For instance, the procedural knowledge one uses to solve problems differs from the declarative knowledge one possesses about problem solving because this knowledge is formed by doing.
Procedural memories are accessed and used without the need for conscious control or attention. Procedural memory is created through procedural learning, or repeating a complex activity over and over again until all of the relevant neural systems work together to automatically produce the activity. Implicit procedural learning is essential for ...
Procedural knowledge is represented in form of productions. The term "production" reflects the actual implementation of ACT-R as a production system , but, in fact, a production is mainly a formal notation to specify the information flow from cortical areas (i.e. the buffers) to the basal ganglia, and back to the cortex.
This knowledge is located and evaluated to determine whether the question can be answered based on what is stored in memory. In this case, the relevant knowledge is not enough to answer the question. Second, when a person has zero knowledge relevant to a question asked, they are able to produce a rapid response of not knowing. [23]
Implicit knowledge usually refers to knowledge acquired unconsciously and intuitively through meaningful exposure to and use of language, resembling the knowledge of a first language. On the other hand, explicit knowledge involves conscious understanding of grammatical rules and structures, primarily acquired through formal education and learning.
Knowledge-Based Decision-Making (KBDM) in management is a decision-making process [2] that uses predetermined criteria to measure and ensure the optimal outcome for a specific topic. KBDM is used to make decisions by establishing a thought process and reasoning behind a decision. [ 3 ]
Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.
However, declarative learning greatly declined in the face of sleep-deprivation. [4] Declarative Learning can be associated with tasks that require a greater amount of attention, such as learning in school. Therefore, the lack of sleep a child obtains can affect declarative learning and can affect how well a child learns during school overall.