Experience, sensations and cognition
Learning can be defined as skill acquisition to accomplish a specific task. The idea that we can acquire cognitive skills and make them accessible to new functions through a learning process has been much studied since Gestalt Theory (KOFFKA, 1975), which has determined that the performance of a task would depends on previous experienced performances. We are currently dealing with the assumption of machine learning, and this has led us to revisit some of these principles. In previous work, we have established relationships between sense experiences and the representational aspects of experiences in what we call Conformed Thoughts (LAURENTIZ, 2017, 2018), that will assist us at this time. We will be focused on how such thoughts (namely patterns, codes, sets of codes, and algorithms) end up giving rise to new representational systems, always considering the intrinsic condition of the relationship between experience, sensations and cognition.
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