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Computerized cognition systems are built under a set of basic postulates. They are constructed in such a way that they can accommodate a collection of previous results, which is later used in the simulation of future outcomes. Due to this fact, most systems are developed with an aim of incorporating a lot of information for accurate simulated outcomes. Nevertheless, there are no computational models capable of imitating complex processes and concepts from generic and simple sets of instructions. In addition, it is impossible to include the whole set of outcomes that will most probably result from a given situation, and this renders the quality of projections from the computational models questionable (Goldstone & Landy, 2005). On the other hand, computerized cognition assists in proper understanding of particular situations, which saves some time that would have been wasted on learning about these systems otherwise. As a result, computerized cognition systems are beneficial because they offer more convenience in learning and decision making, even though they have some disadvantages.

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Discovery of computerized cognitive tools has had a positive impact on psychology. Through analysis of previous outcomes, scientists have been in a position to understand human perception easily. This has fostered greater understanding in learning situations. Reading pattern evaluation and other situations have made it easier for psychologists and other analysts to effectively generate probable outcomes of events and have a clear understanding of why situations happen as they do (Goldstone & Landy, 2005). This feature is recognized as the previously analyzed results in the systems. Discoveries in cognitive systems are advantageous since they save time that was initially wasted on perceiving the operational level of the human mind. The systems greatly assist in projecting suggestions of situational human perceptions through information-rich database systems and an influential inference engine that generate probable analysis results from a situation.

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Cognitive learning through this system has also helped in understanding student issues. As a result, facilitators can access previous student performance patterns from the systems and devise the most effective approaches to solve some of the problems faced by the learners. The languages used in the cognitive systems enable students to build productive learning foundations with the help of applications in the system that enable them into understanding their problems with ease. However, cognitive systems have some drawbacks in mental evaluation of actual situations. Their major demerit is that they assume there is a positive perception of given situations before generating an analysis. As a result, some probable outcomes are excluded from the system, which leads to unreliable generated information. In addition, the languages used in these systems seem to be rather complex and often unable to analyze simple situations (Goldstone & Landy, 2005). In addition, sociological and psychological context analysis in the native way is more influential than computer model-based simulations.

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To conclude, in spite of the enormous support for intrinsic aboriginal representational systems, new discoveries that still embrace the same procedures used by the primitive systems have achieved almost similar success. Since most people perceive them as laboratory experiments, they remain to be persuasive systems that promote the learning process and conceptual understanding. The systems enhance the generation process of new domains that were initially problematic in the primitive psychological processes. Clearly, the discovery of the new system and its incorporation into the psychological process, especially learning, have achieved much success. However, it has not neutralized the problems faced in the psychological processes completely. Nevertheless, if proper investigation of these systems is done and more laboratories are stationed to aid in the research process, then the systems will achieve even greater success.

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