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This strategy requires the participant to monitor the content of each WM slot. It requires a large number of WM resources, so it results in a higher efficiency. However, the WM slot must track the next item. The researchers recommend a simple one-update strategy. This will save the n resources used by switching to a different WM position, so the WM does not need to track every single item. This is a more complex strategy and requires a lot of effort to maintain.
The naive strategy involves changing the content of a WM slot when a new stimulus is presented. This strategy is not efficient because the user needs to track the next item while removing the other items from the memory. Furthermore, switching is more costly than the one-update strategy because it requires a large amount of WM resources. This method requires the individual to track a large number of items instead of one. It also does not use any additional WM resources.
A 2-update strategy is more efficient than the n-update strategy. The attended slot changes with every presentation and is updated in a loop of length n. This strategy requires the participant to keep track of the next item. During this process, he or she has to focus on the new stimulus and compare it with the oldest one. The task is difficult to complete and the outcome depends on the complexity of the problem. The n-update strategy is the most effective option.
Using the 1-update strategy, the attentional processing of each item is improved. It is not possible to learn to ignore the second stimulus. wm , wm55 , คาสิโนออนไลน์ -update strategy reduces the resources required by the WM. Aside from this, it has the most efficient and limiting effect. While it is naive, it is still worth a try. This research shows that naive participants are able to perform very well even with n=1, n=2, and n=3 paradigms.
The attended strategy is more efficient because it does not require the attention to be maintained when the n-update strategy is used. This technique involves the attention of the WM slot to each item without changing the current item. The single-update method is more complex and takes up WM resources. It is not efficient for spatial tasks. It is not effective for learning n-updates, and only works when the focus of the attention is on a single trial.

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