Storing refers to the process of placing newly acquired information into memory, which is modified in the brain for easier storage.
Encoding this information makes the process of retrieval easier for the brain where it can be recalled and brought into conscious thinking.
Baddeley suggested that information stored in short-term memory continuously deteriorates, which can eventually lead to forgetting in the absence of rehearsal.
[1] George A. Miller suggested that the capacity of the short-term memory storage is about seven items plus or minus two, also known as the magic number 7,[2] but this number has been shown to be subject to numerous variability, including the size, similarity, and other properties of the chunks.
[8] More difficult sequences, such as a phone number, would have to be split into chunks and may have to pass through long-term memory to be recalled.
[9] Chunking was introduced by George A. Miller who suggested that this way of organizing and processing information allows for a more effective retention of material from the environment.
[4] Further research into chunking greatly impacted the studies of memory development, expertise, and immediate recall.
[8] Research into behavioral and imaging studies have also suggested that chunking can be applied to habit learning, motor skills, language processing, and visual perception.
[9] Rehearsal is the process by which information is retained in short-term memory by conscious repetition of the word, phrase or number.
Long-term storage may be similar to learning—the process by which information that may be needed again is stored for recall on demand.
[10] The process of locating this information and bringing it back to working memory is called retrieval.
In addition, the model suggests that to perform the recall process, parallel-search between every single trace that resides within the ever-growing matrix is required, which also raises doubt on whether such computations can be done in a short amount of time.
The multi-trace model had two key limitations: one, notion of the presence of ever-growing matrix in human memory sounds implausible; and two, computational searches for similarity against millions of traces that would be present in memory matrix to calculate similarity sounds far beyond the scope of the human recalling process.
[14][15] Anderson[16] shows that combination of Hebbian learning rule and McCulloch–Pitts dynamical rule allow network to generate a weight matrix that can store associations between different memory patterns – such matrix is the form of memory storage for the neural network model.
Furthermore, the primacy effect, an effect seen in memory recall paradigm, reveals that the first few items in a list have a greater chance of being recalled over others in the STS, while older items have a greater chance of dropping out of STS.
The item that managed to stay in the STS for an extended amount of time would have formed a stronger autoassociation, heteroassociation and context association than others, ultimately leading to greater associative strength and a higher chance of being recalled.
When the study of a given list of memory has been finished, what resides in the short-term store in the end is likely to be the last few items that were introduced last.
The long-term store in SAM represents the episodic memory, which only deals with new associations that were formed during the study of an experimental list; pre-existing associations between items of the list, then, need to be represented on different matrix, the semantic matrix.
The semantic matrix remains as another source of information that is not modified by episodic associations that are formed during the exam.