By Roger C. Schank
Roger Schank's influential ebook, Dynamic reminiscence (CUP 1982), defined how desktops may perhaps examine according to what used to be recognized approximately how humans study. on the grounds that that book's book, Dr. Schank has became his concentration from synthetic intelligence to human intelligence. Dynamic reminiscence Revisited comprises the idea of studying offered within the unique publication, extending it to supply rules for instructing and studying, and comprises Dr. Schank's vital idea of case-based reasoning and assesses the position of news in human reminiscence. Dynamic reminiscence Revisited is essential examining for all who're enthusiastic about schooling and college reform. It attracts recognition to how potent studying happens and gives guideline for constructing software program that really is helping scholars study.
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Additional resources for Dynamic Memory Revisited
Our method of processing new episodes utilizes memory structures that point to the unusual episodes that are closely related to the new episode. Reminding occurs when the most appropriate structure in memory that will help in processing a new input is unusual in some way, when it is one of the weird experiences we have had and have stored away for just this eventuality. When no particular unusual episode is closely related to an input, we can process the input by using our general knowledge of that type of situation, and no reminding occurs.
Specific memories, standardized as scripts or not, are thus attached to more general memories to which they relate. The economy of such a scheme is very important. Scenes transcend the specifics of a situation, so they capture generalities. Specifics are added by other structures. The construction of what was previously called the dentist script is actually done on demand during processing time, by searching for information from general organizing structures that tell which scenes will be relevant for processing.
The complexity is in noticing that there is an identity. After all, one partner does not seem like another partner in the same way that one toaster is just like that same toaster a day earlier. In this case, indexing is the issue. Recognizing what we are dealing with is part of the prediction process. Thus, indexing depends upon prediction, and prediction depends upon the characterization we assign to the object in question. As long as we see a toaster as a toaster this is easy, and many predictions follow from that recognition.