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While in this example each event appears only once in memory, there is no requirement that this be the case. Each event is stored in terms of all the spec-MOPs that are relevant to it. So if, for example, they existed a spec-MOP for instances of U.S. ambassadors being held hostage in foreign embassies, then the Colombia takeover would also be recorded in terms of the ways that it varied from that spec-MOP.
Understanding as memory modification
The organization of IPP’s memory, as illustrated above, allows the memory modificaiton process itself to be relatively simple. As IPP reads a story, it looks under any relevant S-MOPs for generalizations that share features with the new event and thus might be applicable. It also uses these generalizations in several ways described in Lebowitz (1980) to assist in the understanding process.
Once IPP accumulated enough information from a story to locate the best available spec-MOPs, it is then possible to find events that differ from those spec-MOPs in the same ways as the new story. If the recalled events have enough in common with the new one, then IPP will create a new spec-MOP, indexed under the old one. This generalization repre-
202 Dynamic memory
sents IPP tentative understanding of the world, based upon the stories it has read, subject to the confirmation process described below.
In effect, understanding a story in IPP is finding the spec-MOPs that best describe the events of the story, locating similar events in memory and making appropriate generalizations.
By continually breaking up memory with generalizations, no single node in memory becomes too unwieldy, and yet IPP can still find the information it is after for understanding later events and making still more generalizations.
Notice that the current state of IPP’s memory can have a dramatic impact on the way it understands a story. For example, consider the story below.
UPI, 17 May 80, El Salvador
The Death Squadron, a right-wing terrorist organization, claimed responsibility for six bombings that rocked the capital and four killings elsewhere in violence-plagued El Salvador.
Normally IPP would assume that the six bombings described in this story might well have resulted in casualties, since that is the normal stereotype for terrorist bombings. However, once the generalization that bombings in El Salvador rarely cause injuries had been made (as IPP does make), IPP would come to exactly the opposite conclusion - that probably no one was hurt in this attack. This graphically illustrates the effect of interpreting stories in terms of constantly changing memory structures.
We can imagine a situation becoming stereotypical enough over time that stories about it stop mentioning altogether pieces of information necessary for understanding. This is generally the case, for example, for terrorism in Northern Ireland, where the IRA is rarely mentioned by name in news stories. In such cases, the spec-MOP that has been developed for the situation as necessary for understanding text as IPP’s original S-MOPs.
The model of generalization described above calls for generalizations to be made whenever two events have enough in common to warrant a new spec-MOP. The generalizations made are immediately used to organize episodic memory and to help interpret later stories. However, it would clearly be foolish to give newly created spec-MOPs the same status as existing ones. Instead, each new spec-MOP is considered to be tentative. Then IPP’s confidence in that spec-MOP can either increase or decrease, depending on evidence collected from later stories.
Stated in another fashion, the constant change of memory consists not
Computer experiments IPP Confirmation Process
only of creating generalizations, but of evaluating the generalizations that are hypothesized. Toward this end, IPP includes a procedure for collecting evidence during the understanding process to help confirm or discon-firm tentative generalizations.
In broad terms, the spec-MOP evaluation process is shown in Figure 9.
The criteria for removing a spec-MOP from tentative status are stated loosely in Figure 9. IPP is more concerned with studying the sorts of information that tend to confirm or disconfirm generalizations, and less with how strong the effects are.
The crucial part of IPP’s algorithm for determining confidence in a new spec-MOP is identifying the conditions that supply positive or negative evidence for the validity of a spec-MOP. Basically, new stories that fit existing generalizations provide positive evidence, and those that contradict generalizations provide negative evidence. However, this broad statement can be made more specific within IPP.
The first rule concerning confidence is about positive evidence.
Events for which a generalization (spec-MOP) is relevant and is not contradicted provide positive evidence for that generalization These are the same events that are indexed under the spec-MOP. The more complete the fit between the event and the generalization, the more powerful the evidence