A Parallel Distributed Processing Model of Story Comprehension and Recall.
An optimal control theory of story comprehension and recall is proposed within the framework of a situation-state space. A point in situation-state space is specified by a collection of propositions, each of which can have the values of either present or absent. A trajectory in situation-state space...
| Publicado en: | Discourse Processes Vol. 16; no. 3; pp. 203 - 238 |
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| Autores principales: | , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Jul-Sep93
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=7569377&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 7569377 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 0163853X 7LQ jtl: Discourse Processes issn: 0163853X maglogo: N pubinfo: dt: Jul-Sep93 vid: 16 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 7569377 10.1080/01638539309544839 ppf: 203 ppct: 35 formats: tig: atl: A Parallel Distributed Processing Model of Story Comprehension and Recall. aug: au: Golden, Richard M. Rumelhart, David E. su: Control theory (Engineering) Distribution (Probability theory) Fiction sug: subj: Control theory (Engineering) Distribution (Probability theory) Fiction ab: An optimal control theory of story comprehension and recall is proposed within the framework of a situation-state space. A point in situation-state space is specified by a collection of propositions, each of which can have the values of either present or absent. A trajectory in situation-state space is a temporally ordered sequence of situations. A reader's knowledge that the occurrence of one situation is likely to cause the occurrence of another situation is represented by a subjective conditional probability distribution. A multistate probabilistic (MSP) causal chain notation is also introduced for conveniently describing the knowledge structures implicitly represented by the subjective conditional probability distribution. A story is represented as a partially specified trajectory in situation-state space, and thus, story comprehension is defined as the problem of inferring the most probable missing features of the partially specified story trajectory. The story-recall process is also viewed as a procedure that solves the problem of estimating the most probable missing features of a partially specified trajectory, but the partially specified trajectory in this latter case is an episodic memory trace of the reader's understanding of the story. A parallel distributed processing (PDP) model whose connection strengths are derived from the MSP causal chain representation is then introduced. The PDP model is shown to solve the problem of estimating the missing features of a partially specified trajectory in situation-state space, and the model's story-recall performance is then qualitatively compared to known performance characteristics of human memory for stories. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 1993 holdings: @attributes: islocal: N |
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